diff --git a/examples/notebooks/05_advanced/04_ranking_model.ipynb b/examples/notebooks/05_advanced/04_ranking_model.ipynb
index 9e17ff8..9152b02 100644
--- a/examples/notebooks/05_advanced/04_ranking_model.ipynb
+++ b/examples/notebooks/05_advanced/04_ranking_model.ipynb
@@ -4,59 +4,62 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Learning-to-Rank using CatboostRanker\n",
+    "# Ranking with Uncertainty Quantification\n",
     "\n",
-    "CatBoost Ranker is designed specifically for ranking tasks, where the goal is to order items by relevance rather than predict exact values. It calculates the loss by pairing two samples via [Pairwise metrics](https://catboost.ai/docs/en/concepts/loss-functions-ranking), which considers one the winner whereas the other the loser. Here, we show the example of CatboostRankerMother, customised CatboostRanker wrapper integrated with Mother pipeline. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Imports needed for the tutorial"
+    "In compound selection workflows, the practical question is not just \"which candidate ranks first?\" but **\"can I trust this ranking?\"** When candidates have similar predicted scores, small model perturbations can swap their positions, making top-$k$ selection unreliable.\n",
+    "\n",
+    "`CatboostRankerMother` combines CatBoost's learning-to-rank with virtual-ensemble uncertainty to quantify ranking stability. This notebook demonstrates:\n",
+    "\n",
+    "1. Fitting a ranking model with groupwise structure\n",
+    "2. Getting rank uncertainty via `predict_uncertainty(use_ranks=True)`\n",
+    "3. Getting virtual ensemble scores via `predict_uncertainty(return_raw=True)`\n",
+    "4. Identifying unstable top-$k$ selections with `groupwise_topk_analysis`\n",
+    "5. Validating uncertainty on out-of-fold predictions"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-17T16:08:19.392821Z",
-     "start_time": "2025-09-17T16:08:13.746701Z"
-    }
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "\n",
-    "from sklearn import pipeline as sklearn_pipeline\n",
-    "from sklearn.model_selection import KFold, train_test_split, cross_validate, GroupKFold\n",
     "import matplotlib.pyplot as plt\n",
-    "from catboost import Pool, cv\n",
-    "\n",
-    "import mother.ml as ml\n",
-    "import mother.pipeline_utils as mother_takes_care\n",
-    "from mother import cv as cv_module\n",
-    "from mother import feature_generation as fg\n",
-    "from mother.ml import CatboostRankerMother, avg_ndcg_score\n",
-    "from mother.preprocessing import SmilesToMolTransformer, StandardizerTransformer\n",
-    "import mother.optimization as opt\n",
-    "from sklearn import set_config\n",
     "import numpy as np\n",
-    "from sklearn.metrics import make_scorer\n",
-    "from pathlib import Path"
+    "import pandas as pd\n",
+    "from sklearn.metrics import ndcg_score\n",
+    "from sklearn.model_selection import GroupKFold\n",
+    "\n",
+    "from mother.ml.models.m_catboost import (\n",
+    "    CatboostRankerMother,\n",
+    "    ranker_predict_for_groups,\n",
+    "    ranker_predict_uncertainty_for_groups,\n",
+    ")\n",
+    "from mother.ml.utils import (\n",
+    "    topk_rank_disagreement,\n",
+    "    topk_score_variance,\n",
+    "    groupwise_topk_analysis,\n",
+    ")\n",
+    "\n",
+    "TOP_K = 3  # decision horizon: select the top-k candidates per group"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Loading test dataset and performing initial preparation steps to modify the test dataset for this demo."
+    "## 1. Synthetic Assay-Ranking Data\n",
+    "\n",
+    "We simulate a compound-ranking scenario with 120 assay groups of 8 candidates each, represented by binary molecular fingerprints. Two regimes are mixed:\n",
+    "\n",
+    "- **Separated**: candidates span a broad activity range (clear winner).\n",
+    "- **Near-tie**: candidates share a fingerprint neighbourhood and differ only through weakly informative bits (ambiguous winner).\n",
+    "\n",
+    "This deliberate contrast makes the uncertainty-quality trend visible. On real screening data, validate this relationship with held-out groups before acting on uncertainty."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-09-17T16:10:59.231989Z",
@@ -64,6 +67,14 @@
     }
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset: 960 rows | 120 groups | 8 candidates/group\n",
+      "Regimes: {np.str_('near_tie'): np.int64(54), np.str_('separated'): np.int64(66)}\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -85,216 +96,289 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>smiles</th>\n",
-       "      <th>expt</th>\n",
-       "      <th>groups_to_rank</th>\n",
-       "      <th>rankable_condition</th>\n",
+       "      <th>fp_0</th>\n",
+       "      <th>fp_1</th>\n",
+       "      <th>fp_2</th>\n",
+       "      <th>fp_3</th>\n",
+       "      <th>fp_4</th>\n",
+       "      <th>fp_5</th>\n",
+       "      <th>fp_6</th>\n",
+       "      <th>fp_7</th>\n",
+       "      <th>fp_8</th>\n",
+       "      <th>fp_9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>fp_33</th>\n",
+       "      <th>fp_34</th>\n",
+       "      <th>fp_35</th>\n",
+       "      <th>fp_36</th>\n",
+       "      <th>fp_37</th>\n",
+       "      <th>fp_38</th>\n",
+       "      <th>fp_39</th>\n",
+       "      <th>group</th>\n",
+       "      <th>activity</th>\n",
+       "      <th>regime</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>CN(C)C(=O)c1ccc(cc1)OC</td>\n",
-       "      <td>16.949166</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>A</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.252828</td>\n",
+       "      <td>near_tie</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>CN(C)C(=O)c1ccc(cc1)OC</td>\n",
-       "      <td>16.401859</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>B</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.108936</td>\n",
+       "      <td>near_tie</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>CN(C)C(=O)c1ccc(cc1)OC</td>\n",
-       "      <td>12.613819</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>C</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.223979</td>\n",
+       "      <td>near_tie</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>CS(=O)(=O)Cl</td>\n",
-       "      <td>19.939828</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>B</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.721942</td>\n",
+       "      <td>near_tie</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>CS(=O)(=O)Cl</td>\n",
-       "      <td>19.297908</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>C</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142</th>\n",
-       "      <td>CCCNCCC</td>\n",
-       "      <td>22.724346</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>A</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>143</th>\n",
-       "      <td>CCCNCCC</td>\n",
-       "      <td>20.843441</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>B</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>144</th>\n",
-       "      <td>c1ccc(cc1)N</td>\n",
-       "      <td>20.828490</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>B</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>145</th>\n",
-       "      <td>c1ccc(cc1)N</td>\n",
-       "      <td>20.788605</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>A</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>146</th>\n",
-       "      <td>c1ccc(cc1)N</td>\n",
-       "      <td>18.457830</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>C</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.199517</td>\n",
+       "      <td>near_tie</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>147 rows × 4 columns</p>\n",
+       "<p>5 rows × 43 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                     smiles       expt  groups_to_rank rankable_condition\n",
-       "0    CN(C)C(=O)c1ccc(cc1)OC  16.949166             1.0                  A\n",
-       "1    CN(C)C(=O)c1ccc(cc1)OC  16.401859             1.0                  B\n",
-       "2    CN(C)C(=O)c1ccc(cc1)OC  12.613819             1.0                  C\n",
-       "3              CS(=O)(=O)Cl  19.939828             2.0                  B\n",
-       "4              CS(=O)(=O)Cl  19.297908             2.0                  C\n",
-       "..                      ...        ...             ...                ...\n",
-       "142                 CCCNCCC  22.724346            49.0                  A\n",
-       "143                 CCCNCCC  20.843441            49.0                  B\n",
-       "144             c1ccc(cc1)N  20.828490            50.0                  B\n",
-       "145             c1ccc(cc1)N  20.788605            50.0                  A\n",
-       "146             c1ccc(cc1)N  18.457830            50.0                  C\n",
+       "   fp_0  fp_1  fp_2  fp_3  fp_4  fp_5  fp_6  fp_7  fp_8  fp_9  ...  fp_33  \\\n",
+       "0   1.0   1.0   0.0   0.0   1.0   1.0   0.0   0.0   0.0   1.0  ...    0.0   \n",
+       "1   1.0   1.0   0.0   0.0   1.0   1.0   0.0   0.0   0.0   1.0  ...    0.0   \n",
+       "2   1.0   1.0   0.0   0.0   1.0   1.0   0.0   0.0   0.0   1.0  ...    0.0   \n",
+       "3   1.0   1.0   0.0   0.0   1.0   1.0   0.0   0.0   0.0   1.0  ...    0.0   \n",
+       "4   1.0   1.0   0.0   0.0   1.0   1.0   0.0   0.0   0.0   1.0  ...    0.0   \n",
+       "\n",
+       "   fp_34  fp_35  fp_36  fp_37  fp_38  fp_39  group  activity    regime  \n",
+       "0    0.0    0.0    0.0    1.0    0.0    1.0      0  0.252828  near_tie  \n",
+       "1    0.0    0.0    0.0    1.0    0.0    1.0      0  1.108936  near_tie  \n",
+       "2    0.0    0.0    0.0    1.0    0.0    1.0      0  0.223979  near_tie  \n",
+       "3    0.0    0.0    0.0    1.0    0.0    1.0      0  0.721942  near_tie  \n",
+       "4    0.0    0.0    0.0    1.0    0.0    1.0      0  0.199517  near_tie  \n",
        "\n",
-       "[147 rows x 4 columns]"
+       "[5 rows x 43 columns]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "input_file: Path = Path(\"../freesolv_train.csv\")\n",
-    "data: pd.DataFrame = pd.read_csv(input_file, sep=\",\")\n",
-    "# Removing rows where 'cat_code' is NaN\n",
-    "\n",
-    "# Ensure 'iupac' column exists before dropping it\n",
-    "if \"iupac\" in data.columns:\n",
-    "    data = data.drop(columns=[\"iupac\", \"calc\", \"float_col\", \"cat_col\"])\n",
-    "\n",
-    "data = data.rename(columns={\"int_col\": \"groups_to_rank\"})\n",
-    "\n",
-    "data = data.head(100)  # limit data to 100 rows for testing\n",
-    "data = pd.concat(\n",
-    "    [\n",
-    "        data.assign(rankable_condition=\"A\", expt=data[\"expt\"] + 2),\n",
-    "        data.assign(rankable_condition=\"B\", expt=data[\"expt\"] + 1),\n",
-    "        data.assign(rankable_condition=\"C\", expt=data[\"expt\"]),\n",
-    "    ],\n",
-    "    ignore_index=True,\n",
-    ")\n",
-    "data = data.sort_values(by=\"groups_to_rank\", ascending=True).reset_index(drop=True)\n",
-    "data = data.dropna(subset=[\"groups_to_rank\"])\n",
-    "\n",
-    "data[\"expt\"] = data[\"expt\"] * (1 + np.random.randn(len(data)) * 0.2)\n",
-    "\n",
-    "data[\"expt\"] = data[\"expt\"] - data[\"expt\"].min() + 1\n",
-    "\n",
-    "data"
+    "rng = np.random.default_rng(42)\n",
+    "\n",
+    "n_groups = 120\n",
+    "group_size = 8\n",
+    "n_features = 40\n",
+    "n_informative = 12\n",
+    "\n",
+    "feature_columns = [f\"fp_{i}\" for i in range(n_features)]\n",
+    "true_coefs = np.zeros(n_features)\n",
+    "true_coefs[:n_informative] = rng.normal(0, 1, n_informative)\n",
+    "low_impact_features = np.argsort(np.abs(true_coefs))[: n_features // 2]\n",
+    "\n",
+    "group_difficulty = rng.choice([\"separated\", \"near_tie\"], size=n_groups, p=[0.55, 0.45])\n",
+    "rows = []\n",
+    "for gid, regime in enumerate(group_difficulty):\n",
+    "    if regime == \"separated\":\n",
+    "        gf = rng.integers(0, 2, size=(group_size, n_features)).astype(float)\n",
+    "        noise_scale = 0.20\n",
+    "    else:\n",
+    "        anchor = rng.integers(0, 2, size=n_features).astype(float)\n",
+    "        gf = np.tile(anchor, (group_size, 1))\n",
+    "        for ci in range(1, group_size):\n",
+    "            flipped = rng.choice(low_impact_features, size=rng.integers(1, 4), replace=False)\n",
+    "            gf[ci, flipped] = 1 - gf[ci, flipped]\n",
+    "        noise_scale = 0.30\n",
+    "\n",
+    "    activity = gf @ true_coefs + rng.normal(0, noise_scale, group_size)\n",
+    "    for ci in range(group_size):\n",
+    "        rows.append({**dict(zip(feature_columns, gf[ci])), \"group\": gid, \"activity\": activity[ci], \"regime\": regime})\n",
+    "\n",
+    "data = pd.DataFrame(rows)\n",
+    "data[\"activity\"] = data[\"activity\"] - data[\"activity\"].min() + 0.1\n",
+    "ranking_groups = \"group\"\n",
+    "\n",
+    "print(f\"Dataset: {len(data)} rows | {n_groups} groups | {group_size} candidates/group\")\n",
+    "print(f\"Regimes: {dict(zip(*np.unique(group_difficulty, return_counts=True)))}\")\n",
+    "data.head(5)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Pre-process the smiles data using an sklearn pipeline. The resulting data is a mol formatted pandas dataframe\n",
-    "\n"
+    "## 2. Model Fitting\n",
+    "\n",
+    "`CatboostRankerMother` wraps CatBoost's ranking with automatic `posterior_sampling=True` for uncertainty. The `group_id` argument identifies which rows belong together for ranking within each group.\n",
+    "\n",
+    "We use `bootstrap_type=\"Bayesian\"` with moderate `bagging_temperature` to make virtual-ensemble variation detectable in this synthetic example. CatBoost defaults to YetiRank loss, but other ranking losses (PairLogit, QueryRMSE, etc.) can be specified via `loss_function` parameter."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-17T16:11:00.452353Z",
-     "start_time": "2025-09-17T16:11:00.442825Z"
+     "end_time": "2025-09-17T16:11:03.121953Z",
+     "start_time": "2025-09-17T16:11:03.110315Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitted: 40 features, 120 groups\n"
+     ]
+    }
+   ],
    "source": [
-    "preprocessor: sklearn_pipeline.Pipeline = sklearn_pipeline.Pipeline(\n",
-    "    [\n",
-    "        (\n",
-    "            \"smiles_standardizer\",\n",
-    "            StandardizerTransformer(flags=[\"STANDARDIZE\", \"NEUTRALIZE\", \"DESALT\"]),\n",
-    "        ),\n",
-    "        (\"smiles_to_mol\", SmilesToMolTransformer()),\n",
-    "        # Add other column transformations here if needed\n",
-    "    ],\n",
-    "    memory=None,\n",
-    ").set_output(transform=\"pandas\")\n",
-    "groups_engine = cv_module.TanimotoGroupingFromMols(similarity_threshold=0.1)"
+    "data = data.sort_values(ranking_groups).reset_index(drop=True)\n",
+    "X = data.drop(columns=[\"activity\"])\n",
+    "y = data[\"activity\"]\n",
+    "X_features = X.drop(columns=[ranking_groups, \"regime\"])\n",
+    "\n",
+    "RANKER_KWARGS = {\n",
+    "    \"cat_features\": None,\n",
+    "    \"logging_level\": \"Silent\",\n",
+    "    \"num_trees\": 500,\n",
+    "    \"max_depth\": 3,\n",
+    "    \"l2_leaf_reg\": 5,\n",
+    "    \"learning_rate\": 0.05,\n",
+    "    \"bootstrap_type\": \"Bayesian\",\n",
+    "    \"bagging_temperature\": 2.0,\n",
+    "    \"random_strength\": 2.0,\n",
+    "    \"boosting_type\": \"Plain\",\n",
+    "}\n",
+    "\n",
+    "ranker_model = CatboostRankerMother(**RANKER_KWARGS)\n",
+    "ranker_model.fit(X=X_features, y=y, group_id=X[ranking_groups])\n",
+    "print(f\"Fitted: {X_features.shape[1]} features, {n_groups} groups\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The code in the following cell defines the grouping strategy for cross-validation using Tanimoto similarity. It utilizes the `TanimotoGroupingFromMols` class from the `mother.cv.core` module to group molecules based on their structural similarity. The similarity threshold is set to 0.3. The resulting groups are transformed into a pandas DataFrame, where each molecule is assigned a \"tanimoto-group\" identifier. These groups are then used as the \"groups\" parameter in sklearn's cross-validation methods, such as `GroupKFold`. The code also prints the number of unique Tanimoto groups and converts the group identifiers into categorical codes for further processing."
+    "## 3. Per-Candidate Rank Uncertainty\n",
+    "\n",
+    "`predict_uncertainty(use_ranks=True)` converts virtual-ensemble scores to ranks and reports:\n",
+    "- **`knowledge_uncertainty`** ($\\sigma_\\text{rank}$): std of rank across ensembles. Zero means all ensembles agree on position.\n",
+    "- **`mean_predictions`**: ensemble-average rank (not necessarily integer).\n",
+    "\n",
+    "$\\sigma_\\text{rank}$ directly quantifies positional instability: if the rank-3 compound has $\\sigma_\\text{rank}=2.1$, some ensembles place it at rank 1 and others at rank 5. This is the signal we use for stability detection."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-17T16:11:01.277119Z",
-     "start_time": "2025-09-17T16:11:00.854523Z"
-    }
-   },
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[08:40:40] Initializing Normalizer\n",
-      "[08:40:40] Initializing MetalDisconnector\n",
-      "[08:40:40] Initializing Normalizer\n",
-      "[08:40:40] Initializing Normalizer\n",
-      "[08:40:40] Initializing MetalDisconnector\n",
-      "[08:40:40] Initializing Normalizer\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10 different Tanimoto groups (to be used for cross-validation) found.\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -316,383 +400,582 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>smiles</th>\n",
-       "      <th>expt</th>\n",
-       "      <th>groups_to_rank</th>\n",
-       "      <th>rankable_condition</th>\n",
-       "      <th>tanimoto-group</th>\n",
+       "      <th>pred</th>\n",
+       "      <th>mean_predictions</th>\n",
+       "      <th>knowledge_uncertainty</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>CN(C)C(=O)c1ccc(cc1)OC</td>\n",
-       "      <td>16.949166</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>0.516398</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>CN(C)C(=O)c1ccc(cc1)OC</td>\n",
-       "      <td>16.401859</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>B</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0.674949</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>CN(C)C(=O)c1ccc(cc1)OC</td>\n",
-       "      <td>12.613819</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>0.316228</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>CS(=O)(=O)Cl</td>\n",
-       "      <td>19.939828</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>B</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>CS(=O)(=O)Cl</td>\n",
-       "      <td>19.297908</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>C</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>0.316228</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>142</th>\n",
-       "      <td>CCCNCCC</td>\n",
-       "      <td>22.724346</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>7.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>143</th>\n",
-       "      <td>CCCNCCC</td>\n",
-       "      <td>20.843441</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>B</td>\n",
-       "      <td>7.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>144</th>\n",
-       "      <td>c1ccc(cc1)N</td>\n",
-       "      <td>20.828490</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>B</td>\n",
-       "      <td>5.0</td>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>145</th>\n",
-       "      <td>c1ccc(cc1)N</td>\n",
-       "      <td>20.788605</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>5.0</td>\n",
+       "      <th>6</th>\n",
+       "      <td>5</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.054093</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>146</th>\n",
-       "      <td>c1ccc(cc1)N</td>\n",
-       "      <td>18.457830</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>C</td>\n",
-       "      <td>5.0</td>\n",
+       "      <th>7</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>0.567646</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>147 rows × 5 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                     smiles       expt  groups_to_rank rankable_condition  \\\n",
-       "0    CN(C)C(=O)c1ccc(cc1)OC  16.949166             1.0                  A   \n",
-       "1    CN(C)C(=O)c1ccc(cc1)OC  16.401859             1.0                  B   \n",
-       "2    CN(C)C(=O)c1ccc(cc1)OC  12.613819             1.0                  C   \n",
-       "3              CS(=O)(=O)Cl  19.939828             2.0                  B   \n",
-       "4              CS(=O)(=O)Cl  19.297908             2.0                  C   \n",
-       "..                      ...        ...             ...                ...   \n",
-       "142                 CCCNCCC  22.724346            49.0                  A   \n",
-       "143                 CCCNCCC  20.843441            49.0                  B   \n",
-       "144             c1ccc(cc1)N  20.828490            50.0                  B   \n",
-       "145             c1ccc(cc1)N  20.788605            50.0                  A   \n",
-       "146             c1ccc(cc1)N  18.457830            50.0                  C   \n",
-       "\n",
-       "     tanimoto-group  \n",
-       "0               0.0  \n",
-       "1               0.0  \n",
-       "2               0.0  \n",
-       "3               1.0  \n",
-       "4               1.0  \n",
-       "..              ...  \n",
-       "142             7.0  \n",
-       "143             7.0  \n",
-       "144             5.0  \n",
-       "145             5.0  \n",
-       "146             5.0  \n",
-       "\n",
-       "[147 rows x 5 columns]"
+       "   pred  mean_predictions  knowledge_uncertainty\n",
+       "0     4               4.6               0.516398\n",
+       "1     2               2.3               0.674949\n",
+       "2     6               6.9               0.316228\n",
+       "3     8               8.0               0.000000\n",
+       "4     7               6.1               0.316228\n",
+       "5     1               1.0               0.000000\n",
+       "6     5               4.0               1.054093\n",
+       "7     3               3.1               0.567646"
       ]
      },
-     "execution_count": 12,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "structure_data: pd.Series = data[\"smiles\"]\n",
-    "mol_data: pd.DataFrame = preprocessor.fit_transform(structure_data)\n",
-    "\n",
-    "# cv grouping\n",
-    "\n",
-    "# Apply the preprocessor and Tanimoto grouping to the unique SMILES\n",
-    "mol_data = preprocessor.fit_transform(structure_data)\n",
-    "cv_groups = groups_engine.set_output(transform=\"pandas\").fit_transform(mol_data.iloc[:, 0])\n",
-    "# Ensure no NaN values in the Tanimoto group column\n",
-    "print(f\"{cv_groups['tanimoto-group'].nunique()} different Tanimoto groups (to be used for cross-validation) found.\")\n",
-    "# Merge the Tanimoto groups with the original data\n",
-    "data = data.reset_index(drop=True).join(cv_groups.reset_index(drop=True))\n",
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a feature generator that prepares the previously formatted mol dataframe\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-17T16:11:03.043759Z",
-     "start_time": "2025-09-17T16:11:01.505870Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "feature_generator = sklearn_pipeline.FeatureUnion(\n",
-    "    transformer_list=[\n",
-    "        (\"maccs\", fg.MaccsFingerprints()),\n",
-    "        (\"morgan\", fg.MorganFingerprints()),\n",
-    "        (\"desc\", fg.ChemicalDescriptors()),\n",
-    "    ]\n",
-    ").set_output(transform=\"pandas\")\n",
-    "\n",
-    "\n",
-    "features: pd.DataFrame = feature_generator.fit_transform(mol_data[\"Molecule\"])\n",
-    "# add features to the data\n",
-    "data = data.join(features)\n",
-    "# drop the original smiles column\n",
-    "# data = data.drop(columns=[\"smiles\"])#"
+    "unc_ranks = ranker_predict_uncertainty_for_groups(\n",
+    "    ranker_model, X_features, X[ranking_groups].values, n_ensembles=10, use_ranks=True\n",
+    ")\n",
+    "display(unc_ranks[[\"pred\", \"mean_predictions\", \"knowledge_uncertainty\"]].head(8))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Mother CatBoost Ranker\n",
-    "\n",
-    "The **Mother CatBoost Ranker** is a ranking model built on CatBoost, enhanced by the `mother` library for group-based ranking tasks.\n",
-    "\n",
-    "\n",
-    "#### Workflow:\n",
-    "1. **Data Preparation**: Prepare the following components:\n",
+    "## 3b. Top-k Stability Analysis\n",
     "\n",
-    "   a. **Features (`X`)**: The input features for the model, excluding the ranking group column (`groups_to_rank`). These features include both numerical and categorical data, as well as the generated molecular descriptors and fingerprints.\n",
-    "\n",
-    "   b. **Labels (`y`)**: The target variable (`expt`) that the model is trained to predict.\n",
-    "\n",
-    "   c. **Categorical Features**: The list of categorical features (`categoric_features`), which in this case includes `rankable_condition`.\n",
-    "\n",
-    "   d. **Group IDs**: The ranking group column (`groups_to_rank`) is used as the `group_string` parameter in the `Pool`. This ensures that the ranking model respects the group structure during training and evaluation. \n",
-    "2. **Pipeline Setup**: Use `PipelineWithHyperparameterRooting` to integrate preprocessing, feature generation, and ranking model.\n",
-    "3. **Training & Evaluation**: Train with `fit` and evaluate using metrics like NDCG via cross-validation.\n",
-    "\n",
-    "The Mother CatBoost Ranker is ideal for grouped data tasks like molecular ranking, search relevance, and recommendations.\n"
+    "Get raw ensemble scores with `return_raw=True`, then use utility functions to analyze top-k stability:\n",
+    "- `topk_rank_disagreement`: fraction of ensembles placing each item in top-k\n",
+    "- `topk_score_variance`: score variance for consensus top-k members\n",
+    "- `groupwise_topk_analysis`: combined analysis across all groups"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-17T16:11:03.121953Z",
-     "start_time": "2025-09-17T16:11:03.110315Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ranking_groups = \"groups_to_rank\"\n",
-    "categoric_features = [\"rankable_condition\"]\n",
-    "\n",
-    "data = data.sort_values(by=[ranking_groups]).reset_index(drop=True)\n",
-    "X = data.drop(columns=[\"expt\"])\n",
-    "y = data[\"expt\"]\n",
-    "# Convert group_id to an integral type\n",
-    "X[ranking_groups] = X[ranking_groups].astype(int)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 15,
    "metadata": {},
-   "source": [
-    "#### Defining the Mother CatBoost Ranker Pipeline\n",
-    "\n",
-    "We can use the utility function 'get_ranking_pipeline' to create the pipeline, comprising of a conventional feature selection step followed by the CatBoost ranking model.\n",
-    "A few settings are automatically carried out, by calling this:\n",
-    "\n",
-    "- *Metadata Routing*:  \n",
-    "Sklearn metadata routing must be enabled with `set_config(enable_metadata_routing=True)` before calling `pipeline.fit()` and `tuner.optimize()`. Metadata routing allows for the seamless flow of additional information (e.g., group IDs, sample weights) through the pipeline, ensuring that all components can access the necessary metadata during training and evaluation.  \n",
-    "`get_ranking_pipeline()` enables metadata routing internally while constructing the pipeline but restores the prior config on return, so the call to `set_config` below is required.\n",
-    "- *Scoring Function*:  \n",
-    "The tuner is set up with a custom scoring function `avg_ndcg_scorer`, which is designed to evaluate the ranking performance of the model using the Average Normalized Discounted Cumulative Gain (NDCG) metric. This scorer takes into account the group structure of the data, ensuring that the evaluation is meaningful for ranking tasks. \n",
-    "\n",
-    "Note: The `group_id` parameter needs to be passed to the 'fit' method of the pipeline, as shown later in the code.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-17T16:11:03.185302Z",
-     "start_time": "2025-09-17T16:11:03.161815Z"
-    }
-   },
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "No cv strategy was passed, using the RepeatedKfold (n_repeats=3, n_splits=3, random_state=42)\n",
-      "/workspaces/MotherML/src/mother/optimization/core.py:146: ExperimentalWarning: Argument ``multivariate`` is an experimental feature. The interface can change in the future.\n",
-      "  self.sampler = optuna.samplers.TPESampler(\n",
-      "/workspaces/MotherML/src/mother/optimization/core.py:146: ExperimentalWarning: Argument ``group`` is an experimental feature. The interface can change in the future.\n",
-      "  self.sampler = optuna.samplers.TPESampler(\n",
-      "/workspaces/MotherML/src/mother/optimization/core.py:146: ExperimentalWarning: Argument ``constant_liar`` is an experimental feature. The interface can change in the future.\n",
-      "  self.sampler = optuna.samplers.TPESampler(\n"
+      "Group 0 top-3 stability:\n",
+      "\n"
      ]
-    }
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_predictions</th>\n",
+       "      <th>topk_disagreement_prob</th>\n",
+       "      <th>topk_score_var</th>\n",
+       "      <th>topk_member</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-3.452</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-3.447</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>0.071</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-3.467</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-3.493</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-3.465</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-3.417</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-3.451</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-3.448</td>\n",
+       "      <td>0.32</td>\n",
+       "      <td>0.072</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   mean_predictions  topk_disagreement_prob  topk_score_var  topk_member\n",
+       "0            -3.452                    0.00           0.000        False\n",
+       "1            -3.447                    0.18           0.071         True\n",
+       "2            -3.467                    0.00           0.000        False\n",
+       "3            -3.493                    0.00           0.000        False\n",
+       "4            -3.465                    0.00           0.000        False\n",
+       "5            -3.417                    0.00           0.075         True\n",
+       "6            -3.451                    0.42           0.000        False\n",
+       "7            -3.448                    0.32           0.072         True"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Unstable top-3 candidates (topk_disagreement_prob > 0.3): 30/360\n"
+     ]
+    }
    ],
    "source": [
-    "mother_cat_rank_pipeline, tuner = mother_takes_care.get_ranking_pipeline(\n",
-    "    categorical_features=categoric_features,\n",
-    "    k_scorer=6,\n",
-    "    target_type=\"single_target\",  # only option currently\n",
-    "    model_kwargs={\"logging_level\": \"Silent\"},\n",
+    "# Get raw score ensembles\n",
+    "unc_full, score_ensembles = ranker_model.predict_uncertainty(X_features, n_ensembles=10, return_raw=True)\n",
+    "\n",
+    "# Apply groupwise top-k analysis\n",
+    "topk_analysis = groupwise_topk_analysis(\n",
+    "    uncertainty_df=unc_full,\n",
+    "    score_ensembles=score_ensembles,\n",
+    "    group_ids=X[ranking_groups].values,\n",
+    "    k=TOP_K,\n",
     ")\n",
     "\n",
-    "# Metadata routing must be enabled so that group_id is forwarded to both\n",
-    "# the ranker's fit() and the NDCG scorer's score() during cross-validation.\n",
-    "# get_ranking_pipeline() temporarily enables it while constructing the pipeline\n",
-    "# but restores the prior value on return, so we set it explicitly here.\n",
-    "set_config(enable_metadata_routing=True)"
+    "# Inspect first group\n",
+    "group_0_mask = X[ranking_groups] == 0\n",
+    "print(f\"Group 0 top-{TOP_K} stability:\\n\")\n",
+    "display(\n",
+    "    topk_analysis[group_0_mask][[\"mean_predictions\", \"topk_disagreement_prob\", \"topk_score_var\", \"topk_member\"]].round(\n",
+    "        3\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# Count unstable selections (topk_disagreement_prob > 0.3)\n",
+    "unstable = topk_analysis[topk_analysis[\"topk_member\"] & (topk_analysis[\"topk_disagreement_prob\"] > 0.3)]\n",
+    "print(\n",
+    "    f\"\\nUnstable top-{TOP_K} candidates (topk_disagreement_prob > 0.3): {len(unstable)}/{(topk_analysis['topk_member']).sum()}\"\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can also include another scoring method \"precision\" which we will use later for our analysis."
+    "## 4. Out-of-Fold Evaluation\n",
+    "\n",
+    "Validate uncertainty on held-out data with 5-fold cross-validation:\n",
+    "- Compute NDCG@k and mean top-k rank uncertainty per group\n",
+    "- Check if uncertainty correlates with ranking quality\n",
+    "- Show selective-ranking curve (retaining most confident groups)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-17T16:11:04.445643Z",
-     "start_time": "2025-09-17T16:11:04.433183Z"
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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112ignCLS91ISCj1FTIAAH6UT6tq7oJ59FdR1+C8h5GxZ8A/qlHJLHO/xOhaFHsXyG6cRnf7f3+v6Dq7PfD5EREQVhUnf0bq4uCA4OHvobseOHQ3bPTw8DN+PGjUKPXr0MDx2dnbGuXPncPjwYcTExGD27NkIDAwss5if5mwtg4ejFV746Ryy9P91amQSAbO71oGzjWle4uclY4wlcJ5MLN1PjM61z8fJGs42cqPJEVuZCIvYG4jc/QH0GWpILJWwCxoMZeP+kClVedqrbCwwprkXutRxRYpGC1uFDJ4OlhAEAVnp2W+ktXH3YRP3I3K6dlqUzRtpQRDQpY4LYlMz8f2Ju4hLy4TK2gIvtfZB1zquqGZvWejkGxEAyFSesAsagsSjK/LsswsaApmTaRLKRERFlZyZgTa7v8u1bHxMRgrmXT6I7RFXcaTbRMRnpuN64qN/i0f/N00sTpNW6OtZSeXw/XdqWM6IoLr2rqht5wIrWcl8hKSUW2JOoxcwp9ELOBIVhg57lwEAtoVfxbXEGNRzqFIi1yEiIjJXJk0S1a5dG7Vr135mm9atW+fZJpfL0bVr19IKq1CSNXpsDY2Ck7UcsalZ0IkipIIAlbUcW0Oj0Kueq8lie1YyxphnJZZaeDki0mhyRMSgOtawPPoFtP+OkNBnqJF4dCUAAQ5tx+Q7osjY9DRTv5EWRRH7rkXh7I17eK2xAxRyGTRZWpy9cRfOllKMaeFT6OQbEZD9M69s3A8AkByy8YmE6hAom/Tnzw4RlTuLQo/kShDl0IsiLsVHwvnXj/JM2yoIF0ub7BFB9q65pop52TpAIpRd/al2VWuilas3Tj66BxEiPr98CGvavlRm1yciIjIFzo0pprQsHa7HpEIqAVyVFpAIAvSiiBSNFtdjUpCaabx+TVnJLxmTn2cllowlRwb7uaB93E5o4+7nOVdyyEbY+hWuGK+p30iHJ6Zjw19XkfjoAe7czb1vfaoGXepWhZeTNUY380SwlyOSNVrYKWSoV8W2QhZOFUUR2riI504jpIKRKVVwaDsGtn5doMtIgdTSFjInvqZEVD4tv3HaaJHnHM9KEEkEAT62TrlGBNWzrwJfexeoCjFFrDQJgoAPAzqj+/7sD67W37mIjxp1RQ0jo6SJiIgqCiaJislSKoGtQorHKZnI0OohABABaHUiXGwtjNbwMXf5JZaMJZBcNQ8QdXCj0fMUtRivKd9Iq9XJSIyNgiBTQGvpCL0ggUTUQ5aRgMTYKCSnJCNOIcPW0ChDsqy+qw0GBlRDVTtLqKzl8HSwqhBv+rXqOKgvbH0qWZf/NEIqGEEQIFd5sUg1EZVroigiJiPlue0sJbLsFcSeKh5d284ZliU0Raw0veDuiyYqD5yPewCdqMfCfw5jWcuBpg6LiIio1DBJVEyWMgmGBFbDtyfuIUv33ydmAoChgdVgVQ6TRM/ydAIpK1ZRKsV4TfVG2lrQwtbBGQ8yLBCrzoROnwWpRAKVjSs8LTNhLxOxNTTKMO2uc21nZGTpMGHTJVjIpaipssbggGro7+8GlY1FGUdfckRRhPrC1lzT/goyjZCIiCoHQRBQxdL2mYkiF0sbRA/9qEyniJU0QRDwQUAnvHhoDQDg51tnMSugC9xt7E0cGRERUekov3dtM6EVRfi7KfF6K2+42lrAQirA1dYCr7fyRoOqSmiLMBe/PMmpIWRMeSzG66GUoXujOohRZ0Cn1wMAdHo9Hqkz0KNRHWglcmz8OxIAUN3BEhlZOiw7dR+JGVrEpWYiNjULK89EYGtoFMRy/H+vjYtAcojxEWLJIRuhjc9buJuIiCqXCb7BkOTzgYFUEPBa3ZblOkGUo69XAzT4t2B1pl6HRaFHTBsQERFRKSr/d24Ts5BKYCGRINjTHt/088PaYY3wTT8/BHnaw0IqwEJasV/inBpCDu3GQWKpBJA9gsih3bhyWYw3SrRDVlYWJrb0hoNV9jgmBys5Jrb0hiYzE7GZUsMKb029HPHbxYeGY3V6Efp/V7jb+HckIhIzyv4JlBCdJsXo6DDgv2mEzyOKIrJiw5Hx8CqyYsPLddKMiIjymu7fHv6ObpA+da+XCgL8HN0wza+diSIrWRJBgpkNOxke/3jjNB6lG79HEhERlXecblZMDjIdrCykOBueiF3XYgwFnXvUq4JmXg5wlJm2cHVZqEjFeFM1Ouy+lQgPSy3eDHYzrG52LiIBDzLk6NNYAqUiO1FkIRWQmKE1HCuVCJBIsp+zWqNDikab32XMnlRhW6xphKxnRERU8Snlljje43UsCj2K5TdOIzpdjapWSkzwDcY0v3ZQyi1NHWKJGewTgNkX/8RtdRzSdVlYfPU4PmvSw9RhERERlTgmiYopSaPH6fuJWHzsDkQAEEVAEHD90R2807YGGrpYws3UQZYBcy3GW9jVuWwVUthZW+KhRosHN9UQ9ToIEikEmSXsrGVwtbHAkMBqWBESgUydCAdLmSFRpLKxgEKWfW6lQgpbhQyiKCIiMR0pGh1sFdJyU9Q6ZxrhkzWJcjxvGiHrGRERVR5KuSXmNHoBcxq9AFEUK+zfd5lEivcbdsS4k/8DAHx77SSm+7WHo8LaxJERERGVrIo9F6oMJGuB1WcjIOLfqTT/do5EiFh9NgLJ2orZWSoPtOo4JB5bhchV4xC1ciwiV41D4rGV0Krj8j3G08EKQwKrARAgyBSQWFhDkCkACBgSWA3V7C3Rz88N44I8cS06GUMbuUMqEeCqVMDJSo7skuXAkMBqUFpIsepMOMZtuoSxmy5h3KZLWBkSjrjUzDJ5/sVRnGmErGdERFQ5VdQEUY6RNZvA08YBAKDO0uDbaydNGxAREVEp4EiiYsrSiUjO0OLfYUS59iVnaJGl05skrsouZzRLSuh+2Ph1hSCzgKjNREroATxrNIsgCOjnlz32K2eJe6VCiiGB1dDfzw2CIEBlY4Exzb3QpY4r1BlZqKGywZbQqFxt+/lVxR9PrIIGZE9BW3kmAoIAjGnuZfad6aJOIyxIPSNzG3FGRFRSUlJSEBERAXd3d9jZ2RXomKSkJERFRaF69eqwsrIqcpuiXJsKzkIqw7t+7fFmyFYAwOKrxzG5QVvYyhWmDYyIiKgEcSRRMdnIBdRytsoeQJLz3vnf72s5W8FWzpfYFLRxEdAmRsFC5Ym4fUvw6PcPELdvCeROHshKjHzmaJacJNCKwYFYOTgAKwYHYkxzLzg9saS9IAjwcrRCAzc7TGxZPU/b1EydYRW0p5W7otbiU/8+R049I2MKUs+IiKi8+vTTT+Hi4oJu3brBxcUF77333jPbR0dHo0ePHnB3d0fv3r1RpUoVfP3114VuU5RrU9GMrROEKlbZ97h4TRqWXT9l4oiIiIhKFjMYxVTVUsDo5p6oYquAVJAAggCpIEEVW0X2duMf9lEp02k10KcnIf7QMujTEgEA+rREJBz+EWJ6MnRZmmcen5MEql9VCS/HZ9cRMtY2RaMzrIL2tPJS1Loo0/WA/+oZGfO8ekZEROXV1q1b8cknn+DPP//E/fv3cfLkSXzzzTdYs2ZNvseMGzcOsbGxePDgAW7duoXjx49j1qxZ2L17d6HaFOXaVDRWMjmmNmhreLzoylGka7NMGBEREVHJYpKomKqoHBDkYY9Jrb3h62INd3sFfF2sMam1N4I87FDFycHUIZYJs1vuXJuJpDObjO5KOrMJ0BWvLtDznq+tQgqlQmr02Jyi1ubsyeLTOVPHcopPqy9sfeb/b3HqGRERlVfLly9H165d0bZtdgKhadOm6Nu3L5YvX260vV6vx4EDBzBu3Dg4ODgAAAICAtC1a1csW7aswG2Kcm0qnol1W8DRIvtTwJh0NVbeDDFxRERERCXHvN+plgOCICAlU4cq1lJ81LU2sueaiUhK0yA1s+Ku8vEks1zuXAAEifEf7/y2F1RBnm9OAewnaxLlGBJYDZ4O5r0s8POKT9v6dYFc5ZXv8UWtZ0REVF6dP38ekyZNyrWtRYsW2LZtm9FVvyQSCWxtbfHo0aNc2x89eoTbt28XuE1Rrk3Fo5Rb4p0GbfDRxX0AgIWhRzDBNxgWUnariYio/OPdrJgiEtOx9OR9OFnJ0MTDDhYSCTL1epx/kIz49Mfwd3eAl2PFnXNmrsudSxVKyFXVAQC6lDjDUvZSWxXkztUhzadmzvMU9PkWpAC2OSuJ4tOCIECu8mKRaiKqFOLi4qBS5f5gRKVSIT09HWlpabCxsclzzMSJE7Fo0SK4ubmhXr162LZtGy5duoSMjIxCtSnKtTUaDTSa/6ZeJycnA8gevaTXc9GN53nDtyUWhR6FOkuDiNRErAk7h7G1m5s6LCIiIgAo1r2cSaJiyqk9o9bocD/xsZH95l97pjiKO+KktMhUnrBvORyJR1dAam0PUa+HIJFAkClg32J4keviFOb5PrkKWopGC1uFDJ4OlmafIAL+Kz5tLFHE4tNERHnJ5fJcSRcAhsdyufF0+dy5c+Hj44MtW7bgp59+Qrt27fDee+9hwYIFhWpTlGvPnz8fc+bMybM9ISEBWm3F7ruUlNHVG2Fp2GkAwPxLB9HbsQZkElZyICIi01OrjX/gXxBMEhVTTu0ZY0WKy0PtmeIy1+XOc+riAHhqWtiQYtXFKezzzSlqXd7kFJ9+csRUDhafJiLKy9PTE1FRUbm2RUZGwtXVFRYWFkaPkUgkGDt2LMaOHWvYNmzYMNSrV69QbYpy7ffffx9TpkwxPE5OToanpyccHR1hZ2dXgGdM7zfuip/unke6Lgt3UxNwIDkCw2o0MnVYREREkMmKnoeo2BmMMlDea88YI4oiIhLTkaLRwVYhhadD/qt7mfOIk9Koi2POz7cklVaSjYioourYsSP27NmDBQsWGP5G7ty5E506dTK0yVmlLDAwEACQmZmZK4kTHR2N7du347PPPjNsK0ibglz7aQqFAgqFIs92iUQCCUfDFEhVGztM8A3GkqvHAQCf/3MIw2o2gkTg60dERKZVnHu5IJp8Gaqyl5ycDHt7eyQlJZXIp2VxqZnYGhpltPaMk43xT/DMlbHnMjigGvr7u0Fl5LmIoojEY6uMjjhxaDfOZDWJSktlfL7a+AgWnyYis1bS9/WiuHPnDho3boxBgwZh2LBh2LJlC1asWIEzZ87Az88PAPDtt9/izTffRFZWFmQyGdavX4/Tp09j4MCBiI+Px+zZs+Hq6oo///zT8AlgQdoU5NrPYw6vYXn0IDURNX+fj0x99ojyPzq+gv7V/U0cFRERVXbFua9zJFEJKM+1Z54kiiK2hkblGhWl1uiw8kwEBAEY09wrz3OqbCNOKuPzZfFpIqLnq1GjBk6cOIHPPvsMM2bMQPXq1XHs2LFcSRoXFxcEBAQY7hXDhg1DWloa5s6dC0EQMHbsWLz22mu5hogXpE1Brk2lw8PGAaNqNcPym9m1ieZdOoh+Xn4Vrj9ARESVB0cS8dMyg/CENIzbdCnf+korBgfmW1+nso040ev1yIy8Bn1GMiSWdrCoVo/D84mITIT39eLja1h0d9RxqLN5AXRi9koye7qMQzePuiaOioiIKjOOJDIDoihCGxcBnSYFUoUtZKrylyTJWanNGLVG98yV2irTiBOtOg7qC1ufGkk0GMrG/SFTqp5/AiIiIqowaihVGFajEdbePg8A+PTSAbzg7lvu+oFEREQAk0QloqIkDSriSm0lnbwTRRHqC1tz1STSZ6iReHQlAKFINYkqQoKRiIioMnu/YUesu30BIkScfHQPx2LuoF3VmqYOi4iIqNDK37t+M1MaSQNTqWgrtZVG8k4bF4HkkI1G9yWHbIStXxfIVV4mjZGIiIjKVj2HKhjg7Y/f710GkF2biEkiIiIqj1hEpZielzTQxudNuJgrQRDQz88N44I8oVRIAWSPIBoX5In+fm7lJtkF5E7e5SxXn5O8U1/YiqKW4tJpUgzne5o+Qw1dRorJYyQiIqKy90HDTobv90fexJnH4SaMhoiIqGg4kqiYCpI0KE91eirKSm05yTuZqjqsfJpCkFlA1GYi/e65Io34ySFV2EJiqTT6fy6xVEJqaVvoGI0pToxERERU9gJV7ujpUQ+7HlwDkD2aaFvn0SaOioiIqHCYJCqmkkwamAtBEPJdxay80GlSYFkzCMjKQNy+JdCnJUJi7QD7oCEQqtQqcvJOpvKEXdCQXNMLc9gFDYHMybNQMVakBCMVDWtSERFVHB8EdDIkibZHXMHl+Eg0dKpm4qiIiIgKjtPNiiknaWBMYZMGVHIkVvaANhPxh5ZBn5YIANCnJSLh8I+ALgsSa/sinVcQBCgb94NDu3GQWCqzr2WphEO7cVA26V+oN/c5CUaj8ZfTBCMVjlYdh8RjqxC5ahyiVo5F5KpxSDy2Elp1nKlDIyKiImjh6o2ObrUMjz+7fMiE0RARERUek0TFVJJJAypBei1SQvcZ3ZUSug/Q5V3BraBkShUc2o5BtbEr4DZ2JaqNXQGHtmMgs3Uq3HmYYKzUWJOKiKhi+jCgs+H7TXcv4UbSIxNGQ0REVDicblYCcpIGtn5doMtIgdTSFjInThkxJb0mFYJEBpmdK3QpcRD1OggSKaS2KghSGfSagheYNkYQBMhVXsWaDpaTYATw1OpmQ5hgrARYk4qIqGJqX7UmWrhUx6nH9yFCxOeXD+HnNkNNHRYREVGBMElUksSn/iWTkSpsIbVxhCCVQWptD1GvhyCRQJApzGoqV0knGMtDfZvyEGNZYE0qIqKKSRAEfBjQGT0PrAQArL19AR8FdoW3snAjjomIiEyBSaISoFXHQX1h61OjQQZD2bg/ZEqVqcOrlJ4sMC3IFHgyBWFuU7lKYlQSUD5+DstDjGWlIha9JyKibN096qKRkzsuxj+ETtRjwT+H8UPLAaYOi4iI6LlYk6iYWFfEPJV2rShRFJEVG46Mh1eRFRtu8v/n8vBzWB5iLEusSUVEVHEJgoAPAjoZHq+6dQaRaUkmjIiIiKhgOJKomHLqishU1WHl0xSCzAKiNhPpd8+xroiJlVatKHMcDVMe6tuUhxjLEmtSERFVbP2r+6GevSuuJT1Cpl6HRaFH8VXzPqYOi4iI6JmYJComnSYFljWDgKwMxO1bAn1aIiTWDrAPGgKhSi3WFTGxkprKlePJ0TA5ckbDAAIc2o4xyZv78lDfpjzEWNZY9J6IqOKSCBLMDOiEkcd+BQD8eOMU3m/YES6cTkxERGaM082KSWJlD2gzEX9oGfRpiQAAfVoiEg7/COiyILG2N22AlUxpTwN73mgYbXxEiV6voHLq2xhjLvVtykOMppCTyLR0rw+5yuu5CSJzm+pIRET5G+oTiBr/jjJO02Zh8ZXjJo6IiIjo2TiSqLj0WqSE7oO8qi9SAoYhw8IRlpkJsL20ASmh++DY6Q1TR1hplMU0MHMdDfNkoe6nmUt9m/IQo7kzx6mORESUP5lEihn+HTDhr98BAN9eO4npfu3hoLAycWRERETGcSRRMek1qdAGDMafDefgzXMKTDyYgDfPKbC34cfQBgyGXpNi6hArhbIqimyuo2FKu1B3SSgPMZozFv4mIiqfXq7VFB7/jixPzsrAt9dOmjgiIiKi/HEkUTFJrOxxUN4MK0/eAkQBgkyBVK2AVWciIG3VDGM53axMlFVRZHMeDVMe6tuUhxjNFQt/ExGVTwqpDNP92+PtkG0AgMVXj+GdBm1gK1eYODIiIqK8OJKomKIER/x+Kz37gSBAkEiAf9/w/u9WOqJFRxNGV3kUZBpYSTD30TCFrW9TGCVVC6c0Y6zIyupnnIiISt64OkFw/Xe0cZwmDT/eOGXiiIiIiIzjSKJiStXokCa1hdSuCnQpsYBeB0ikkNo6I02qREqmztQhVgo508CMvYku6WlglXE0DGvhmF5Z/owTEVHJspZZYEqDtphxfjcAYFHoUbxRtxUsZZVtXU8iIjJ3HElUTLYKKeysLSGzc4GFa03IXWvCwrUmZHYusLNWwFbBPFxZyJkGZkxpTAOrTKNhWAvHPJT1zzgREZWs1+q2hKNFdsHq6HQ1Vt06Y+KIiIiI8mKSqJg8HawwJLAagOx6RBILawgyBQABQwKrwdPB0tQhVgolPQ2My4z/53m1cLTxEWUcUeVk7lMdiYjo2ewsLPFW/daGxwv+OYwsPUecExGReeEwl2ISBAH9/NwAABv/joRao4NSIcWQwGro7+fGN25lqKSmgWnVcUi5dhi6xEjDNqmDG2zrdayUU6sKUguHg+XLRmWc6khEVJG8Vb8Nvgw9hhStBuGpiVh3+zxG125u6rCIiIgMmCQqASobC4xp7oUudVyRotHCViGDp4Ml37iZQM40sKImLURRRPrt09DcCUHi6d+gT0uExNoB9kFDILWwhm1AD7P+fxVFEdq4COg0KZAqbCFTFT+BwFo45qW4P+NkeqXxe0pE5YOTwhqv122BhaFHAADzLx/CyzWbQirh4H4iIjIPTBKVEEEQ4OVoZeowqJiyEiKRduMY4g8tM2zTpyUi4fCPECRSKKoHwsLR3YQR5q+0ikvn1MJJPLoizz7WwiEqHBaBJ6Ipfu2w9NoJZOi0uJUci//du4ShNRqZOiwiIiIArElElItO/QiJpzYY3Zd4agN0yY/LOKKCKc3i0qyFQ1QyWASeiACgipUS4+sEGx7Pu3QQelFvwoiIiIj+w5FERE/SaSHmU39HzFAD+qzs781susjzikvb+nWBXOVV5POzFg5R8ZX27ykRlR/T/dpj2Y1TyNLrEJoYjR3hV9G3up+pwyIiImKSiOhJUjtXyF18kBkTlmef3MUHUmUVs5wuUhbFpVkLh6h4WASeiHJ42jrglVpNseJmCADg08sH0MerAT98ISIik+N0M6InyFVecOzwKmR2rhAkUgCAIJFCZucKx46vQebkYZbTRXKKSxvD4tJE5oG/p0T0pBn+HSD5Nyl0LvYB9kfeNHFERERETBKVGFEUkRUbjoyHV5EVG87aEuWUIAiwazYIzr1nwtIrABauNWHpFQDn3jNh12wgdPEPnjldRBsfUcYRZ8spLm0Mi0sTmQf+nhLRk2raOeMln/8KVn966YAJoyEiIsrG6WYlwBynH5WVotbmMbeaPk96Vv2djKRos5wuklNcGsBTP4dDWFyayEzw95SInjYzoCPW37kAADgecxfHom+jbdWaJo6KiIgqMyaJiunJ1Wpy5Ew/AgQ4tB1TYTv+RU2OlYekWn71d3KmixhLFJl6ugiLSxOZP/6eEtGT6jtUxYvV/fHH/X8AZK90xiQRERGZEqebFVPOajUyVXUomw6AXfBLUDYdAJmqukmnH5W2oi7lXN6XgDb36SI5yS1L9/qQq7z4xpPIDPH3lIie9EHDTobv90XexNnH4SaMhoiIKjsmiYpJp0mBZc0gWKg8EbdvCR79/gHi9i2B3MkDljWaQ5eRYuoQS8XzlnLOLzlW1OPMRc50EYd24wwFaCWWSji0G8fpIkRERFRojZ090MOjruHxvMsHTRgNERFVdpxuVkwSK3tAm4n4Q8sM2/RpiUg4/COcOr0OibW9CaMrPUVdyrkiLAHN6SJERERUkj5o2Bm7H1wHAGwLv4J/4qPg7+Rm4qiIiKgy4kii4tJrkRK6z+iulNB9gE5XxgGVjaIu5VxRloDmdBEiIiIqKS2reKPDE7WIPuNoIiIiMhEmiYpJr0mFIJFB5uQBuaM75I7VIHd0h8zJA4JUBr2mYk43K2ptHnOv6UNERERkCh8EdDZ8v+neJdxMemzCaIiIqLJikqiYpApbCApriJo0ZD6+i8yYMGQ+vgtRkwrBwrrcjIwprKLW5jHHmj6iKCIrNhwZD68iKzbc7ItnExERUcXT0a0Wgl2qAwD0oojP/zlk4oiIiKgyYk2iYpI4usPWrwse//HRfxv1WmgTHsKxw6uQOLibLrhSVtTaPOZU00erjoP6wlYkh2yEPkMNiaUSdkGDoWzcHzKlqszjISIiospJEAR8ENAJvQ+sAgCsDTuPjwK7oLqtk4kjIyKiyoRJomLKiroObdIjOHZ8DUmnf4U+LRESawfYB78EbVI0sqKvQ+rewNRhlpqc2jyFLTZd1ONKkiiKUF/YisSjKwzb9BlqJB5dCUCAQ9sxrDVEREQFdv/+fXz11VcICwuDt7c33n77bdSpU+eZxxw8eBC//fYboqKiUK9ePUyZMgVubrkLFh8+fBgbNmxAdHQ0XF1dMXDgQHTv3t2wf+fOnVi8eHGec+/btw8SCQeNlyc9PeohwKkaLsVHQivqsfCfI/iuxYumDouIiCoR9hyKSZ+RjMTjPyP11ik4tB8P534fwaH9eKTeOoXE46uhT082dYiUD21cBJJDNhrdlxyyEdr4iDKOqOLhVD4iqiwiIyPRvHlzPHjwAKNGjUJiYiKCgoIQFhaW7zHff/89evfujRo1amD8+PGIjY1Fs2bN8OjRI0ObjRs3okuXLnB3d8err76KmjVrom/fvli+fLmhzYMHD3Dt2jXMmDEj1xc/6Ch/BEHABw07GR6vvHUGUWnsSxIRUdnhSKJikljaQWrrhKyYm0iIuZlrn9TWCRIrOxNFRs+j06RAn6E2uk+foYYuI8WkI53KO07lI6LKZOHChbC3t8emTZsglUoxcOBANG7cGPPmzcPPP/9s9JhPPvkEU6ZMwfvvvw8A6Nu3L5o2bYovvvgCX3zxBQBg06ZNeOGFF/Dxxx8DAHr16oUrV65g48aNmDBhguFcVlZW6Ny5c55rUPnzYnV/1LV3xfWkR9DotPgy9CgWNe9t6rCIiKiSMPlIoqysLOzfvx/r1q3DpUuXCnRMfHw8du/ejQ0bNjzzE7qyYFGtHhw7vm50n2PH12HhVq+MI6KCkipsDcWznyaxVFbYouNl4cmpfDmJuJypfOoLWzmiiIgqnH379qFXr16QSqUAskeE9O3bF/v27TPaXqfTITY2Ft7e3rm2e3t7Y8+ePYbH/v7+uHXrFtLS0gAAGo0GV69eRUBAQK7jYmNjMWDAAAwePBiff/45UlIq5uqqlYFUIsH7DTsaHi+7cQqxGakmjIiIiCoTk44kiouLQ6dOnZCSkgJ/f3+88cYbGDlyJL799tt8j9myZQtGjx6NRo0awdnZGa+//jqmT5+ODz74oAwj/49EIoF98EsAgIRD30OXEg+prRMcO74O+xbDWAvAjMlUnrALGpKrJlEOu6AhEAUpMh5ehVRhC5nKNIW1y6vnTeWz9esCucqrjKMiIio99+7dg4eHR65tHh4eiIyMRFZWFuTy3GNTpVIpgoODsWHDBowcORIKhQL37t3DoUOHoNFoDO0+/PBDaDQa+Pj4oE6dOrh16xaGDBmCzz//3NBGEAT06NEDffr0QUZGBpYsWYLly5fjwoULcHBwMBqvRqPJdZ3k5OwpTXq9Hnq9vrgvBxXTEO8AfHxxH+6mxCNVm4nFV45hbqMXTB0WERGVE8W5l5s0SfT+++8jKysLly5dgo2NDc6ePYugoCD07NkzV0HGHOnp6XjllVcwZcoUw7Dr0NBQNG7cGF27dkWzZs3K+Blks3D2gqrHdCgb9YI+PRkSKztYuNVjgsjMCYIAZeN+AJBrSpSy2QDI7KsiasVoTpMqIk7lI6LKJjMzE1ZWVrm2WVtbG/Y9nSQCgJ9++gkDBw6Et7c3fHx8EB4ejk6dOmHbtm2GNocPH8ayZcvwxhtvoHnz5rh06RK++uordOzYEX379gUAjBgxAq+++qrhmBdffBF16tTBokWL8OmnnxqNd/78+ZgzZ06e7QkJCdBqtYV/AajEveHTDNP++RMA8M3VExhTzR92cksTR0VEROWBWm38vVhBmCxJpNfrsXHjRsyePRs2NjYAgGbNmiE4OBi//vqr0STRzZs3oVar0adPH8M2Pz8/VK9eHevWrTNZkgjIHlFkWYFXMauoZEoVHNqOga1fF+gyUiBR2CLtzhnEbv+vU80VzwovZyqfsUQRp/IRUUXk6OiI+Pj4XNvi4uKgUCgMyaKn1a1bF5cuXcKtW7cQGxuLhg0b4pNPPkHVqlUNbSZPnowhQ4YYkj19+vRBSkoK3nzzTUOSKKcflcPOzg6tWrXChQsX8o33/fffx5QpUwyPk5OT4enpCUdHR9jZsZ6iOXjdvh2+CjuFyPRkJGs1+DXmeq5paERERPmRyYqe6jFZkigiIgLJycmoX79+ru0NGjTA+fPnjR7j4eEBQRBw+fJlNG7cGEB2faLIyEiEhobmey0OqabnkTp6QAogKy4CiUd+goi8iaCkkE2wbtAZcifPsg+wnJE4ukPZfAgSj63Ms0/ZfAgkDu783SOiEmMOf08CAwPzJGUuXLiAhg0bPvPDBalUirp16xoe79mzB506/be61aNHj4zWLXr06BFEUcz33LGxsVCp8h/9qlAooFAo8myXSCQcCW0mrCQWmO7fHpPPbAcAfH31GN5p0AY28rz/b0RERE8qzr3cZEminESNo6Njru1OTk6GfU9TqVSYOnUq3nrrLdy8eRPOzs5Yu3YtXF1dkZSUlO+1OKSaCioz9jHUUgfAxsHofovHj2EBG6P7KDddjfYQtHKkXjsEMTMdgoUVbOp1RFbN1khISDB1eERUgRRnSHVJGTVqFMaPH4+///4bgYGBuHHjBjZv3pyrdtC2bdvwzTffYN++fZBIJDh58iSqVKmCWrVqAQAWLFiAO3fuYPPmzYZjWrZsiY0bN+KNN96AUqlEWloaNmzYgODgYEOCaNmyZRg5cqRhRNH//vc/HD9+HOvWrSvDV4BKw/g6Qfjs8kE8zkhFnCYNy2+GYHKDtqYOi4iIKjCTJYly5u0/3bFTq9V55vQ/6YsvvkCXLl1w+PBhhIeHY+nSpViyZEmeId5P4pBqKqgsMRWZusR8p0mpXFwgd3IyQWTlkJMTRHdvaAPbQZeRAqmlLWSOHpyuR0QlrjhDqkvK8OHDcebMGbRo0QL169fHtWvXMHToUEycONHQJiIiAgcPHoRer4dEIoGzszMGDBgACwsLJCQkICsrCzt37oSvr6/hmG+++QaDBg2Ct7c36tati5s3b8LDwwObNm0ytNHpdPD19YWbmxvUajUiIyOxcOFCDBs2rExfAyp5NnIFJjdoi5nns1e8++KfI3jNtwUsZazsR0REpUMQTbQWdWZmJmxtbfHdd99h/Pjxhu3du3eHpaUltmzZUqDziKIIT09PvPTSS/jiiy8KdExycjLs7e2RlJTEJBHlIooiEo+tMrrimUO7caxJRERkhszpvv7w4UPcuXMH1atXh5dX7lUcHzx4gOvXr6NTp06Ge4lOp8Ply5chCAIaNmyY7/Dwu3fvIjIyElWqVEHNmjXz3Is0Gg2uXLkCuVyOWrVqPfMDN2PM6TWk3JIzM1D9f/OQmJkOAPihxYuYWLeliaMiIiJzVpz7usmSRADQu3dvpKWl4cCBAxAEAVFRUfDx8cH333+PMWPGAABOnDiBR48e4cUXXwSQ3flyd3c3nGPt2rUYO3YsLl++nGtO/7OwI0TPolXHQX1ha64Vz+yChkDZpD9kthxFRERkbnhfLz6+huZt9oW9+OTSAQBAdVtH3BowA3KJ1MRRERGRuSq3SaKrV6+iZcuW6NixI4KDg7FmzRo4ODjgyJEjhqVix40bh9OnTxsKUy9YsACnT59Ghw4dcOPGDfz8889YunQpxo0bV+DrlkZHSBRFaOMioNOkQKqwhUzlyREn5ZgoitDGR/w3TcqJ/59EROaKCY7i42to3uIyUlH9f/OQqs0EAKxuPQSv1Dbdqr5ERGTeinNfN+kk/vr16+Py5ctYvXo17t+/jzfffBOjR482JIgAoE2bNrlGDr333nvYvXs39u/fD1dXV5w/fx716tUzRfgGxkeeDIaycX/IlPmvLELmSxAEyFVe4Ix/IiIiMjWVpQ1eq9sCi0KPAgA+u3wII2o2gZQr0RERUQkz6UgiUynJT8tYw4aIiMi0OAqm+Pgamr/otGR4//4ZNLrslXk3th+BwT6Bpg2KiIjMUnHu6/z4oZi0cRFIDtlodF9yyEZo4yPKOCIiIiIiqmiqWtthfJ0gw+N5lw6iEn7WS0REpYxJomLSaVKMLpcOAPoMNXQZKWUcEZkLURSRFRuOjIdXkRUbzo4cERERFct0v/aQCdnd98sJUdgZcdXEERERUUVj0ppEFYFUYQuJpdJookhiqYTU0tYEUZGplfc6VSzETkREZH68bB3xSq2mWHnrDADg00sH0MuzPu/RRERUYjiSqJhkKk/YBQ0xus8uaAhkTp5lHBGZmiiKUF/YisSjKwzJQ32GGolHV0J9YavZjyjSquOQeGwVIleNQ9TKsYhcNQ6Jx1ZCq44zdWhERESV3oyGHSH5Nyl0JjYCB6NumTgiIiKqSJgkKiZBEKBs3A8O7cZBYqkEkD2CyKHdOCib9OcnO5VQea5TVd4TXERERBVdLTtnDH2iYPWnlw6YLhgiIqpwSmS6WWpqKkRRhK1t5ZxaJVOq4NB2DGz9ukCXkQKppS1kTpyeU1kVpE6VvIxjKqjnJbhs/bpArvIq46iIiIjoSe837IgNdy4CAI5G38GJmLtoXcXHxFEREVFFUKiRRGlpaRg/fjz8/f0xd+5c6HQ6jB49GkqlEvb29njppZeg0WhKK1azJggC5CovWLrXh1zlxQRRJZZTp8oYc69TxULsRERE5s/P0Q39vfwMj+dxNBEREZWQQiWJZs6cif3796NLly5YvXo1Ro0ahePHj+OXX37BihUrcOLECSxdurS0YiUqF8pznarynOAiIiKqTD4I6Gz4fu/DGzgf+8CE0RARUUVRqOlmW7Zswf/+9z80b94cw4YNQ7NmzXDixAm0atUKAFCtWjVMmzYN06dPL5VgicqDnDpVAJ5a3WyI2depyklwJR5dkWefuSe4iIiIKpMmzh7o5u6LvQ9vAMgeTfRHp1GmDYqIiMq9QiWJHj9+jIYNGwIAAgICAADNmjUz7G/ZsiXu3btXctERlVPltU5VeU5wERERVTYfBHQ2JIm2hIciNCEKfo5uJo6KiIjKs0IliXx9fXH37l3Uq1cPcrkcvr6+sLCwMOxXq9VwdHQs8SCJyqOcOlXmWqQ6P+U1wUVERFTZtK7ig3ZVa+Bo9B0AwPzLh7C+3XATR0VEROVZoWoSvfrqq/j5558Nj69fv55r/8GDB9GrV6+SiYyITIaF2ImIiMqHDxr+V5vot7t/Iyw51oTREBFReVeoJNHYsWPRpUuXfPffuXMHs2bNKnZQVPGJoois2HBkPLyKrNhwiKJo6pCIiIiIyp3O1WqjuXN2zUC9KOLzy4dMHBEREZVnglgJ350nJyfD3t4eSUlJsLOzK5FziqIIbVwEdJoUSBW2kKk4PSc/WnUc1Be2PlXzZjCUjftDplSZOjwiIipnSuO+XtnwNSzftodfQd+D2aP9ZYIEtwe+Dy9bloAgIqqsinNfL1RNoielpqbi1KlTSElJgb+/P2rWrFnUU5V7THoUnCiKUF/Ymmv1LH2GGolHVwIQ4NB2DJNrRERERIXQy7MeGjq64XJCFLSiHl+EHsE3wf1NHRYREZVDhZpulmPJkiWoU6cO5syZgzVr1qBjx44YO3Ys9Hp9Scdn9nKSHimh+2Hj1xV2wS/Bxq8rUkIPQH1hK6dRPUUbF4HkkI1G9yWHbIQ2PqKMIyIiIiIq3ySCBDMbdjI8/ulmCKLTkk0YERERlVeFThJNnToVW7duxalTp3D8+HFs2bIFYWFh0Ov1WLlyJQDg0aNHyMrKKvFgzZE2LgLaxChYqDwRt28JHv3+AeL2LYHcyQNZiZFMejxFp0mBPkNtdJ8+Qw1dRkoZR0RERERU/g30bog6di4AAI1Oi6+uHDNxREREVB4VKkl0/PhxbN++HTt37kR4eDhOnDiBEydOICQkBD179sRPP/0EAOjatSt27NhRKgGbG51WA316EuIPLYM+LREAoE9LRMLhHyGmJ0OXpTFtgGZGqrCFxFJpdJ/EUgmppW0ZR0RERERU/kklErzfsKPh8ffX/0JcRqoJIyIiovKoUEmib7/9FjNnzoSNjQ1mzpyJNm3aoF+/fujfvz8GDRoEZ2dnAMCYMWOwZs2aUgnY7GgzkXRmk9FdSWc2AbrMMg7IvMlUnrALGmJ0n13QEMicPMs4IiIiIqKKYXjNxqj+b8HqVG0mll47YeKIiIiovCn0SKIXXngBAODr64t58+bh8ePHePz4MebNmwcPDw8AQL9+/RASElLy0ZojARAkxut/57e9MhMEAcrG/eDQbpxhRJHEUgmHduOgbNKfRauJiIiIikgukeI9/w6Gx0uvnkByZoYJIyIiovJGEAtRWdnCwgJqtRoKhQKurq64c+cObG2zpwelpKTAzc0NarUaWVlZsLa2Ntu6RCW5zGtWbDge/jQKWXH3oUuJg6jXQZBIIbVVQe5cHe7jV0Ou8iqhyCsOURShjY+ALiMFUktbyJw8mSAiIqIi4fLtxcfXsOLI0Gahxu/zEZWeXbh6fpMemPHENDQiIqr4inNfL9RIIpVKhQcPHgAALC0tcfHiRcO+ixcvwtExe3hrXFwcnJycChVIeSVTecK+5XDI7Fxh4VoDFq41YeFaAzI7V9i3GM7pU/kQBAFylRcs3etDrvJigoiIiIioBFjK5Jju397wePmN08jQmecHt0REZH4KNR+qTZs22L9/P2rWrIkPP/wQvXv3xqBBgyCKIn7//XcsXLgQAHDo0CEEBweXSsDmJmf6FJC9hLs+Qw2JpRJ2QUM4fYqIiIiIytyEOkFYH3YBY+o0x8hajWEplRv26UUREvZPiYgoH4Wabnb48GGMHz8ely9fhrW1Nc6ePYu9e/cCALp164ZmzZpBo9GgWbNm+Pzzz9GjR49SC7w4SmNINadPERERmQanShUfX8OKJ02bCWuZBS7EPcCasHOISlPDzVqJV2o1Q2OVO5NFREQVWHHu64VKEgHA+PHjERMTgw0bNhjqEeXIyMjAiBEjYGFhgQ0bNhQqkLLEjhAREVHFUdT7+r59+7Bt2zY0bdoUo0ePxoEDBzBnzhwkJCSgd+/emDt3LuRy+fNPVAGwb1Sx6EUR6bosDDm8FrseXMuzv6dHPWzsMBJWUjkTRWZEFEV+yExEJaI49/VCL7/1ww8/4O2334a/vz/Gjx+Ppk2bQiKR4Pz581i2bBk6deqE77//vrCnJSIiIiozR44cQY8ePRAYGIgNGzYgPj4es2fPRu/evREYGIjly5dDEAR89tlnpg6VqNAkgpBvgggAdj24hqFH1mFH5zFlHFnJqghJleTMDCwKPYLlN04jJiMFVSxtMcE3GNP920MptzR1eERUCRV6JFGOixcv4tdff8XVq1cBAHXr1sXQoUPRtGnTEg2wNPDTMiIiooqjKPf1YcOGwcfHB/PmzcOtW7fQsGFDvPPOO5g/fz4A4OzZs+jUqRMSExMhkRRqnY9yiX2jikMvirgY9xBNdyx+btvzfd5BXXtXvHl6CwQIsJZZwFomz/6SZn9vI7N4YrtFrn1PbpdLpKX/5FCxkirJmRlos/s7hCZGQ//EWzKJIMDf0Q3He7xe7p4TEZmHMh1JlKNRo0Zo1KhRUQ8nIiIiMpl79+5h0qRJAIDatWvDy8sLPXv2NOxv1qwZ7O3tERkZCQ8PD1OFSVRoEkHAL7fPFajtmrBzWBLUDw9Sk7Av8maxrisTJLmTSVJ5oZJM2e3zPrZ5YlumTou2e77PlVSJyUjBvMsHsT3iqiGpIooidKIeWXo9tKIOWr0eWXodtOK//+r/25el10Or1+VuK+belt0m7zly7cv3OtmPje27nvQId1Pi87yWelFEaEIUFoUexZxGLxTr/4WIqLCKnCTKkZGRYfheEAQoFIrinpKIiIioVFWrVg1xcXGGx1ZWVnlqLUqlZTMygqikRaWp893X5u94pFtIcM3HFtHp2e3sLIo/WkUr6pGclYHkrIznNy5helHEpfhIOK6fBQECtKK+zGMoaTpRxPIbp5kkIqIyV6gk0d27d/Hxxx9jzZo1hm1WVla52pw5cwbNmjUrmeiIqEyIoghtXAR0mhRIFbaQqbg6HxFVbIMHD8b27dvRu3dvANk1ipRKpWH/o0ePIJFIOIqIyiU3a2W++yKdFWgYpkbNyDRYxV7Fw9oP8Uqtpgh28UKaNuvfr0yk6f79N7/H2iyk6bL/NRc6UQRQpEoaZik6XV0h6i4RUflSqCTR4sWL0bx58zzbf/31VwDAsWPH8N1332H16tUlEhwRlT6tOg7qC1uRHLIR+gw1JJZK2AUNhrJxf8iUKlOHR0RUKgYNGoRbt25Bo9FAoVDAwcEh1/6VK1di3rx5pgmOqBj0oohXajXD0qsnjO6/7WGDe25WqPkwDe2jE/Huu++iWbNmeLFvX/j4+BT6euK/K6nlSh4VMsmUps1Eap59/32fqs2ErhijgySCAJkggVwihUwigVz491+JFDJBkut7Q5sn9z3VPqeNsfZ59uV3TokUr/21GUnPGHklgYA9D66jh2e9Ij93IqLCKlTh6gYNGuD3339HvXr//aESBAE5p7h79y7at2+P+/fvl3ykJYjFGYmyiaKIxGOrkHh0RZ59Du3GwaHtGH56RURmj/f14uNrWPH02r8y39XNAKCXZ31saf8yjh8/ju3btyMmJgYBAQHo27cv6tatW4aRFkyWXgePjXPxKCM13zYulja41v/dPAkZiWCexednX9iLeZcP5ipabcy4OkH4slnvEpkWSESVQ3Hu64X6i3nv3j14e3vn2vbqq68avnd3d0dUVFShAiAi09HGRSA5ZKPRfckhG6GNjyjjiIiITCMzMxOPHz+GTqczdShExaYXRWzsMBK9POsb3d/Lsz5+az8CEqkUHTp0wKJFizBp0iTEx8dj7ty5mDt3Li5duoQiLoJcKuQSKV71bQFJPh9eSQUBr9VtCZWlDewtrGAts4CFVGa2CSIAmO7fHv6ObpA+9ZwECJDgv20rboag4bYvcTgqrKxDJKJKqFB/NSUSCR49epRr27JlywzfJycnw9KSGW6i8kKnSYE+w3hxS32GGrqMlDKOiIiobN25cwfDhg2Dg4MD/Pz84OzsjFmzZkGvL/+Fb6nykggCrKRy7Og8Buf7TMZb9VtjsE8A3qrfGuf7TMaOzmNgJZUbEi5SqRQtW7bE/PnzMXXqVGRlZWHBggX48MMPcfbsWbP5fcgvqSIVBPg5umGaXzsTRVY0Srkljvd4HR8EdEZVq+w6UlWtlJgV2Bmh/aahc7Xahrb3UxLQce8yvBOyDWnaTFOFTESVQKGmm7Vo0QJjx47FuHHjjO5ft24dfvjhB5w8ebLEAiwNHFJNlC0rNhyRq8YZTRRJLJWoNnYF5CovE0RGRFRwRb2vnz17Fv3798e7776L8ePHw8rKCrGxsXjjjTcQFBSEKVOmAAD0ej0kEvMdjVAS2DequPSimGv0zdOPjRFFEaGhodi2bRuuXr0Kd3d39O3bFy1atDD5qn/qrAwsCj2K5TdOIzpdjapWSkzwDcY0v3ZQysv3h9VPF6nWi3osu34K08/tzFUgvI6dC9a0GYpg1+qmCJOIyoHi3NcLlSRavnw5Zs6cia1bt6J169a59h07dgyDBw/GvHnzMHbs2EIFUdbYESLKxppERFQRFOW+np6ejgYNGuDHH39Ely5dcu3LyMhA/fr1cefOHbz33nuoXr06Xn/99QKd9+7duwgLC4O3tzdq16793PZ6vR7Xrl1DVFQU6tatm+9qaleuXEF0dDRcXV3RoEEDo0mrwl77SewbUX5u3ryJrVu34u+//4arqyt69+6Ntm3bQi6Xmzq0SrPyV1hyLEYd/w0nH90zbJMIAmb4d8TswC5QSAu1FhERVQJlliTS6/UYMmQIfv/9dzRr1gx16tSBKIq4efMmzp07h6FDh2LDhg1m/8eaHSGi/xhf3WwIlE36Q2brZOrwiIieqyj39RUrVmDfvn3YtGkTgoKCcPbs2Vz1VywsLHD37l0cOXIEixYtwoULF555PlEU8dprr2Ht2rUIDAzE5cuX0adPH6xZswYymfE3cGFhYRg4cCDi4uJQq1YtXLhwAePGjcOXX35paPPw4UP07NkTMTExaNCgAa5fvw6lUomdO3eiZs2aRb7209g3oue5d+8etm3bhjNnzsDBwQE9e/ZEx44dWWqijOj0enx15Sg+vLAXmfr/aqc1dHTDL21fQoBTNRNGR0TmpsySREB2R2T9+vX49ddfcfPmTQBAnTp1MGzYMAwbNszsE0QAO0JETxNFEdr4COgyUiC1tIXMybNc/C4TEQFFu68PHDgQgwcPxuDBg7Fv3z58/PHHWLx4MVxcXPDjjz8iOjoaq1evRlZWFhwcHPDw4UM4ODjke741a9bg9ddfx5kzZ9CgQQOEhYWhadOmmDt3Lt566y2jx7Rt2xYKhQK7du2ChYUFIiIiEBgYiO+++w5Dhw4FAEyYMAHHjh3DxYsXYWVlhczMTLRo0QK1atXCxo0bi3ztkngNqXJ6+PAhduzYgRMnTsDGxgbdu3dHly5dYGNjY+rQKoUrCdF4+fivuBD30LBNLpHio8AueM+/A2QS004HJCLzUKZJooqAHSEiIqKKoyj39eDgYCxZsgRBQUHo378/Jk2ahE6dOhn2e3l54cyZM6hatSrq16+PzZs3o169evmer1OnTlCpVNi0aZNh29ixY3Hp0iWcO3cuT3udTgdLS0ssW7Ys1zT9gQMHIjk5Gfv27QMAvPTSS0hKSsLu3bsNbZ7eVthrG8O+ERXW48ePsXPnThw5cgQymQxdunRBjx49+PNTBrL0Osy7dACfXjoInfhfUfHmzp5Y0+Yl1HVwNWF0RGQOinNfL9QEVo1Ggz179qBfv35G92/duhXdu3eHQqEoVBBEREREZcna2hqxsbEAsjtSiYmJhn2ZmZlITU1FVlZ2odi0tDRYW1s/83yXLl3C5MmTc20LDAzEunXrjNZNkUqlcHZ2xu3bt3Ntv3v3LiIiIgyPZ86cid69e2PmzJlo0qQJ/vnnH5w8eRJ//PFHka8NZPfpNBqN4XFycjKA7NIC5rKSFZk3lUqFV155BX369MGePXvw559/Yu/evejQoQN69OgBlUpl6hArLCkEzA7ogp4e9TDq+EZcTYoBAJyJjUCj7V/hs8bd8Wa9VpAIFbvgPhHlrzj38kIliVauXInr16/nmyQ6ePAgoqKi8NprrxU5ICIiIqLS1rhxY5w5cwY9e/bEiBEj8OabbyIqKgrOzs5Ys2YN/Pz84OnpiZiYGKjV6nwLSudITEyEk1PuOm4qlQqZmZlIS0szOhVnxowZmDFjBhQKBerVq4ft27fj4cOHSEhIMLTx8fFBt27d8PPPP+P06dO4ceMGOnfujFq1ahXr2vPnz8ecOXPybE9ISIBWq33mcyV6Wrdu3dCmTRscPXoUx44dw4EDB9C8eXN07twZLi4upg6vwvIRrPFnq5H4/MYxfH/7DEQAGTotppzdgd/v/I2lgT1R3drB1GESkQmo1XlXry6oQiWJVq9ejR9++CHf/aNGjcLrr7/OJBERERGZtREjRqB79+549913MXr0aNjY2GD9+vVQq9Vo0aIF3n33XQDA0qVLMXjw4Ocu+21hYYG0tLRc23IeW1hYGD3m7bffRt26dbFlyxb8888/aNeuHerVq4cvvvjC0GbcuHEICwvD7du3YW1tDY1Gg27dumH48OHYtWtXka/9/vvvY8qUKYbHycnJ8PT0hKOjI6cLUZE4OTlhxIgRGDBgAA4ePIg9e/bg9OnTaNGiBXr37g1PT09Th1hhLXUZiMG1m2D0iY24kxIPAPgrLgLtj67Coma9Ma52c9aaJKpkCrpwhdFjC9P4xo0b8PX1zXd/nTp1cOPGjSIHQ0RERFQWAgMD0bdvX4waNQobNmwwFLF+0q5du7B69eoC1fXx8fHBgwcPcm2LiIiAu7v7M5cKf+GFF/DCCy8YHr/44oto2LCh4fGBAwcwbdo0w3Q3hUKBIUOG4K233jJMJSvKtRUKhdHyABKJBBIJp6hQ0dnY2KBPnz7o1q0bjhw5gp07d+L9999HkyZN0K9fP8OqfFSy2rrVxKV+U/HeuV34/vpfAIAUbSYmntqMbeFXsKL1IFSztjdxlERUVopzLy/UkVqt9plDkJ+3n4iIiCofURSRFRuOjIdXkRUbDnNZM2PJkiUQBAGtWrXCtm3bkJiYCK1Wi3/++Qdvv/02Xn31VWzbtg1ubm7PPVe3bt2wY8cOQz9Ir9djy5Yt6Natm6HNvXv3sHPnTsPzT0pKynWOW7duYffu3bkKWbu5ueWpWxQWFoaqVasaRgYU5NpEZc3CwgJdu3bFV199hQkTJuDhw4eYNWsW5s+fj2vXrpnN34GKxFauwHctXsSfXcfD44mE0J6H1+G3ZRE23L7A152InqtQq5s1b94cU6ZMMSzL+rRff/0VixcvRkhISIkFWBq4ggcREVHZ0KrjoL6wFckhG6HPUENiqYRd0GAoG/eHTFkyhW2Le1///fff8dNPP+H8+fPQaDTw9vZG3759MWXKlDy1fvITHR2Nxo0bo0mTJhg8eDC2bduGw4cP4/z58/D29gYAfPvtt3jzzTeRlZUFmUyGzZs3Y926dRg4cCDi4+OxcOFCtG7dGhs2bDAkgNatW4dRo0Zh8uTJaN68Of7++28sWrQIX375JSZNmlTga5f2a0j0PHq9HiEhIdi2bRvCw8NRp04d9O3bF4GBgZwKVQoSNel458w2rAnLPRJyQHV//NByAFwsbU0UGRGVheLc1wuVJPrhhx8wd+5cbNy4EW3bts2179ixYxg8eDDmzp2LCRMmFCqIssaOEBERUekTRRGJx1Yh8eiKPPsc2o2DQ9sxJfLm0Fzu6w8fPsSSJUtw+/ZtVK9eHW+++SZ8fHwM+3ft2oUffvgB27dvNwwD3717NzZu3AhBENCrVy8MGDAgz2ty6tQp/Pbbb4iMjESVKlUwYMAAdOjQoVDXfh5zeQ2p4hNFERcvXsTWrVsRFhZmSMo2a9aMUx1Lwbb7oZjw1+94lJFi2OZqaYvlLQeib3U/E0ZGRKWpzJJEer0eQ4YMwe+//46mTZvC19cXoiji5s2bOHfuHIYMGYJff/3V7D8NYEeIiIio9GXFhiNy1TjoM/KusCGxVKLa2BWQq7yKfR3e14uPryGVNVEUcfXqVWzduhVXrlxBtWrV0KdPH7Rs2bJYBVcpr9iMVLx2ajN+v3c51/aXazbBkqB+cFBYmSgyIiotxbmvFypdL5FIsGnTJqxbtw6urq4ICQnB2bNn4erqivXr15eLBBERERGVDZ0mxWiCCAD0GWronvhk2xQ+/fRTpKenGx4PHz4cTZs2NXzt37/fhNERVWyCIKBBgwb44IMPMGfOHFStWhXLli3D1KlTsX//fmRmZpo6xArD2dIGm9qPxIZ2w+Fo8V9C6Jfb5+G/dRH2P7xpwuiIyNwUaiRRRcFPy4iIiEqfOY8k+vPPP7FkyRLs3r3bsC0wMBB169aFs7MzoqOjkZKSgr179xY7vvKAfSMyB+Hh4di6dStCQkJgb2+PHj16oHPnzrC0tDR1aBVGZFoSxp34H/Y8vJ5r+2t1W2Bh016wledd9ZCIyp8ym272pIiICNy9e9ew9KqHh0dRTmMS7AgRmS9RFKGNi4BOkwKpwhYylSdHKBKVU+Zck2j8+PFo2bIlRo8ebdgWGBiI1atXIzAwEBkZGXBxcUFMTIxh+fmKjH0jMidRUVHYsWMHjh8/DisrK3Tr1g1du3aFrS2LLZcEURSx8tYZTA7ZjhStxrC9plKF1W2GonWVgtczIyLzVGbTzQDgwoULaNasGby8vNCuXTu0bdsWnp6eCAoKwqVLlwp7OiIiA606DonHViFy1ThErRyLyFXjkHhsJbTqOFOHRkRFIAgClI37waHdOEgslQCyRxA5tBsHZZP+Jk0A3759GzVr1sy1rVatWrCyyp6KYWlpCTc3N0RERJgiPKJKzc3NDRMmTMDXX3+Nli1bYtu2bXjrrbfw66+/IikpydThlXuCIGBcnSD8028q2lf97+/gbXUc2u7+HtPP7kCGNsuEERKRKRVqJFFERAT8/f3h7++PiRMnwtfXFwBw/fp1LFu2DFevXkVoaCiqVatWagGXBH5aRmR+ymrEARGVPVEUoY2PgC4jBVJLW8icSnaEYFHu6y1atMCcOXPQtWvXfNvUqlULu3fvRp06dUoqVLPFvhGZs6SkJOzevRsHDhyAVqtFhw4d0KtXLzg7O5s6tHJPL+rxzdWTmHF+FzJ0WsP2+g5V8Eubl9DEufzMFiGi/5TZdLOpU6fi+vXr2LFjR54lKvV6PXr06IGGDRti4cKFhQqirLEjRGR+yqp2CRFVPEW5rw8fPhwNGjTAzJkzje6PiYmBt7c34uPjDaOLKjL2jag8SElJwb59+7B3716kp6ejdevW6NOnD9zc3EwdWrl3I+kRXjn+G0Iehxu2SQUJPgzohA8COkMukZowOiIqrDKbbnbgwAHMmDEjT4IIyF75bObMmVwJhIiKxNxXQSKiiuXFF1/EwoULcfNm3lV9NBoN3n77bbzwwguVIkFEVF7Y2trixRdfxNKlSzF06FBcunQJ06ZNw9KlSxEeHv78E1C+fO1dcaLHG5jXuLshIaQT9Zjz934E71yKKwnRJo6QiMpKoUYS2dnZITIyMt+icWq1Gh4eHmY/V5iflhGZH44kIqKiKsp9XRRF9OnTB/v378eIESPg7+8PW1tb3LlzBxs2bEBycjLOnDmTp25RRcW+EZVHmZmZOHr0KHbs2IHY2Fg0atQI/fr1Q+3atU0dWrl2KT4SLx/7FZcTogzbLCRSfNq4G6Y0aAepkQEDRGReymy6mUQigV6vL3YbU2NHiMj8sCYRERVVUe/rWVlZ+Oyzz/DTTz/h4cOHAAArKyv06NEDCxYsqDQJIoB9IyrftFot/vrrL2zfvh2RkZFo0KAB+vXrh/r167PvUESZOi3m/r0f8/85BP0TbxdbuXpjdZuhqGXHelBE5qzMkkSCIODx48fPbOPi4oJCnNIk2BEiMk9adRzUF7YiOWQj9BlqSCyVsAsaAmWT/pDZOpk6PCIyUyVxX4+Li0NmZiacnZ0hl8tLOELzx74RVQR6vR5nz57Ftm3bcO/ePdSqVQt9+/ZF48aNmSwqopDH9/Hysd9wM/m/94DWMjm+aNoLE+u2gETgqCIic1SmSaKCYJKIiIqqtFdBIqKKp6j39ZiYGKhUKshksjz7Hj9+DAcHh0qTMGLfiCoSURRx6dIlbN26FTdv3oSXlxf69u2LoKAgo7VV6dnStJn44PweLL56PNf2ztVqY1WrIfC0dTBNYESUrzJLEu3cubNA7Xr16lWoIMoaO0JEREQVR1Hu63fv3kX37t0RGhpqNEn00UcfQS6X48MPPyzpcM0S+0ZUEYmiiOvXr2Pr1q34559/ULVqVfTp0wetW7c2+ntPz3YkKgyjTmzE/ZQEwzY7uSWWBvXFy7Wa8kM9IjNSZkmiioIdISIiooqjKPf16dOno1q1apg8ebLR/fHx8ahXrx5iYmJKMlSzxb4RVXS3b9/Gtm3bcO7cOahUKvTq1QsdOnSAhYWFqUMrV9RZGZh6Zgd+uhmSa3sfzwb4seUAVLX+7++HKIpMHBGZSJklib799tsCtZs0aVKhgihr7AgRERFVHEW5r3fr1g3Tpk1D586d823j7u6OCxcuoEqVKiUVqtli34gqi4iICGzbtg2nTp2CUqlEjx490LlzZ1hbW5s6tHJld8Q1jDv5P0SlJxu2qRTW+Kp5b4Qlx2H5jdOIyUhBFUtbTPANxnT/9lDKLU0YMVHlUmZJoqpVqz5zf0JCAjIzM1mTiIiIiMpMUe7rwcHB+Prrr9GiRYt82/j6+mL79u3w9fUtqVDNFvtGVNlER0djx44dOHbsGBQKBbp164YXXngBSqXS1KGVG/GaNLx5egs23LmYa7sA4Ml3gxJBgL+jG473eJ2JIqIyUpz7eqEqt0VHRxv9OnPmDLp16watVovu3bsXKgAASExMRFhYGDIzMwt8THx8PG7fvo309PRCX4+IiIgqN29vb4SEhOS7PzY2FuHh4fDw8CjDqIiorFStWhXjx4/H4sWL0bZtW+zcuRNvvfUW1q9fj4SEhOefgOCksMb6dsPxvw4joVL8NxLr6eECelFEaEIUFoUeLdsAiahIilXePyEhAe+++y58fX1x/fp1HDp0CLt37y7w8VqtFmPHjkWVKlXQtm1buLq64pdffnnmMTdv3kRwcDCqV6+Orl27wsnJCWPHjkVWVlZxngoRERFVIi+++CIWLFiAmzdv5tmn0Wjw5ptvonPnzrCxsTFBdERUVlQqFV5++WUsXboU3bp1w6FDh/DOO+9g1apVePz48fNPQBjoHYAr/adDIZHm20Ynilh+43QZRkVERVWksv4ZGRn45ptvMH/+fLi4uGDt2rUYOHBgoc/z2WefYefOnbh69Spq1qyJX375BaNHj0bDhg0RGBho9JgJEybAysoKMTExsLa2xrVr19C8eXM0bNgQb7/9dlGeDhWDKIrQxkVAp0mBVGELmYrLlRMRkfkbOHAgfvnlF/j7+2PEiBFo2LAhbG1tcefOHaxfvx5qtRqnT/MNDVFlYWdnhyFDhqBXr17Yv38/du/ejUOHDqFVq1bo06cP3N3dTR2iWXO1tIVGr3tmm+h0NYtZE5UDhUoS6fV6rF27FrNmzUJWVhbmzZuH8ePHF3kJyeXLl2P8+PGoWbMmAODll1/G/PnzsWLFinyLZD948ADDhw83FJerV68efHx8EBERUaQYqOi06jioL2xFcshG6DPUkFgqYRc0GMrG/SFTqkwdHhERUb4kEgm2bNmCzz//HD/++CNWrVoFALCyskLPnj3x+eefG/onRFR52NjYoF+/foZRRbt27cKJEyfQrFkz9OvXD97e3qYO0SwJgoAqlraIyUjJt01VKyUTRETlQKGyO4GBgbh16xbefPNNTJ06FTY2NsjIyMjTztbW9rnnio6OxsOHDxEUFJRre4sWLXD+/Pl8j5sxYwbmzJmDhg0bonr16ti3bx9iY2MxYcKEwjwVKiZRFKG+sBWJR1cYtukz1Eg8uhKAAIe2Y3gTICIisyaXyzFr1izMmjULcXFxyMzMhIuLS5E//CKiisPS0hI9evRAly5dcOzYMezYsQMzZ85Ey5Yt8cYbb7Cfa8QE32DMu3wQ+nwWMRpfJ8jodiIyL4XqBf3zzz8AgC+++AJffPFFvu0KsrpZXFwcgOx5wE9SqVSIjY3N97hBgwZh3759eOWVV+Dq6oqYmBjMnz8fderUyfcYjUYDjUZjeJycnL1Uo16vh16vf26slFdWXASSQjZBRN4bZFLIJlg36Ay5k6cJIiMiosqmJO7lTk5O0Gq1TBARUS5yuRydOnVC+/btcerUKURHRzNBlI/p/u2xPeIqQhOioDPyflAmFKscLhGVkUL1hHbs2FFyF/63E/b0imYajQZyuTzf43r27AlbW1tER0fD1tYW169fR+vWraHVajFlyhSjx8yfPx9z5szJsz0hIQFarbYYz6Lyyox9DLXUAbBxMLrf4vFjWIDFPomIqPSp1eoiHZeVlYUvv/wSv/zyC27cuAG9Xg9XV1e88MILmDNnDnx8fEo4UiIqr6RSKVq3bm3qMMyaUm6J4z1ex6LQo1h+4zSi09WwksqRrsteYGjupQPoXK0OWlbxNm2gRPRMgliQYT+lICUlBXZ2dli/fj1eeuklw/ZBgwYhOTkZf/75Z55jIiMj4e7ujp07d6Jnz56G7ePHj8fly5fzXcrW2EgiT09PJCQkwM7OrgSfVeWRFReBqJ8nQJ+Rt2MusVTCbcxyjiQiIqIykZycDEdHRyQlJRXqvt6/f3/s3bsXAwYMQGBgICwsLBAeHo7NmzcjJSUF586dQ/Xq1UsxcvORnJwMe3v7Qr+GRET5EUUR6bosBO1YitDEaACAp40DLvaZDJUlP0wmKk3Fua+bbEy1ra0tmjVrht27dxuSRBqNBgcOHMB7771naBcZGYm0tDTUqlUL9vb2EAQhz3S0x48fw9HRMd9rKRQKKBSKPNslEgkkEg57LAoLZy/YBw3OVZMoh33QYFiovDgUl4iIykRR7uX79u3DkSNHcP78edSvXz/Xvk8//RQDBgzAhx9+iLVr15ZUmERElYogCLCWWWBTh5FoumMx0rRZiEhNxOgTG7Gt02i+VyAyUyadeD9nzhz06tULfn5+aNGiBb7++mvY2Nhg4sSJhjazZ8/G6dOnERoaChsbGwwfPhwzZ86EQqFAjRo1sG/fPmzfvh3/+9//TPhMKh9BEKBs3A8AnlrdbAiUTfrzjz4REZm1bdu24e23386TIAKyC9Z+8803CAwM5HLNRETFVM+hCr4PfhGjTmwEAOyIuIrFV49jcoO2Jo6MiIwxaZKoW7du2LlzJ5YuXYpNmzbB398fJ06cgIODg6GNu7s7ateubXi8YsUKfP/991i9ejXi4uJQvXp17NmzBy+88IIJnkHlJlOq4NB2DGz9ukCXkQKppS1kTp7sTBMRkdm7du0aXnzxxXz316hRA0qlEg8fPoSHh0cZRkZEVDH06dMHM2fORHBwMF6p3QyHo29jTdg5AMB753ahlas3mrt4mThKInqayZfw6NatG7p165bv/qcLTisUCkyePBmTJ08u7dCoAARBgFzlhfxLjRMREZmf5ORkODk5PbONs7MzEhMTmSQiogrnyy+/hE6nw7vvvltq17hw4QISExMNj78L7o+Qx+G4nvQIWXodhhxZi4t9psBBYVVqMRBR4bEgDxEREVU6Wq32uSNfBUHgKqhEldDFixcxYcIEdOrUCePGjcPff/+da39MTAzefPNNtGvXDsOHD8+1Py0tDdOmTUOXLl2wZMmSXMdFRERgyJAh0Ol0JRrvwoUL8eWXXxbqmAEDBmDQoEElGsfz2MgV2NR+JCyl2eMU7qUkYMyJjTDROkpElI8CjyR6cgrY8zyZMSYiIiIyR/379ze6sEWOe/fulV0wRGQWLly4gMmTJ2P06NEYOnQodu/ejZYtW+LcuXOoX78+MjMz0bFjR9SsWRMzZszAwYMH0aZNG1y8eBG1atXCJ598ghs3bmDatGl4++23UaNGDfTu3RsAMGHCBEyaNAlSqbREY75z5w5kssJNEPH29i7RGArK38kN3wT3x/iT2fVkt4SH4rtrJzGpfmuTxENEeRX4r8nq1asN3//zzz+YP38+xowZg2bNmgEAzp49i1WrVuH9998v8SCJiIiIStKYMWMQGRn53HZVqlQpg2iIyFzUr18fR48eNTzu2LEjdu7cib1796J+/frYsmULwsPDce7cOVhZWaF79+44e/YslixZgm+++QZ//fUXPv74Y3To0AEvv/wyTp48id69e+Pnn3+Gi4sLevbs+czrHz58GD/++CNefvllrF27FlKpFOvWrcOJEyewYsUKPHjwAH5+fvjggw/g4uKCDRs2YMuWLRAEAefOnYNMJjO0XbFiBQRBQNWqVdGrVy+MHTvWcJ2np5vNnz8fCoUCUqkU+/btg6WlJd555x20adPGcEx+MeRYvXo1fv/9dyiVylzXetrY2s1xOCoMG+5cBABMPbsDLV290diZU3uJzEGBk0T9+vUzfP/1119j/fr16N+/v2HbK6+8gk6dOmHJkiWYNWtWiQZJREREVJLeeustU4dARGbI0tIy1+Pw8HBERkbC398fAHDq1Cm0aNECVlb/1dHJSSQBgIuLC27evIkOHTrg1q1b8PPzQ2RkJBYuXIiTJ08+9/pxcXHYsmUL4uLiMGXKFLi7u+P333/HxIkTMWfOHIwcORKbN29Gy5YtERoaig4dOqBVq1aQSqWYOnWqYRptt27d4OfnByB7VOSsWbOgVqvxzjvvAADu3r2bazrt7du3sX79erzzzjuYOnUqDh06hB49euDu3btwdnZ+ZgwKhQLffPMNPv30U3zxxRdwcnLCzJkzERMTY/Q5CoKAZS0H4GxsBG4lxyJTr8PgI2txoc9k2FlYGj2GiMpOkQpXX7x4EZ06dcqzvVOnTnjllVeKHRQRERFRabpx4wbS09Of287X1zfXm0Eiqhz69OmDiIgIhIWF4bPPPkOXLl0AALGxsXB2ds7V1sXFBbGxsQCAadOmoXfv3liyZAl0Oh0WLVqE0aNHY/78+bhy5QoWLlwIKysrfPLJJ/D19TV6bUEQ8L///c9Q7mPgwIFYsGCBYXROp06dEBAQgK1bt2LIkCFwdXWFTCZDcHCw4RweHh6GovvBwcFwcHDAtGnTDEkiYzp06ID58+cDyE58/fzzzzh79iy6d++OmTNnPjOGhQsXYtGiRRg5ciSA7BFZNWvWzPdaSrklNrUfieBd30Cj0+K2Og7jT/4Pv7UfwZWSiUysSEkie3t7bNmyJU9CaMuWLXB0dCyRwIiIiIhKy5AhQ3Dp0qXntrt48SICAwNLPyAiMitz5sxBUlISTp06hVmzZqFevXro3LkzFAoF4uPjc7VNS0sz1DcLDg7GnTt3EB4ejtq1a2PTpk2wsbEx1DH68ssvER0djb59++L69etGr+3h4WFIEGVkZCAsLAyLFy/GTz/9ZGjz4MED3Lx5M9/4Y2JisGTJEly8eBGJiYlIS0t7bp21OnXq5Hrs6OiIpKSk58aQnp6OBw8eoGnTpoZ9NWrUeO77wkCVO75u3gevn/oDALDp3iV0vFELr9Zt8czjiKh0FSlJ9NFHH2HcuHHYtWsXmjVrBlEUce7cOWzZsgXLly8v6RiJiIiIStSOHTug0WiM7tu9ezc++eQTZGRkwM7OrowjIyJz0KhRIwBA+/btce3aNSxfvhydO3eGr68vfv7551xtr1+/nmtUkFKpRIMGDRATE4N58+bh+PHjuHLlCmrUqIGXX34ZALBgwQKjo5IA5CpCrVAoYGtri0mTJiEgICBXu5yRQsZG3vTp0wfe3t6YNGkSHB0dce3aNbz66qtFei2eF4OlpSWsrKxyJc+ysrKQkpLy3HNP9G2Bw1Fh+N+9ywCAt89sQ7BrdQQ4VStSrERUfJKiHDRu3DgcOnQIOp0Oq1atws8//wydTocjR45g9OjRJR0jERERUYny9PRErVq1cn3Fx8dj/PjxmDZtGgYNGoSwsDDUqFHD1KESURn6448/cOvWLcPj6OhohISEGEbZDBgwAGFhYdi+fTuA7Fo+mzdvxtChQ/Oc6/XXX8fcuXPh7OwMFxcXREREICMjA9HR0dBoNAVKQguCgAEDBmD37t3w9/dHcHAwgoODce/ePSQnJwPIXoU6Z7pbjitXrmD06NHo2bMnmjdvjlOnThX5NXleDIIgoHv37vjyyy8NdY6++uorZGVlFejcP7UahBpKFQBAo9Ni8OG1UGdlFDleIiqeIiWJAKBNmzbYvHkzrl27hmvXrmHz5s1o1apVScZGREREVOrCwsIwePBgBAcHw8XFBVevXsX333/Plc2IKiFvb2+89NJLqFGjBvz8/FCzZk00bdoUH3zwAQCgZs2a+PHHHzFs2DDUrVsX/v7+GD58eJ4k0caNGyEIAgYNGgQAqFWrFrp3745atWqhYcOGmDlzJiwsLAoU0+LFi2FjY4Nq1aohICAATk5O2LFjB9zc3ABk1yzau3cvAgIC0Lp19lLy7733Hvr374/AwEB4enoaEkpF9bwYvv76a9y+fRseHh6oVasWTp48aXSUlDH2FlbY2H4E5BIpAOBm8mO89tcfEEWxWDETUdEIYiX87UtOToa9vT2SkpI4jJyIiKicK+p9/dGjR5g7dy6WL1+O1q1bY8GCBWjWrFkpRmq+2Dciyu3+/ftISUmBl5cXlEplnv1qtRphYWGoVq2a0YTytWvXUK1aNdjb2+fafvv2bSgUCsNUsafFx8cjIiIiz7SunGveu3cP1atXz/N7mpKSgtu3byMjIwNBQUEAsv/GPXjwANWrV4e1tTUuX75s2Hfv3j2IoggfHx8AwJ07dyCVSlG9enXDOS9fvgx3d3eoVKoCxSCKIsLCwqBUKlG1alVcvHgRNWrUyPMa5Gfp1eN4O2Sb4fHKVoMxpk7zAh1LRLkV575e4CRR1apVC3zS6OjoQgVR1tgRIiIiqjiKcl//+uuvMXv2bNSsWRPz589H9+7dSzlK88a+ERGZmiiKePHQGmwNDwUAWEnlONv7bTRwLPj7UCLKViZJonXr1hX4pCNGjChUEGWNHSEiIqKKoyj39cDAQFy+fBl+fn6QSPKffb9x48Z8l6muSNg3IiJzkKBJQ6PtX+N+SgIAoL5DFZzp9RZs5AoTR0ZUvhTnvl7g1c3MPfFDREREVFBjxoxBZGTkc9sVpmN18eJFfPrppwgLC4O3tzdmzJiBFi3yX8pZFEWsXbsWv/76K6KiolCvXj3MmjUL9evXN7QZPnw4zp8/n+fYwMBA/PbbbwCyE1kfffRRnjZXrlyBVCotcPxERKbmqLDGxvYj0HrXd9CKelxNjMGbIVuxqvUQU4dGVGkUOEmUH7VaDVEU+akTERERlRtvvfVWiZ7v1q1baNu2LV555RVMmzYNf/zxBzp27IhTp04hMDDQ6DFz587F4sWLsXTpUtSvXx9bt25Fq1atcPHiRXh7ewMA5s+fj7S0NMMxarUarVq1wrBhwwzbEhISkJ6ejj///DPX+ZkgIqLyKMilOj5v2gPTzu4EAPx86yw6VK2FkbWamDgyosqhSKubiaKIb775Bh4eHrCzs4O9vT08PDzwzTffsAo9ERERVTpffPEFatSogW+//RYtWrTAF198gcaNG2P+/Pn5HvPNN99g+vTpGDlyJJo0aYJPPvkE/v7+WLhwoaGNl5cX6tata/j6+++/odfrMWbMmFznksvludrVrVu31J4rEVFpm9ygLXp61DM8fu3UZlxPfGTCiIgqjyIliebPn4/Zs2dj4sSJ2L9/Pw4cOICJEydi9uzZz+wMEREREVVEhw8fRrdu3XJt69mzJw4fPmy0vV6vR0pKSq5VgwBApVLh4MGD+V5n5cqV6NatW56VkaKjoxEUFISWLVti0qRJBZpKR0R5Xb58GcHBwZg2bVqu7ceOHUOXLl3ytAsODkarVq3Qo0cPTJ06FX/99ZfR8/75558YM2YM2rdvjwEDBmDBggVQq9W52hw4cADjxo1Dhw4d0Lt3b0yfPh03btwwer4rV67g/fffR7du3dCxY0eMHTsWO3fuNNr24sWLmDBhAjp16oRx48bh77//fuZrEBMTgzfffBPt2rXD8OHDn9v+8uXLGDNmDDp06ICxY8fi1q1bufZfunQJY8eORYcOHTB48GDs2rXLsC8+Pt7wOgYHB2Pt2rUAAIkgwZo2Q+Fhnb0yWqo2E4OPrEW6NuuZsRBR8RVputmyZcuwceNGdO3a1bCtU6dOaNasGcaPH4+ZM2eWWIBERERE5i4iIgJubm65tlWtWhWPHz9GZmYmLCwscu2TSCTo0KEDli9fjoEDB0KlUuH8+fPYt29fvqOyr1y5gpCQEGzdujXXdrlcjtdffx19+vRBRkYGvvjiCzRs2BD//PNPnphyaDQaaDQaw+Pk5GQA2ckrvV5f2KdPVGEkJiYiJCQEly5dwuDBg9G0aVMAQGxsLM6ePWv4/chpd+DAAVhaWiIhIQEnT55E165dMWrUKCxdutRwznfeeQerVq3C1KlTMWDAAGg0Gpw9exY9e/bEkSNHcrWZPHkyevfuDZlMhuvXr2PAgAFYuXIlmjVrBgDQ6XR49913sWnTJowePRqvvvoq7O3tcfPmTSxcuBCLFi3C77//DicnJwDAhQsXMHXqVLzyyisYPHgw9uzZg5YtW+LMmTO56p/lyMzMRMeOHVGjRg28++67OHToENq0aYPz58+jVq1aedr/888/CAoKwtSpU/HSSy9h586daNmyJS5duoSqVavi/v37aN26NcaMGYMPP/wQoaGhePHFF7Fp0yb07t0bNjY2+OqrrwAAo0aNwoMHDwyvsaOFFda3HYaOf/4InajHPwlReDtkK5a1GFBC/9tEFVdx7uVFShJFR0cjODg4z/bg4GDExMQUORgiIiKi8kin0+VJBCkU2avxaLXaPPsAYMWKFRg1ahQ8PDzg4uICuVyOl156yfBJurH2bm5u6NmzZ67to0aNylV/qHXr1vD19cWiRYvw5ZdfGj3X/PnzMWfOnDzbExISoNVqn/1kiSqwnITpmDFj8M4772D79u0AgJSUFIiiiPj4+FztatasCVtbWwDZ74WaNGmCQYMGoVWrVujSpQv27duHb7/9Fnv27EGTJv/V1Gnbti0mTpyI+Ph47Nu3D9999x12796dq01QUBBGjhyJjIwMw3VnzZqF8PBw/PXXX7CxsTG0bdiwIQYOHIhly5ahV69ehlFFVatWxebNmw3tAgMDsX37dmzZsgVVq+ZdWn7Lli24f/8+9u7dCysrKwQFBeHUqVNYuHAhPv/88zztly5diqCgIEyePBkA0KhRIxw5cgSLFi3CzJkz8eeff8LS0hKzZs0CAAQEBGDfvn2GGmwAUKdOHQCAhYUF0tLSDM8VAOrL7THDtw3mXT8KAPjpZgia2VZBf/e8CS4i+s/TIxULo0hJojp16mDjxo0YP358ru2//fYbateuXeRgiIiIiMojlUqF2NjYXNtiY2NhZWUFa2tro8e4u7tj//79SElJQWxsLLy8vDB58mR4enrmaZuZmYl169bh1VdfhUyWu/v2dIFqS0tLNG/eHFeuXMk33vfffx9TpkwxPE5OToanpyccHR25GAlVajk//5988gnq1KmDY8eOoV+/frC1tYUgCIYROjntnJycDEkiAHjxxRcRGBiIgwcPYsiQIdi9ezfatWuXa6pajpxzPavNk65evYojR47g3LlzsLKywvLly/HHH3/A2dkZgwYNwqlTp/D555/j9OnTOHXqVJ6EMgCEh4cjJiYGzZs3N1z/SaGhoWjRogXc3d0N27p27Ypdu3YZbZ+WlgYPD49c+9zd3XHx4kU4OTmhRYsWUKvViI2NRZ06dZCSkoJbt26hb9++ec4nk8lgbW2dZ/vHzbvjbHIU9kXeBABMubwXbav7oradyzNfL6LK7Om+QqGOLcpBH3/8MYYOHYpdu3ahefPmAICQkBDs2rXLsBwrERERkbnT6/VYuXIldu3ahZiYmDxTvdauXVugD8CaNWuG06dP59r2119/5RoVkB9bW1vY2tpCp9Nh586d6N69e54227ZtQ1xcHMaOHfvc8wHZbwRzVkgzRqFQGEY6PUkikUAiKVLJSqIKIefn38nJCbNnz8aMGTPQu3dvw3Zj/z79O1OrVi1ERkZCIpEgPDwcdevWfebvlbE2/fv3R1RUFABg4sSJGDVqFP744w+8+uqrsLGxwfr16/HVV19h4cKFEAQBs2fPho+PDyQSCXr27IkzZ86gd+/ehvP16dMHERERCAsLw2effYYXXnjBaCxxcXFwcXHJFYurqytiY2ONPodWrVph7ty5iIyMhIeHB27evInjx4/D29sbEokEAQEBWLNmDVq1agVPT0+Eh4fj1VdfxYQJE4xeXxCEPNeRQIK1bV9C4LavEZWejBRtJl46ugF/9ZwES5k839eVqDIrzr28SEcOHDgQJ0+ehEwmw9q1a7Fu3TrI5XKcPHkSAwcOLHIwRERERGVp8eLFmDZtmmEaV69evXJ9FXRUzauvvooDBw5g//79ALITRNu3b8fEiRMNbdavX4+6detCp9MBAPbt24eTJ08CyB4pNHXqVCQmJmLGjBl5zr9y5Up07twZPj4+efbNmTMHERERALJXoP36669x5swZvPzyy4V7MYgol9deew0A8MMPPxTquLi4OMNUMBsbm1zTp4yxtbXN02b27NlYvHgxUlJS8ODBAwDA7du30aBBAwDA3r17MWPGDPTr1w99+/bF1KlTDcempqbmGWE4Z84cfP3115g5cyZmzZqFAwcOGI1FoVAgPT0917a0tDSjSWUgO4HVpUsX+Pr6omHDhujatSvatm1reP4XL17EpEmTMGXKFCxevBgLFy7Ezz//jHXr1j3zNXmaq5USG9oNg0QQss8b/xDTzu4o1DmIqGAKPJJoxowZhnmof//9N4KCgvD777+XWmBE5kwURWjjIqDTpECqsIVM5Qnh35sWERGVHzt27MAPP/yAYcOGFes8vXr1wrx58wzTUpKTkzF9+nQMHz7c0CYhIQE3btwwjFaqX78+XnnlFVy9ehWpqamoX78+Dh8+nGflsoiICOzfvz/f0dp+fn7o3LkzkpOTkZqaCpVKhQ0bNhidakJEBSeXy7Fw4UKMGzcOixYtKtAxcXFxuHjxomEhnzZt2uCbb75BampqrhpCT2rVqhW+++67XG0aNWoEALmmsimVSkMtJDc3N5w5cwZjxowBAMNIxoiICPz444/4448/cl0j53zt27fHtWvXsHz5cnTu3DlPLL6+vvj5559zbbt+/Tp8fX2Nxi6TybB+/XrExsbi4cOHqFOnDvr06WOYbbJixQo0adIEH3zwgeH6UVFR+PrrrzFixAij58xPe7da+CiwCz66uA8A8N31v9DBrRYGeDcs1HmI6NkKPJJo4cKFhk5Nzh8ZospIq45D4rFViFw1DlErxyJy1TgkHlsJrTrO1KEREVEhWVtb50nKFNX06dMRFxeH06dPIy4uDnPnzs21f8SIEbh27ZqhToCHhwcOHjyIy5cv486dOzh9+jQaNsz7ZsfBwQFXrlxB//79jV53wIABuHHjBi5duoTw8HDcvXsXL730Uok8J6LKrl+/fmjQoAEWLFjw3LZRUVEYOXIkbG1tMW7cOADZo5HkcjleeeUVxMX911eMiYkxnPP111+HTCbDK6+8gsePHxvapKWlISUlxfC4VatWhkLaU6ZMwdGjR1GjRg3UqlUL0dHROHToELp27YrFixfD398fAPDHH3/kWpI+OjoaISEhhmLRTxswYADCwsIM17l9+zY2b96MoUOHGp5jcHCwoeZZQkICNm/eDGdnZwQEBOD333/HyZMnMWnSJADZ9YmuXr1qqNmWmZmJU6dOFfnv7gcNO6Oj23+rrI09uQl32AcnKlEFThJ5eHhgw4YNuHv3LgDg3r17+X4RVVSiKEJ9YSsSj66APiO7Yrw+Q43EoyuhvrA132WLiYjIPPXp0werV68usb/flpaW8PHxMVqs2sHBAXXr1s2z3cXFBc7OzvmeU6lUom7dus8tQunq6goHB4dCx0xEz/bll1/ixo0bRvd17NgRQUFB8PX1Ra1atWBra4sTJ07A0dERAODo6Ihjx44hNTUV1apVQ926deHj44OmTZvCxcXF0Ob48ePIyMiAh4cHateujYYNG6JatWqoX78+Bg0aBCC7KPZff/2Fffv2oWrVqvjnn3+wa9cunDt3DmvXrsXVq1dx7dq1XKMIvb298dJLL6FGjRrw8/NDzZo10bRpU8PInqfVrFkTP/74I4YNG4a6devC398fw4cPNySJNBoNQkJCDCsn2djYYPPmzfDx8YGHhwdmzpyJrVu3GpJQb7/9NgICAuDt7Y1GjRrB3d0dDx8+zLXyYs+ePREcHIxbt27h+++/R3BwMBYvXmw0PqlEgvVth8HVMnuEVVJmBoYeWYdMHVdlJCopgljAXtEvv/yCiRMn5pmjaoy5v1FOTk6Gvb09kpKSuIIHFUpWbDgiV40zJIieJLFUotrYFZCrvEwQGRFR5VWc+/qsWbOwcOFC1K5dG4GBgXkSMXPnzoWXV8X/u86+EVE2tVqNK1euIDg4ONf2v//+G1qtFk2bNs3VDsheYdDOzg41atSAXJ5/IeWEhATcu3cPKpUKnp7GSxUkJSXh7t27sLGxQfXq1WFhYZEnjl69euHNN9/E66+/DqVSCQBIT0/H999/D39/f3Tt2jXPee/fv4+UlBR4eXkZjnne6xAWFoZq1aqhSpUqhu0ajQYXL16En59frqlwDx48QHJycr5Fuh8/foyHDx8anvuTLly4gMzMzFzb3NzcUL169XzjOxB5E13//Akist93vlO/Db4O6vvc50VUWRTnvl7gJBGQPTwwMjISPj4+uYYtPq1WrVr57jMH7AhRUWU8vIqolfmvLOM2diUs3euXYURERFSc+/oHH3yA+/fv57v/s88+Y5KIiMxKeHg43n//fezYsQNeXl7QarW4e/cuWrdujdmzZ6Ndu3amDrFMzLqwF59e+q8A97ZOo9HHq4EJIyIyH8W5rxe4cDUAWFhYwNvbG7/++qvZJ4KISoNUYQuJpTLfkURSS1sjRxERkbmaN2+eqUMgIioULy8vrF+/HhkZGQgLC4NUKoW3tzesrKxMHVqZ+iiwC45F38GxmDsAgFHHf8PFvpNR3dbJxJERlW8Frkn0pJw5qTl69epVIsEQmTuZyhN2QUOM7rMLGgKZk6fRfUREREREJcnS0hJ+fn6oV69epUsQAYBMIsWGdsPhrMheES4hMx1Dj6xHll5n4siIyrdCjSTKz65du0riNERmTxAEKBv3AwAkh2yEPkMNiaUSdkFDoGzS3+jcciIiMm86nQ4nT57EnTt3kJGRkWvfoEGDoFKpTBQZERE9i7uNPda2fQnd968AAJx+fB8fnt+DBc04iIGoqEokSURUmciUKji0HQNbvy7QZaRAamkLmZPx4oNERGTeYmNj0bFjR9y7dw8ajQZOTk54/PgxdDodqlWrhnbt2jFJRERkxrp51MV7/h2w4J/DAICFoUfQrmpN9PCsZ+LIiMqnAk8327lzZ777pFJpiQRDVF4IggC5yguW7vUhV3kxQUREVE7NmzcPtWrVQlxcHOrVq4c9e/YgPj4eo0aNQvPmzVGvHt9kEBGZu08ad0NLV2/D45eP/4oHqYkmi4eoPCtwkqh3796G759c7hAAtFptyUVEREREVEYuXLiASZMmQS6XQyKRIDMzE3Z2dvjpp59w5MgRxMXFmTpEIiJ6DrlEil/bDYejRXZtpjhNGl46uh5a1iciKrQCJ4lUKhVu3rwJAEhNTS21gIiIiIjKSlJSEpycslfCcXV1xYMHDwAAMpkMjo6OePz4sSnDIyKiAvKydcSaNv8tsHQi5i4+vrjPhBERlU8Frkn0yiuvwM/PDy4uLgAADw+PfNvmdLCIiIiIyovWrVtj4cKFUKlUOH36NB4/fozq1aubOiwiIiqg3l4NMKVBW3x15RgA4LPLh9Cuak10ca9j4siIyo8CJ4m+/PJLDBo0CGFhYRg5ciQ+/fTT0oyLiIiIqNRNnDgRVatWBQC89dZbOHPmDDp37gwHBwesXLmyUi4rTURUns1v0gMnYu7iTGwERIgYfmw9LvWdCjdrO1OHRlQuCKIoioU9aNKkSfj2229LI54ykZycDHt7eyQlJcHOjn8siIiIyrOSvq9nZWVBLpeXQGTlB/tGRFSR3FXHodH2r5GUmQEA6FC1Jva/8CqkkgJXWyEq14pzXy/Sb0l5ThARERERPUtlSxAREVU0PkoVVrUaYnh8OPo2Pr10wIQREZUfRU6lnj9/HkOHDkVAQAAaNmyIoUOH4vz58yUZGxEREVGp27JlC4KCgmBvb4/Lly8DyP5A7NixYyaOjIiIiupFb39MqtfK8HjO3/txOCrMhBERlQ9FShJt2bIFzZs3R2JiIvr3748BAwYgMTERzZs3x5YtW0o6RiIiIqJSsX//fowZMwYDBw6Ek5MT9Ho9AKBNmzaYOnWqiaMjIqLiWNSsNxqr3AEAIkQMO7oej9LVJo6KyLwVKUk0e/ZsLFmyBHv37sXHH3+Mjz76CHv37sWSJUswe/bsko6RiIiIqFT8+OOP+OKLLzB9+nQ4ODgYtgcEBODevXt4+PCh6YIjIqJiUUhl2Nh+JJRyBQAgOl2Nkcd+hV7UmzgyIvNVpCTRjRs38PLLL+fZPnLkSNy8ebPYQRERERGVhYiICDRo0AAAIAhCrn1WVlZQq/mJMxFReVbLzhkrWg0yPN4XeROfXz5swoiIzFuRkkRVqlTBuXPn8mw/e/YsXF1dix0UERERUVnw8fEx9GmeTBKFhIQgNjYW3t7eJoqMiIhKymCfQLzqG2x4POviXhyPvmPCiIjMl6woB02YMAFDhgzBe++9h+bNmwPI7kwtWLAAb7/9dokGSERERFRa3n77bfTq1QuiKCIlJQUXLlzAvn37sGDBArzxxhuwtLQ0dYhERFQCvm7eF6ce3cflhCjoRREvHV2Pv/tOgbOljalDIzIrgiiKYmEP0uv1WLx4MRYuXIiYmBgA2aOL3n33XbzzzjuQSIq8aFqZSE5Ohr29PZKSkmBnZ2fqcIiIiKgYintf37NnD6ZNm4arV68CAGxtbfH6669j3rx5kMmK9HlaucO+ERFVBjeSHqHJ9sVI1WYCAHp41MWOzmMgEcz7/StRYRXnvl6kJNGT4uLiIAgCnJycinOaMsWOEBERUcVRUvf1uLg4pKamolq1apUmOZSDfSMiqizW3T6Pkcd+NTxe2LQnpvt3MGFERCWvOPf1YqdMVSoVi1UTERFRuadSqeDl5VXpEkRERJXJiJpNMKZ2c8Pjmef34NSje6YLiMjMlEgvqEWL/7N311FVbG8fwL/n0N2CdCoICCgiGCBid2N3Xa/dcb22Xq/d3d1iNypcDFRAUSREASUE6Y6z3z94mZ/HQ0kaz2ct13L2nnhmhnNmzjN79nZCJRskEUIIIYTUGHd3dwQHB5c536lTp1C/fv0aiIgQQkhN2eLYA4/jI/AmOQ75TID+94/Cr/t0qErJ1nZohNQ6elRGCCGEkN9OcHAwoqKi0KtXLygpKZU4n4KCQg1GRQghpCbIikvidKshaHJ5E7IK8hCZkYwRXqdw0W240EiXhPyOKElECCGEkN/OggULsHnzZhw/fhy9evXCqFGj4OLiQj8OCCHkN2GpooVtTj0x0vs0AOBS1GtseuOFqZbOtRwZIbWrSrpxX7VqVVWshhBCCCGkRvTt2xdeXl548eIFtLS04O7uDjMzM6xcuRKfPn2q7fAIIYTUgOGmTTDEpDE3PfvZVfjGR9ZiRITUvgolib59yjZ37twS6wghhBBCflT169fHmjVr8PHjR6xevRp3796FgYEBgoKCajs0Qggh1YzH42G7Uy/UV9IAAOQJCuB+/yiSc7JqOTJCak+VtCQqkpubCykpqapcJSGEEEJItQsMDMT9+/fh5+cHCwsL6ouIEEJ+E/ISUjjdagikxQp7YnmfnojR/52mgZnIb+u7+iTauXNnsf8HAIFAgKdPn8Lc3LxqIiOEEEIIqUZJSUk4fvw49u3bh7CwMPTv3x/Xr19H06ZNazs0QgghNaihqjY2Ne2BcT5nAQDnIl5hx1sfTLBoXsuREVLzvitJtHbt2mL/DwASEhIwNDTE7t27qyYyQgghhJBqMm3aNOzcuRNNmjTB5MmT0a9fP8jK0tDHhBDyuxpTryk8Y8Jw8r0/AGDa00twqmMIOzWd2g2MkBrGYxVoR+fo6IjHjx9XRzw1IjU1FUpKSkhJSYGiomJth0MIIYSQSqjIdd3W1haRkZHQ19cvdb5Tp06hfv36VRHmD43ujQghBEjNzUbjSxsRlpYAADBVUMfzblOhKCldy5ER8n0qc13/rpZERX7mBBEhhBBCyMiRIxEdHV3mfJQwIYSQ34eipDROuw6B45XNyBUUICwtAeN8zuK4yyAaoIn8NiqUJPr2VbNvzZw5s0LBEEIIIYTUhMmTJ9d2CIQQQn5Admo6WO/QDRMfXwAAnHzvj9Z1TTGmviMYY5QsIr+8Cr1uZmtrKzQtEAgQERGB1NRUWFlZ4dWrV1UVX7WgJtWEEELIr4Ou65VHx5AQQv6HMYY+nodxPqLwd60YjwdlSRl8ycmEprQ8xtZ3xCzrVlCQoNfQyI+pxl838/f3FynLzc3F+PHjYWpqWpFVEkIIIYQQQgghtY7H42Ff8354nvARERlJKGAMX3IyAQBx2elY8fIuLkW9gVenCZQoIr8cflWtSFJSEqtWrcL+/furapWEEEIIIYQQQkiNU5aSQRtts2LrBIzhVVIM1gY+qOGoCKl+FWpJVJKCggLExcVV5SoJIYQQQgghhJAadyXqTYl1Asbwz8t7kOCJwVnLCA7q+pAWl6jB6AipHhVKEl25ckWkLCkpCTt27ECLFi0qHRQhhBBCCCGEEFJbGGOIy04vdZ5cQQEW+t0AAEiJiaOpuj6ctYzhomUMJw0DyElI1USohFSpCiWJ+vTpI1KmoqKCli1bYsOGDZUOihBCCCHkZ8MYg7e3N8LCwmBoaAgXFxfw+aW/2Z+ZmQkvLy/ExMTAwsICTZs2Far38PBATEyMyHJ16tRBr169KrVtQgghJePxeNCUli8zUVQkpyAfD+PC8TAuHMsDAHEeH43VdeGiaQxnLWO00DSCkqRMNUdNSOVVKEmUnZ1dZQHs3r0bmzZtQlxcHKytrbF27Vo0bty4xPm1tLSQni76QR03bhzWrVtXZXERQgghhJRXXl4eevXqhWfPnsHZ2Rk+Pj4wNTXFtWvXICNT/I+CFy9eoFu3btDS0oKFhQUWLlyIZs2a4fjx4xATEwMAhIaGIiwsTGi5/fv3o2fPnlySqCLbJoQQUrax9R2x4uVdCIoZEJwPHtrr1IeChBQexIUjLitNqD6fCfAkPhJP4iPxb+B98MCDrao219KopaYx1KXlampXCCm3SvVJlJWVBcYYZGVlK7T8sWPHMHnyZBw8eBBOTk5Ys2YN3Nzc8ObNG2hraxe7zLt378C++pB6e3ujY8eO6NSpU4ViIIQQQgiprF27dsHLywuvXr2Cnp4e4uLi0LBhQ6xfvx4LFiwodpkxY8bA0dERZ86cAY/HQ3JyMiwtLbFv3z6MHTsWADBz5kyhZXx8fLBr1y6MHj26UtsmhBBStlnWrXAp6g0Ck2JQ8NVvUDEeD1YqdXHKdTAUJKTBGENoagIexL7Dw7hwPIgNR1RGstC6GBj8Ej/BL/ETNr3xAgA0UNaEi5YxnP+/tZG2rFJN7h4hxeIxVkxatBTp6elYtmwZjh07hk+fPgEAdHV1MXDgQCxcuBDy8vLlXpeVlRVatmyJHTt2AAAEAgF0dHQwevRoLFu2rFzrGD58OLy8vBAWFgYej1euZVJTU6GkpISUlBQoKiqWO15CCCGE/Hh+hOt6ixYtYGxsjMOHD3NlEydOxIMHD/Dq1SuR+QsKCiAlJYXdu3dj5MiRXHnfvn3x+fNnPHhQ/Ig5o0aNgqenJ969e8fd93zvtovzIxxDQgj5EaXlZWNt4APsDn6M2Kw0aMkoYGx9R8y0coGChHSJy31ISyx8/Sy2MGkUlpZQ5rZMFdS5lkbOmsYwVFCtyl0hv5HKXNe/qyVRamoqnJ2d8e7dOwwaNAj169cHAAQHB2P79u24efMmvLy8oKCgUOa6kpOT8fr1ayxevJgr4/P5aN26Nby9vcsdz5kzZ/DXX3+VO0FECCGEEFLVXr9+jS5dugiVNWjQALt27YJAIBDpH0hMTAw6OjoICAjgygoKCvD69WvExsYWu4309HScPn0ac+fOFbrv+d5tA0BOTg5ycnK46dTUVACFD+wEAkE595oQQn59cmKSWGTTFots2oIxJvT9W9r3pb6cMgYbN8Jg40YAgOjMFHjFvcfDuPfwigvH62TRUcHD0hIQlpaA/aFPuXW01DRGS00juGgaw0xRnX73knKpzLX8u5JEK1asgKSkJMLDw6GhoSFUt2zZMnTq1AkrVqzAP//8U+a6oqOjARR2vPi1OnXq4Pnz5+WK5+TJk8jNzcWIESNKnY9uhAghhJBf149wLU9NTYWysrJQmYqKCvLz85GZmVlsS+tVq1Zh+PDhyMzMhIWFBa5evYr8/HzuPuVbp06dQlZWlsh9T0W3vWTJEpHypKQk5Ofnl7G3hBBCvpc0gLZK+mirpA/Uc8GXnEw8TozCoy9R8PkShcDUOHz7ik9kRjKOhb/AsfAXAAANKTk4qeqhmZoenNT0YK6gAT4ljUgx0tLSyp6pBN+VJDpz5gxOnjwpkiACAA0NDWzduhUDBw4sV5KoyLdPt/h8Psr7Bty+ffvQtWtXaGlplTof3QgRQgghv67K3AhVFRkZGZE4UlNTwePxIC1d/OsIAwcOhLW1NTw8PBAZGYmJEyciMDAQGzduLHb+ffv2oXPnziL9NlZk2/PmzcP06dOF5tfT04OKigq9bkYIITVAFaowq6uLIXACAKTkZuG/zx+4lkbPEj4inwk/BInPycClmLe4FPO2cB1SsmhRxwjOmkZw1jKGjUpdiPPFyh3Dty2jyK9DXLzi3U9/15IfP36Era1tifW2traIiooq17qKWhDFx8cLlcfHx4u0LipOYGAgnj59imvXrpU5L90IEUIIIb+uytwIVRUzMzO8f/9eqOz9+/cwNDQsNT5ra2tYW1tz03v27Cl2lNegoCA8evQIly9frpJtS0lJQUpKSqScz+cX+3oaIYSQ6qUiLYcu+pboom8JAMjIy8Gj+Ag8jA3Hw7hwPI6PRE6BcAOHxJxMXIp6jUtRrwEAChJSaF7HEC5aJnDWNIa9ui4kxYSvA6m52VgbeB+7gx8jLjsdmtLyGFvfEbOsW5XaxxL5uVTmWv5dd1VKSkqIjo6GoaFhsfXR0dEizZ1Loq6uDlNTUzx8+BA9e/bkyh88eIB+/fqVufzevXuhp6eH9u3blzkv3QgRQgghv64f4VrerVs37Nu3D2vWrIGcnBxycnJw9uxZdOvWjZvn9evX8PLywrhx48Dj8RAdHQ0tLS0u/mfPnuHWrVs4e/asyPr37t0LHR0ddOzYsULbJoQQ8nORk5BCG+16aKNdDwCQnZ8H34QobvQ0n88fkJGfK7RMWl4ObnwKxo1PwQAAGTEJONUxgLNmYWfYFkqaaHdrNwKTYyH4/7d34rLTseLlXVyKegOvThMoUUS+b3Szfv36QVFREXv37i22ftSoUUhPT8epU6fKtb5t27Zh3rx5uHz5Mpo2bYo1a9Zg5cqVCAwMhImJCYDC0TmePn2Kp0+fcsvl5uZCW1sbkyZNwqJFi8obPodG8CCEEEJ+HT/CdT0lJQWOjo5QVFREjx49cP36dURGRuLp06dcC+mtW7di0qRJyMvLg7i4OG7cuIFly5ahW7duSExM5Ia2X7t2rdC68/LyoKOjg/Hjx2Pp0qUV2nZZfoRjSAghpPzyBAXw+/IJD2Lf4WFcOLzi3iMlN7vUZfjgQSDS81EhMR4PC2zaYIld2Y0wyI+vxkY3W7RoEZo2bYqwsDBMnToV5ubmAIC3b99i48aNePbsmVAypyx//vknvnz5gp49eyIlJQVmZma4dOkSlyACgOzsbGRmZgotd/HiRSQlJQkNGUsIIYQQUluUlJTw9OlTHDhwAO/evUOPHj0wYsQIqKiocPNYWVlh3LhxXMuhDh06QFNTE2fPnoWEhASuX78OJycnkXV/+PABvXr1wpgxYyq8bUIIIb8WCb4YHDT04aChj1nWrigQCPAqKYZrafQwNhwJORlCy5SUIAKAAsawO/gxJYnI97UkAoDHjx9j5MiRCAoKEipv0KAB9u3bB0dHxwoFkpeXBwkJCZHynJwcCAQCyMjIcGW5ubnIz8+HrKxshbZFT8sIIYSQXwdd1yuPjiEhhPxaGGN4m/L5/1savcf9mDDEZJU90INg+BrqzPoXUGMtiQDA0dERr1+/hr+/P0JCQgAA9erVg62tbaX+mIpLEAEoti8hSUlJSEpKVnhbhBBCCCGEEELIr4rH48FCWRMWypoYb94MjDHUObFYpHXR17RkFChBRL4/SQQU/sHZ2dnBzs6uquMhhBBCCCGEEEJIFeLxePjD3AkrXt7lOq3+Vl/DhjUcFfkR1f5wIIQQQgghhBBCCKlWs6xbwVqlLsRKaC10KfINPmWk1HBU5EdDSSJCCCGEEEIIIeQXpyAhDa9OE7DApg20ZBQAAMqSMihKGUVkJKHNzV34XI6+i8iv67s7rv4VUOeMhBBCyK+DruuVR8eQEEJ+P4wx8Hg8HH/3AoMfngD7/9HPbFW14dnhDyhLyZSxBvKjqsx1nVoSEUIIIYQQQgghv5miTqoHmjTCzma9uXL/xGh0ur0X6Xk5tRUaqUWUJCKEEEIIIYQQQn5jY+s7Ym2TLtz0o/gIdL97ANn5ebUYFakNlCQihBBCCCGEEEJ+czOsWmGRbVtu+l5MGPrdP4I8QUEtRkVqGiWJCCGEEEIIIYQQgkW27TDd0pmbvhz1BkMenkCBQFCLUZGaREkiQgghhBBCCCGEgMfjYW2TrhhTrylXduq9P8b5nMVvOObVb4mSRIQQQgghhBBCCAFQmCja4dQbA43tuLJ9oU8x/eklShT9BihJRAghhBBCCCGEEI4Yn4+DLfuju74lV7bxjRcW+d2sxahITaAkESGEEEIIIYQQQoRI8MVw0mUw2mibcWXLAu5gzSvPWoyKVDdKEhFCCCGEEEIIIUSEtLgELrYejuZ1DLmy2c+uYsdbn9oLilQrShIRQgghhBBCCCGkWHISUrjadhQaqelwZRMenceRsOe1GBWpLpQkIoQQQgghhBBCSImUJGVwo90YWCjV4cpGeJ/ChYhXtRgVqQ6UJCKEEEIIIYQQQkipNKTlcafDOBgrqAEACpgA7veP4uan4FqOjFQlShIRQgghhBBCCCGkTNqySrjbfhx0ZJUAAHmCAvS8exAPY9/VcmSkqlCSiBBCCCGEEEIIIeViqKCKO+3HQUNaDgCQVZCHLnf241lCVC1HRqoCJYkIIYQQQgghhBBSbubKdXCr3VgoS8oAANLyctD+1h4EJsXUcmSksihJRAghhBBCCCGEkO9iq6aD621HQ05cEgCQmJOJtjd3IzQlvpYjI5VBSSJCCCGEEEIIIYR8N8c6BrjkNgJSYuIAgNisNLS5uQuR6Um1HBmpKEoSEUIIIYQQQgghpEJaa5vhrOtQiPMK0wuRGcloc3MX4rLSajkyUhGUJCKEEEIIIYQQQkiFddFrgKPOA8Hn8QAAoakJaHtzNxJzMms5MvK9KElECCGEEEIIIYSQSnE3tsWeZn256VdJMeh4aw/S8rJrMSryvShJRAghhBBCCCGEkEobWc8Bm5p256afJkSh6539yMzPrcWoyPegJBEhhBBCCCGEEEKqxOQGLbG8UQdu+kFsOPrcO4zcgvxajIqUFyWJCCGEEEIIIYQQUmXmN3TDHGtXbvr6p7cY+OAY8gUFtRgVKQ9KEhFCCCGEEEIIIaTK8Hg8rGrcCRPMm3Fl5yJeYfR/ZyBgglqMjJSFkkSEEEIIIYQQQgipUjweD1sce2CoSWOu7FDYM0x+fBGMsVqMjJSGkkSEEEIIIYQQQgipcnweH/ta9ENvA2uubNtbH8x/fr0WoyKloSQRIYQQQgghhBBCqoU4XwzHXAahg059ruyfV/ew6uXdWoyKlISSRIQQQgghhBBCCKk2UmLiONd6GJw1jbmy+c+vY8sb71qMihSHkkSEEEIIIYQQQgipVrLikrjcZiSaqOtxZZOfXMSB0Ke1GBX5FiWJCCGEEEIIIYQQUu0UJaVxve1oWClrcWWj/zuDM+8DajEq8jVKEhFCCCGEEEIIIaRGqEnL4Xb7sTBVUAcACBjDwAfHcDXqTS1HRgBKEhFCCCGEEEIIIaQGackq4k6HsdCTUwYA5DMBensehmdMWO0GRihJRAghhBBCCCGEkJplIK+Ku+3HQVNGAQCQU5CPbncO4El8RC1H9nujJBEhhBBCCCGEEEJqnJmSBm63GwtVKVkAQHp+Djrc2ouAxOhajuz3RUkiQgghhJAqcOvWLTRt2hRqampo3LgxLl68WOr8ubm5WLlyJaytraGuro6WLVvi4cOHIvOlpKRgxowZMDExgZ6eHqZNm4aMjAyuft++fZCXlxf5V1BQUNW7SAghhFQ5a9W6uNF2NBQkpAAAyblZaHtzF4JTPtdyZL8nShIRQgghhFSSn58funTpgh49esDf3x/Dhg1Dnz594OXlVeIyM2fOxLZt27B582a8fv0agwcPRocOHRAYGMjNk5OTAzc3Nzx69AhnzpzBixcvYGZmhpMnT3Lz5OXlQVNTE7GxsUL/xMTEqnWfCSGEkKrSREMfV9qMhLSYOAAgPjsDbW7swoe0xFqO7PfDY4yx2g6ipqWmpkJJSQkpKSlQVFSs7XAIIYQQUgk/wnV98ODBePfuHR49esSVtWvXDlJSUrh8+XKxyygrK2PRokWYNm2a0DLa2to4ePAgAGDDhg1YuHAhwsPDUadOnWLXs3PnTqxduxZhYRXv7PNHOIaEEELIjY9v0e3uAeQJClvDmiiowavTn6grS9em71GZ6zq1JCKEEEIIqSRvb2+4ubkJlbVt2xbe3t7Fzi8QCJCXlwdJSUmhcikpKaFXzs6fP4/27duXmCAq8vHjR+jo6EBfXx89evTAq1evKrgnhBBCSO3poGuOEy6DwOfxAADv0r6gzc1dSMjOKGNJUlXEazsAQgghhJCfXXR0tEgip06dOkhOTkZ2djakpaWF6vh8Prp27YpNmzahTZs2MDMzw9WrV3Hz5k3w+f97hhcWFgYHBwcMHz4c165dg6qqKrp06YJFixZBQaFwNBh5eXn8+++/6NatG7Kzs7Fy5Uo4OjoiICAApqamxcabk5ODnJwcbjo1NRVAYfJKIBBUyTEhhBBCKqKnvhX2Ne+HEd6nAABvkuPQ/uZu3Gk/FkqSMrUc3c+hMtdyShIRQgghhFSBr5M7X0+X9Gb/jh07MH36dDg6OiI9PR0ODg6YOHEiduzYwc1TUFCATZs2Yc2aNfjnn38QHh6OoUOHIioqCqdOFd48Dx48WGi9Bw4cwJMnT7Bx40Zs3bq12G2vWrUKS5YsESlPSkpCfn5++XeaEEIIqQZdVIyw2rod5ry6BQB4kfgJHW7swWnHfpATlyxjaZKWllbhZSlJRAghhBBSSXXq1EF8fLxQWXx8PBQUFCAjU/xTTxUVFRw4cAAHDhxAfn4+xMXF8ccff8DQ0JCbR0tLC0ZGRly/RVpaWliwYAHGjh2LnJwcSElJiaxXTEwMDRs2REhISInxzps3D9OnT+emU1NToaenBxUVFeqTiBBCyA9hpmobCCTEMO/FdQDA06SPGO1/GZfcRkBKjFIZpREXr/jxoSNLCCGEEFJJTk5OIsPXe3p6omnTpuVaXlxcHDk5Obh06ZJQy6BmzZoJjXYGABISEmCMldqUPDg4GA0bNiyxXkpKqtgEE5/PF2kRRQghhNSWuTZuSM/PxYqXdwEAd2JCMfDhcZx2HQIJPo3iWZLKXMvpLoAQQgghpJImT54MLy8vHDx4EDk5OTh9+jRu3LiBqVOncvPs3r0b8vLyKCgoHLHlwoULOHXqFHJychAfH49hw4ZBXFwcc+bM4ZaZNGkSnj9/jhMnTqCgoADv37/HmjVr0KVLF66F0h9//IFHjx4hNzcXycnJmDlzJt68eYPx48fX6DEghBBCqsOyRh0w2aIFN30xMhAjvE5BwKgPvepASSJCCCGEkEpq2bIlDh06hL///huysrKYOnUqtm3bhs6dO3Pz5ObmIiMjg+ujqFWrVrh27Ro0NDRgYGCA/Px8eHl5QVVVlVvG0tISFy5cwNKlSyElJYXGjRujSZMmOHDgADfP4MGD8ddff0FVVRV6enp48uQJ7ty5gxYt/ndDTQghhPyseDweNjTthpFmDlzZsfAXmPDofIn9/pGK47Hf8KimpqZCSUkJKSkp9N49IYQQ8pP70a7reXl5kJCQKLY8JycH8vLyQuUFBQXg8/ng/f9wvyUp6reoJAKBoMLNy3+0Y0gIIYR8q0AgwMAHx3D6QwBXNsPSBWuadCnzGvq7qcx1nVoSEUIIIYRUoeISREXl3yaIgMKOpstzc1tWJ5TUlxAhhJBfmRifjyPOA9BZ14IrW/f6AZYF3AZQ8mii5PvQ3QQhhBBCCCGEEEJ+eJJi4jjjOhSuWiZc2SK/W1A4sgD8g7OgdWIx/n5xA2l52bUY5c+NkkSEEEIIIYQQQgj5KciIS+BSm5GwV9PjytLzcwAAcdnpWPHyLlpe206JogqiJBEhhBBCCCGEEEJ+GvISUmhV17jYOgFjCEyKwdrABzUc1a+BkkSkxh09ehSbN2+u7TAIIYQQQgghhPykjoQ9L7GugDHsDn5cg9H8OihJ9IvYu3cvhg8fjvv37wuVh4aGYvjw4UhJSamVuA4dOoRt27YJlT1+/Bj37t2rlXgIIYQQQgghhPzcGGOIy04vdZ7YrDQkZJU+DxFFSaJfhLe3Nw4dOoSJEyeioKCAK4+Li8OhQ4eQlZVVK3E9evQInp6eQmVDhgzB5MmTayUeQgghhBBCCCE/Nx6PB01p0RFDv2VxYQ0OhD6lkc++Q+ljqZKfir29PUJCQnDo0CGMHDmyxPkiIyNx+PBhfPz4EcbGxhg2bBg0NTW5+kOHDnGJHTU1NTg6OqJv375C61izZg0MDQ0hLy+P69evQ0dHB3PmzBGa5/z58/D09EROTg6GDx8OAJg9ezZCQ0ORmJiI1q1bc/PGx8fj4MGDePfuHfT09DBw4EAYGRlV9pAQQgghhBBCCPkFja3viBUv70JQSgIoIScDI71PY3+oL3Y49YKVSt0ajPDnRC2JqgljDHkJkcj+9AZ5CZE1krlUU1PD7Nmz8ffff5fYcui///6DnZ0doqOj0bBhQwQFBcHS0hJhYWHcPPXq1UOrVq3QqlUr1KlTB3PmzMGYMWOE1nPz5k1MnjwZK1euRL169WBjYyOyLSMjI2hra0NDQ4Nbn4qKisjrZkFBQWjYsCECAwPRsGFDxMbGws7ODo8f0zukhBBCCCGEEEJEzbJuBWuVuhDj8YTKxXg86MkpQ1VSlivzjnsPO48NmON7BRl5OTUd6k+FWhJVg/y0L0h7cRGpT05BkJ0GvrQCFJv2g0KjnhBXUKvWbU+bNg3btm3Dpk2bMHfuXJH6UaNG4e+//8aUKVO4smHDhmHx4sU4evQoAMDJyQlOTk5cfd++fWFqaopFixZBV1eXK5eXl4enpyfExYv/M7Kzs0P9+vWRkJDAtSQqzsSJEzF48GCsWbOGK1NUVMTcuXNF+lgihBBCCCGEEEIUJKTh1WkC1gY+wO7gx4jNSoOWjALG1nfETCsX5AkEmPfsGnaHFDY+yGcC/Bt4Hyff+2Nz0x7obmBVy3vwY6IkURVjjCHtxUUkP9jLlQmy05D8YB8AHpSdR4L3TaazKsnKymLRokWYO3cuxo4dK1QXFhaG4OBgPHz4EK9evQJjDIwxBAcHIz39fx16CQQCXLx4EY8fP0ZCQgIEAgH4fD6Cg4OFkkRubm4lJojKKyMjA/fv34eEhARGjx7NxfThwwf4+/tXat2EEEIIIYQQQn5dChLSWGLXHkvs2oMxJvJbe1fzPhhh1gTjH51DQGI0ACAyIxk97h1EV70G2Ny0BwwVVGsj9B8WJYmqWP6XKKQ+OVVsXeqTU5C3agsJNf1qjWHUqFHYsGEDVqxYgZ49e3LlX758AQA4OjpCQ0ODK3d2doa8/P86/RowYACeP3+OIUOGwMzMDBISEjh+/DjS0tKEtqOsrMz9/8OHD1i8eDE33bt3b3Tt2rXMWJOTkyEQCLhWR1/HVFrrI0IIIYQQQgghpEhJjTEc6xjgWdcp2Br0Hxa+uIn0/MLXzS5HvcGd6FD8bdsW0y2dISlG6RGAkkRVriAnHYLstGLrBNlpKMhOh0Q1xyAuLo6VK1di4MCBaNSoEVde1ArI3Ny8xAROUlISTp8+jRcvXsDOzg4AkJCQgLy8vFK3qaCggFatWnHTBgYGAEr+oBZRV1eHlJQU9PX1KSlECCGEEEIIIaTKifPFMNXSGX0NbTD1qQfOfngJAMgqyMO859dw5N1zbHfqBRctk1qOtPbVesfVT548wbBhw9ChQwfMmjUL8fHxZS6TnZ2NzZs3o0ePHhg4cKDIEOu1SUxKHnxphWLr+NIKECvHMH1VoVevXrCzs8OSJUu4Mh0dHbRu3RpLlixBcnIyVx4fH487d+4A+F9SJykpiatfsWJFmdtTU1PD8OHDuX8NGzYEAKiqqiIxMbHE5aSkpODu7o5///0XMTExXHlaWhquXLlSvp0lhBBCCCGEEELKoCOnhDOuQ3G97WgYf9Vf8JvkOLS6vgPDHp7A56ziG338Lmo1SeTt7Y2WLVtCQ0MDY8aMwfPnz9G8eXNkZGSUuExGRgZatmyJgwcPwt3dHe7u7li9ejV8fX1rMPKSiavpQbGpe7F1ik3dIa6qV2Ox/PvvvwgNDRUqO3LkCMTExFC/fn306tULrq6ucHR0RGZmJoDCV8gmTpyInj17om/fvmjcuDEePHgAKSmpCsXQrVs3+Pj4oFu3bhg+fDjevHkjMs/mzZtRr149NGjQAN26dUO7du1gbW2Nz58/V2ibhBBCCCGEEEJISTromiOwx0z8bdsWknwxrvzwu+cwP/8vdgc/hoAJajHC2sNjNTE2ewlcXFxQp04dnDlzBgCQnp6OunXrYtmyZZg6dWqxy8ydOxcHDx5EcHAwlJSUABR2Fp2VlQVZWdlil/lWamoqlJSUkJKSAkVFxSrZl68VP7qZOxQa94S4fPV0iuXt7Y2srCy0bdtWqPzcuXNIS0uDu7s7ZGRkABQer2fPniEsLAza2tpo0qSJyLF79uwZQkNDUbduXbRs2RInT55Ey5Ytoa9f2J/SrVu3oKamhsaNG5cZW2RkJJ49e4bU1FS0b98ekZGRyMjIQOvWrYXme/nyJd68eQN1dXU0adKEO7+EEEJIaar7uv47oGNICCHkdxWSEo8Jj87jboxwAwtHDQPscOoFWzWdWoqs4ipzXa+1JFFmZiYUFBRw8OBBDBkyhCvv2bMncnJycO3atWKX09bWxqBBg4SGS/9eNXEjxBhDfmIUCrLTISYtD3FVvWod1YwQQgj5XVGCo/LoGBJCCPmdMcZw8r0/pj+9hNivXjfj83iYbNECSxu1h4KEdC1G+H0qc12vtY6rP378CIFAAB0d4aycjo5OiX0MffnyBTExMbCyssJff/2F58+fQ1tbG8OGDYOzs3OJ28rJyUFOTg43nZqaCqBwqHeBoPqakImp6KKo4VrR0O6EEEIIqVrVeS0nhBBCyK+Px+NhgLEdOuma468XN7D9rQ8EjEHAGDa+8cLpDwHY6NAdfQwb/vKNP2otSZSbmwsA3CtQRWRlZbm6b2VlZQEAZs6ciTFjxmDSpEl48uQJWrdujePHj6Nfv37FLrdq1SqhDpyLJCUlIT8/vzK7QQghhJBalpb2e3cwSQghhJCqoSQpgy2OPTHctAnGPzqLZwkfAQDRmanod/8I2uvUxzbHnjBRVK/lSKtPrb1u9unTJ+jq6uLy5cvo0qULVz5q1Ci8evUKT58+FVkmIyMDCgoK6Nu3L06dOsWVDx06FMHBwXjy5Emx2yquJZGenh6SkpKoSTUhhBDyk0tNTYWKigq9KlUJ9LoZIYQQIqxAIMCu4EeY/+I6UnKzuXIpMXHMb9gac6xbQ0qs1trdlOqnfN1MR0cHderUwYsXL4SSRL6+vnBycip2GTk5OdSvXx96esIjhOnp6cHHx6fEbUlJSRU7OhefzwefX6sDvBFCCCGkkuhaTgghhJCqJsbnY4JFc/QysMZM3ys4Fv4CAJBTkI9Ffrdw7J0ftjv1gpu2WS1HWrVq9a5q+PDh2Lt3L+Li4gAAly9fxqtXrzB8+HBunjVr1mDEiBHc9OjRo3H+/Hl8+fIFQOErY+fOnYOrq2uNxk4IIYQQQgghhJBfm5asIo66DMSd9uNQT1GDKw9JjUebm7sw8MExxGSm1mKEVatWk0SLFy+Gra0tTE1N0bBhQ/Tr1w/r168XakkUHBwMX19fbnrq1KlwdXWFiYkJHBwcYGRkBGNj40qNdkYIIYQQQgghhBBSEjdtM7zsMQPL7DpA+qvXzE6E+8H8/L/Y+sYbBb/AYBq11ifR18LDwxEXF4f69etDVVVVqC44OBipqalo0qSJUHl0dDQiIyOhr68PbW3t79oevXdPCCGE/Droul55dAwJIYSQ8gtP+4KJjy7g+qe3QuWN1XSxs1lv2KvrlbBkzajMdf2HSBLVNLoRIoSQmpGTkwMvLy9ER0dDX18fjo6OkJaWru2wSAXFx8fj3r17yM/PR6tWraCjo1Pq/Pfu3UNISIhQmaamJnr27ClUlpKSgjt37iAjIwMtWrSAsbHxd8VF1/XKo2NICCGkNHFxcbh16xaGDBlS7PTviDGG8xGvMOWJBz5lpnDlPPDwh7kTVjTqCGUpmVLWUH0qc12nnh7JL+3x48dCrysSQmqOr68vjI2NMWPGDNy9exfLly+HlZUVLl68WNuhkQq4d+8eTExMsHPnThw9ehT16tXD2bNnS11m//792LBhA/z9/bl/oaGhQvP4+fnB1NQUa9euxdmzZ2FlZYWdO3dW564QQggh5DuFhoZi0qRJJU7/jng8HnobNkRQr1mYbukMMV5heoWBYftbH5hf+BfH3r3Az9Yu58ccr41USF5eHl68eIG4uDgYGxvD0tISPB6vtsP6bl5eXpCXl4ednV2l17V161ZIS0uLvK5ICKl+48aNQ8uWLXHy5EmuLC4uDq9fv+amT58+DQsLC8jLy8PX1xcSEhLo0KEDZGSEn7okJSXB09MTWVlZsLW1haWlJVcXHh6OW7duASgcBdPCwgL29vZCy+/btw9ubm5IS0vD8+fPYWFhAT09PVy6dAmjRo3C06dP8e7dO1hYWKBJkybIz8/HvXv3EBsbCwcHB5ibm3Pr8vb2RmBgIABAVVUVjRs3homJCVcfHx+Pc+fOYcSIEVxSxMTEpMSRO4tERETgzp07GDp0KO7du4fIyEj07t1b5DXs2pCfn49hw4ZhxIgR2LRpEwBg2bJlGD16NNq1a1fqEyonJ6dSkz4jRoxA69atcerUKQDAnj17MHHiRHTu3FlkNFNCCCGEVK8LFy6Az+eje/futR3KD0cgEODhw4eIioqCtbU1bG1tAQAKEtJY59ANQ03t8YfPOTyKjwAAxKUkYvCOVVjJV8DafqPR0b6ZyDo/f/4MLy8vyMrKws3NDZKSkgAK7wtv377Nzde5c2fUrVu3+ncS9LrZL9Ok+tGjR+jfvz+kpKRgYWGBiIjCP8xt27ahefPmtRzd9+nSpQtMTU2xcePGSq9r8ODBkJaWxt69eysfGCHku8jLy2Pp0qWYPn16ifNYWVlBXl4esbGxaNasGZ49ewYxMTF4eXlBXV0dAHDt2jUMHjwYTZs2haqqKu7cuYN+/fphy5YtAApbouzatQsAkJ6ejjt37qBly5Y4c+aMUCx2dnZISEiAk5MTunXrBmVlZbi6uqJx48ZQU1ODoqIiLl68iFmzZuHOnTtQV1eHtLQ0rl27htOnT6Nbt24AgFOnTsHT0xNA4YX91q1bWLFiBaZMmQIAePbsGZo0aQIXFxcAgLa2Nq5cuYKxY8di7dq1JR6LTZs2YcWKFdDV1YWOjg50dXWxYcOGMl/PO3z4MDIzM0usV1JSwoABA0pdR1nu378PV1dXBAcHo169egCAhIQEaGlp4dixY3B3dy92ucGDB+Pz588YNGgQVFVV4eDgAE1NTa7+7du3sLCwwIMHD+Ds7AwAyM3NhYaGBpYuXcod07L8itf1mkbHkBBCyKdPn1C/fn0IBAKEh4dDS0uLq/P29kaXLl2QnJxc7PSvLjc3F507d0ZoaCgaN24MT09PjBw5UuTeTsAE2B/qi5lXjiBl1VFAXhpQVwJef4DrqEG4umEXZMQlAAB79+7FjBkz4OjoCHl5ecTExODWrVuQl5eHv78/tm7dioKCAhw8eBCenp5o1apVueOtzHWdWhL9IoYMGQI3Nzfs27ePaz0UFBSET58+Cc3HGMPz58/x8eNHGBsbo2HDhkL19+/fh6qqKvT09ODn54eCggI4OztDSkqKm+fZs2cICwsDAKipqcHGxgZ16tQpdj26urp4/PgxZGRk4OrqWuayT548QXR0NAQCAdf6oHPnzlBQUCgzdqDwB+LDhw+hoKBQJS2RCCEV5+LignXr1kFFRQVt2rQpsVVIeHg4AgMDUadOHWRmZsLR0RHLli3Dpk2b8OXLF/Tv3x8nT55Ep06dAAAxMTGwtLREly5d0L59e9jZ2Qm1VElMTESDBg1w+fJldO3alSsXCAQICAjgntDcv38fQOH3Z1EyYvHixViyZAl27tyJcePGAQBmzpyJf/75h0sSubu7CyVF/vvvP7i5uWHgwIHQ0PjfsKitW7fG33//DaAw0dWtWzcsWLAAKioqxR4Hf39/pKSk4Pz582jRokW5j/Pr16+RkpJSYv23388V8fr1a4iLi8PMzIwrU1dXR506dYRahhXn3bt3uHXrFj58+AB/f39s3LgRY8aM4dYLAA0aNODml5SUhImJSZnrJYQQQkjV2rdvH/r164fs7GwcOHAA8+bNq9T68vPz4e3tjcTERDg4OCA6OhrZ2dlwdnZGdHQ07t+/j169euHx48f4/Pkz+vXrh6ysLPj4+CAxMRG2trZC9x4PHz6EjIyM0FsiZ8+ehb29PQwNDfH06VNkZ2ejXr16ePz4MRQUFNCyZUvu3g8ofLgYFBQEAwMDODk5gc8vfEUsISEBFy9eRP/+/SEvLy+yL3v37sXr16/x+vVrqKio4OXLl7Czs4O7u7tQPHweH6PrNcU5z2V4Y1EPkSMKH4LhQxw8lx+EmakSdvcaizpxGZgwYQLu3bvH3ff5+/ujoKAAAGBra4u9e/ciOzsbBw8erNR5+F6UJKomjDFEJWchPacA8lJi0FOWqbZXv7Kzs/Hu3TssXrxYaBsWFhawsLDgpuPi4tC9e3ckJCSgQYMGePnyJUxNTXHx4kXug7B8+XJkZmYiKioKlpaWCA4OhqSkJDw9PblR5AICArimb3FxcfD19cXWrVsxfPhwblvLly9HVlYWoqOjYWlpCQcHB7i6upa5bNHrchkZGVy/Jc7OzsjMzCwz9jdv3sDNzQ3KysrQ1dXFu3fvoKSkhMaNG1fLcSeElO7w4cNYuHAhZs+ejYSEBOjo6KBnz55YtGgR10oIAPr168clMmRlZTFmzBisXbsWmzZtwqVLlyAQCBAdHY09e/YAKPx+1dLSgpeXF9q3bw8ASEtLg5eXF2JiYpCXlwd1dXU8f/5cKEk0dOhQoZuEIoMHD+b+X/R98W3Z4cOHhZaJiorCs2fPEB8fD4FAAIFAgNevXws94Rk2bBj3f0dHRxQUFODDhw8lJokCAgLwxx9/fFeCCABWr179XfMX8fHxwcuXL0udp3fv3tDQ0OCeRn17HVNRUUFqamqJy0+cOBGHDx/mbsC2bNmCP//8E82bN0eDBg24ZZWVlb9rvYQQQgipWgKBAPv27cPly5eRm5uLfv36Ye7cuRX+DZubm4v27dsjMjISTZo0wZw5c6CgoICGDRvC2dkZb968wfjx47Fx40Zoa2ujXr16aNy4MTp06ABFRUUYGRlh9OjRmDlzJhYuXAigsK9DLS0toaTMX3/9heXLl8PQ0BCnT5/GjRs3kJ2djcaNG8PPz49rhS4vL4/Jkyfj9OnTcHNzQ3R0NMTFxXHr1i3weDx8+PABY8aMQZs2bYpNEl26dAm9evXi7uMaNmwIe3t7XLp0qdiuTYJeBWLunDmw6tgefzw6h9eGAOSk8em/5+isuA+GZ33h2r4tdHR0cOzYMejo6KBly5YQExOr0PGuSpQkqgZfMnJxMTAGp/yjkZZTAAUpMfSz0UZP67pQkxP9gVJZ0tLSsLGxwT///ANVVVW4uLhATk5OZL6RI0fC0tISe/bsAZ/PR05ODtzc3LB8+XL8888/3HzPnz/HixcvYGlpiezsbLi6umLevHk4dOgQAGDUqFEYNWoUN/+1a9fg7u6Onj17QklJiSsPDAzEq1evoK+vz5WVtewff/yBq1evirxu1rlz5zJjnzhxIpycnHD27Fnw+XzcuXMHbdu2pSQRIbVETU0N27dvx9atWxESEoI7d+5g2bJlePToEXx9fbmbjm9bGOnr6+Pjx49gjCEyMhKSkpJ49uyZ0DzOzs5c65MHDx6gR48eMDc3h5mZGWRlZZGRkYEvX74ILfP1a05f+/p7S0JCAmJiYkLfoRISEsjNzeWm165diyVLlqB58+bQ1taGpKQkeDyeyPa+Xm9RcionJ6fYGPLz8/HmzRusW7eu2PrSVPR1s0+fPsHf37/UdRe13pKRkUF6erpIfWpqKmRlZUtc3tHRUWh64sSJmDdvHu7evYsGDRpwfU+lpaUJJc9SU1NhYGBQamyEEEIIqTrXrl2DmZkZ97aGjo4Obt26xT2Q+1579+7Fp0+f8OrVK8jKyiIxMRHm5uZCb4OkpaVh8eLF3P1Gjx49YGNjgzNnzoDH4+HZs2dwdHREt27dYGNjU67tfvjwAUFBQdDT00N2djYcHBywceNG/PXXXzhy5AiuXLnCdcfy4MEDMMbA4/GgoaGBUaNGQUFBocT1tmnTRqjM0NAQHz58KHZ+HR0dvHnzBn/88Qf8uk/H0gcXsTxjPRBf+BDsQ1AIIvlicHB1hptjczx//hyKiorw9PSs9de+KUlUxRhjuBgYg71PoriytJwC7HsaBR4PGOmgXy0tii5cuIDp06ejV69eyMvLg42NDfr06YPp06dDWloanz9/xrVr17Bq1SqcP38ejDEwxmBkZMT1rVGkU6dOXKew0tLSmDx5MkaNGoWDBw9ysX/58gUBAQFISEhAQUEBsrKy8ObNG6GOWbt37y6UICpSnmW/Vp7YExIS4OnpCW9vb+6JdZs2bdCoUaPKH1xCSKXw+XyYm5vD3NwcampqGDhwICIjI7kkwLfJlYSEBGhoaIDH40FFRQV5eXnYvn0799n+1sKFCzFy5EihBEuzZs2qZSSJnJwczJ8/H+fOneNaKWVnZ2PPnj2V2l5QUBBycnKKfU02Ojoafn5+XKfdpqamQvUVfd2sb9++6Nu3b7niMzMzQ05ODqKjo7lWpZmZmfj8+bNIPKXh8XiQkpJCUlISt14AeP/+PZckEggEiIiIQM+ePcu9XkIIIYRUzu7du6Gnp8f15WpkZITdu3dXOEnk5eWFHj16cA+TVFVVuWRQETk5OaEyLy8vHDp0iPvNaW9vD0tLS3h5eZU7SeTq6so9gJSWlka/fv3g5eUFoLDhwdKlSzFhwgQ4OTlx/UcCgIGBQan92BYUFEBcXDh9IiEhwb0e9q358+ejd+/e4PF4MDIywrWjR6GhUQdy8ir4ULhCCOKTkbByJIL0jLF3zRKMceuKDRs2YNGiReXa1+pCSaIqFpWchVP+0cXWnfKPRtt6daCvIlNsfWUYGRnhwoULyMzMxIsXL3Djxg2sWLECT58+xcWLF7kM56NHj0SeHDdt2lRo2tDQUGTdWVlZiI+PR506dbBjxw7Mnj0bVlZW0NbWhoSEBHg8Hj5//iy0XHG9r5d32a+VJ/bIyMgSYyeE1I6XL1+K9B0WHx8PPp8vNGLXhQsXsGLFCkhIFHbid/LkSe61rU6dOmHatGnYu3cvxo4dyy2TlpaGjIwMaGlpITExETo6OlxdcHAwnj17Vi1J4rS0NOTl5Qlt7/jx4xAIBJVar7+/P/T19UVeu7pw4QKmTp0KOzs7iIuLQ0tLSyQpU9HXzb5Hq1atoKCggOPHj2PmzJkACkemY4yhQ4cOAAqble/fvx+urq6oX78+cnJyuNcMi9y8eROJiYlo1qxwdA87Ozvo6uri+PHj3Pm6desW4uPjhV4VJIQQQkj1+fjxI54+fYouXbrg8ePHAApbQd++fRsxMTEVGlUrJydH5DV/KSkpoVbV377WlZOTI9QXbtEy2dnZAFBsY4tvkzSlLX/06FHcv38f9+/fxz///AMVFRWcPn262NfLvlW3bl3ExMQIlcXExHAjnH2rc+fOePLkCc6dO4eIiAhs3boVo0ePxrCWHWHh1hnuWy4jS0EWkJfBy6QYtLqzB/XMdPA8wF9oPbUxzhgliapYek4B0nKKzyam5RQgPSe/WrcvKyuLFi1aoEWLFlBRUcGsWbOQkZHBNVmbM2cOd3NekqInvF9Pi4mJQUVFBTk5OZgyZQpOnTrFPeXNysrifix87dsP8fcs+7XyxK6mpsbF+vUPkqSkJChKiyP70xuISclDXE2v2vqGIoQImzBhAsTExNCsWTNoamrizZs3OHLkCObPny/UlDc7OxsuLi7o0aMHHj58iKdPn+Lp06cAAFNTU6xfvx5//vknfHx8YG1tjffv3+PmzZs4efIktLS04O7ujuXLlyMtLQ0CgQC7d+8WetWrKqmrq6N169YYMmQIRowYgQ8fPuDMmTPF9nX0PQICAop9Qnbz5k0MGDAAkydP5lrw1AY5OTnuPERHR0NKSgpbtmzBokWLhFoW/fHHHzhw4ADq16+PvLw8tGvXDs2aNYO5uTnev3+Pffv2YezYsVxzbT6fj61bt6Jv375IT0+HpqYmtm3bhokTJ8La2rrW9pcQQgj5nezduxfdunXD7t27hcrHjBmD/fv3Y8GCBd+9ThsbG6Eh3AUCAR48eCDyKvq3yxR1GQIAsbGxCAwMxMqVKwEU/ub7+PEjN//nz5+5xgJFfHx8kJ2dzY0Oe+/ePdja2qKgoADR0dFo1aoVWrVqhcWLF8PY2BiXL1/GgAEDyuy42tXVFWfPnsXKlSshJiaGhIQE/Pfff5g2bRqAwoFY7t27h9GjRwMofDBqZWXF3d89ePAAoaGh6Nu3L4z0jbCw/0is3rQB6QKg4P8by4cEByOyngF2BPkgOisVe4IfIy4tGQCwP+QpGjd3hIJE6aPeVgVKElUxeSkxKEiJFZsoUpASg7xU1R9ygUCAjx8/irzaVfThkJKSQv369WFoaIidO3eKJFo+ffok8qT36w/W+fPn0aRJE0hISCA6Ohp5eXmoX78+N/+5c+fKleH88uVLuZaVl5fnsr0AyhW7np4e9PX1cfHiRVhZWQEAPoa9gY+3FzRsNBCzbxT40gpQbNoPCo16QlxBrcx4CSGV4+3tDR8fHzx8+BAREREwMTHBo0ePRJ64zJgxA6ampvDx8UGjRo2wefNmGBsbc/VTpkyBm5sbLly4gI8fP6JBgwZYvHgx1/n1woULYWFhAR8fH8jKyuLSpUt4/vy5UB83o0ePFmlZqK2tjXHjxgm9xqavr8+NalbExMQEI0eO5KavXLmC/fv3IyQkBLq6unj27Bk2bNgAExMTAIWvd40bN07oSZaEhATGjRtXYr9IJiYmxd40/fvvvzhy5AimT5+OpKQk3Lhxo9YS3aNHj4alpSU8PDyQl5cHDw8PuLm5cfVSUlIYN24c9x0vLy+PFy9e4NSpU3j58iV0dHTg6ekpsp/du3eHr68vzpw5g7S0NOzbtw/du3ev0X0jhBBCflcFBQXYv38/Nm3aJFLXsWNHzJgxo0KjnE2cOBG7du1Cz5494eLigqtXryI5ObnU+5gVK1agS5cuSE9Ph7GxMfbu3YvWrVtzD5d69OjBjZirpqaGM2fOiLQcKigoQJs2bdC7d288fvwYvr6+2L17NxhjaN++PZydnWFlZYXg4GDEx8dzLZnL6rh68uTJOHjwILp27YrWrVvj6NGjaN68OTp37gwAePr0KcaMGcMlicLCwjBnzhx0794d0dHR2L17NzZs2MDdj06a8CeOHDwElePPEGemgXfPXwKfk5H9R1dMeHweyM4Fnr4FCgp/Kx+5cAaeL5/h6qQlaFjfQiS+qsRjtdF+qZYVjdKSkpJS5Z1CMcaw/2mkUJ9ERUY31auWPony8vJgYWGBRo0awcHBAWpqaggICMDOnTsxf/58bghmT09PdO3aFW5ubujcuTOSkpJw7do1dOzYEXPnzgVQ2I9PQEAA6tWrh6FDh8Lf3x979+7F7du3udc/HBwcwOPxMHbsWISHh2PPnj1ITEzE2bNn0aNHD2499vb2Qh1il3fZtWvXYuPGjVi8eDHk5eXRuXNnPHv2rMzYT5w4gWHDhmHatGnQ19fHtnWrEBsXh7bmKvinuwkXg7LLaCg7j6QWRYT8AKysrDB69GhMnTq1tkP54cTFxcHLywsCgQAhISE4cuQIgoODazusH1J1Xtd/F3QMCSHk9xQeHo6VK1diw4YNIp02p6WlYdq0aVi4cCHy8vKwefNmbN68GUBhEuTr6eLExsZi//79SEpKgouLC86fPw95eXls3rwZQUFB2Lt3r8igHcHBwThz5gwSExPRqFEjDBw4UOiB3sOHD3Hp0iUoKSnB3d0dR44cQY8ePdC4cWPMnDkTCQkJGDJkCG7dugV5eXkMHz6c66MoKysLR48exatXr1CnTh24u7tz/SNGRERg2bJlWL16NfeWyrcSEhKwZ88efPz4EVZWVhg5ciSXpPL19cWuXbuE+jV6/vw5Tp48CUlJSfTp00ek78nU1FTs27cPYWFh+KIgjttGkkiU/P/0THoWcOah0Pw88DBy7GjsHTy5xGP+9borel2nJFE13AgVN7qZu602elrVhWo1jG4GFGZMPTw88OjRI3z58gV169ZF165dRZ7YRkZG4tChQwgLC4O2tja6dOnC9e4O/C+54+DggPv37yM/Px/Dhg0T6rcoKSkJW7duRVhYGOrWrYuRI0di5cqVmDhxIuzt7QEUZoGNjY1FRtQpz7K5ubnYvXs3/Pz8kJGRgfXr10NbW7vM2AHg9u3bOH36NOTEGZrk+yEsOhESYjwMsP/f03u+tAK0R+2FhJpop9qEkJpFSaKSPXz4EJs3b4aYmBiMjY0xYcIEkZHgSCFKcFRe0TGcMmWK0FNZGxsbDBw4EAC41zq/ZmxszLW+27RpE6KjhftlVFdXx6xZswAABw4cwNu3b4XqpaSksHTpUgDA2bNn4evrKxLb0qVLISUlhRs3bogMtgEAs2bNgrq6Onx8fODh4SFSP378eBgZGSEwMBBHjhwRqR84cCBsbGwQERGB7du3i9R37doVLVq0QGJiYrF9gLVq1QodO3ZEXl4e/vrrL5H6xo0bo1+/fgCAxYsXIysrS6i+Xr163Miv69atE+mnUUtLi3udYc+ePQgLCxOql5OT4x4Injx5En5+fiIxFL0ecfXqVTx8+FCkft68eVBWVsbDhw9x9epVkfqJEydCT08Pfn5+OHnypEj9sGHD0KBBA7x7907kdRUA6NmzJxwdHREfH4+1a9eK1Ldp0wZt27ZFdnZ2sR22Ojg4oHfv3gAKW49+PeIkAFhYWGD48OEACltgfjsggq6uLiZNmgQA2L59OyIiIoTqlZSUMH/+fADgfkB+jc/nY9WqVQAADw8P+Pj4iMS4cOFCyMvLw9PTEzdu3BCpnzJlCrS1tfHs2TOcOXNGpH7kyJGoX78+goODsX//fpH6vn37wt7eHtHR0cW2+OjQoQNcXV2Rnp6OZcuWidQ3a9aMa6U5b948kb70rK2tMXjwYACFfy/fDohgYGCACRMmAAC2bNki9MoPUPga0OzZswEABw8eRFBQkFC9pKQkF9e5c+e418q/tmTJEkhLS+P27du4c+eOSP3MmTOhoaGBx48f48KFCyL1Y8eOhYmJCd68ecONyvy1/v37w87ODlFRUdi6datIfefOneHs7Izk5GTufH/N2dkZnTt3RkFBAff38jU7Ozv0798fQOH3VkZGhlC9qakpxowZAwDYsGEDYmNjherr1KmDGTNmAAD27duHkJAQoXoZGRksXrwYQGG/gM+fPxeJYfny5ZCQkMD169dx//59kfo5c+ZAVVUV3t7euHz5skj9hAkTYGBggICAABw/flykfsiQIbCyssL79++xc+dOkfru3bujWbNmSEhIwJo1a5CRkcGNGFtQUIAjR45g9erVGDBgAPe99bUmTZqgT58+AIC///5bZFRYc3NzjBgxAgCwZs0aJCQkCNX7+vpCX18fBw8exK5duxAeHi5Ur6CgwH1PHz9+HAEBASIxFH3PX7lyhevw+msLFiyAoqIiHjx4gGvXronUT5o0Cbq6ulxr6m8NHz4cFhYWCA0NFekoO18gwBm1LERpSkMmIw+NnsWILJ9cTwuB8zcjMzMTS5YsEal3dHREz549K3VvRK+bVQM1OUmMdNBH23p1kJ6TD3kpcegpS1dryxUxMTH06tULvXr1KnU+fX19LFy4sMz1lbYuFRUVkXUcPHhQaLqk91bLs6ykpCQmTpwosmx5Ym/btm3hTcanN4jZNwrN9LRE5hFkp6EgOx0Spa6JEFIT3N3dRTq3JoWcnZ3h7Oxc22EQQgghhFTItWvXoK6uDjk5OQQHB0NWVpZLmhNR4nw+cgWl92GckptV7Z1ZU0sieuIopKTXxH42eQmRiN4/GoLsNJE6aklECCG/FrquVx4dQ0IIIVUtJSUFhw4dQnh4OMzNzTFs2DDIyFT9SN9Fzpw5g/T0dK610c9I68RixGWnl1wvo4CY/qItLr9FLYlIlXF1dRXqMPZnJa6mB8Wm7kh+sFekTrGpO8RV6ZUNQgghVSs3Nxfnzp1DWFgYDA0N0adPnzJvhmNjY3H16lXExMTAwsICPXr0gJiYmMh84eHhuHr1KnJzc9G1a1fUq1ev0tsmhBBCqpOSkhImTy67/5yq0rdv3xrbVnUZW98RK17ehaCYtjxiPB7G1i95dLiqwi97FvI7WbBggUg/Qj8jHo8HhUY9oOwyGnzpwg7Y+NIKUHYZDYXGPanTakIIIVUqMzMTLVq0wLJly5CcnIy1a9eiSZMmSE5OLnGZu3fvol69erh06RIyMjKwbNkyuLq6Co3wCRT2/2FlZQVfX18kJiZiwIABQv0gVGTbhBBCCPnxzLJuBWuVuhD75veqGI8HK5W6mGnlUu0x0Otm1KT6l8YYQ35iFAqy0yEmLQ9xVT1KEBFCyC/mR7iur1q1Chs3bkRQUBBUVVWRlpYGS0tLDBw4sMRXuM3NzdGiRQuu48qcnBw0aNAAEyZM4DovffToEZo3b45z586hZ8+eAApHNf3w4QM3IktFtv2tH+EYEkIIIQRIy8vG2sAH2B38GLFZadCSUcDY+o6YaeUCBQnpcq2DRjf7TnQjRAghhPw6foTrepMmTWBnZyc0stOMGTNw+fJlkRFqgMJRXiQlJbFnzx6MHDmSK+/Xrx8iIyPx+PFjAIUjbwUHBxc7ik1Ft12cH+EYEkIIIUQYY6xCjRyoTyJCCCGEkFoUHBws0heCmZkZ3r17h4KCApF+hsTExGBmZgYvLy8uSZSdnQ1fX18kJSVx8z19+hRdu3bF8+fPcfPmTaiqqqJt27YwMTGp8LaBwlZLXw8tnJqaCgAQCAQiQ2MTQgghpPZUpF1PZa7llCQihBBCCKmkjIwMkSd1SkpKEAgEyMrKgry8vMgymzZtQu/evREbGwsLCwvcvn0bGhoa+PjxIzdPcnIy7t+/j3v37qFjx47w9/fHtGnTcPDgQbi7u1d426tWrcKSJUtEypOSkpCfX/rwu4QQQgj5saWliY7yXV6UJCKEEEIIqSR5eXmkpKQIlSUnJ4PP55c4ylj79u0REhKCW7duISEhAXv37sW1a9fw4cMHofXGxsYiLCwMcnJyAIBZs2Zh0qRJXJKoItueN28epk+fzk2npqZCT08PKioq9LoZIYQQ8pMTF694qoeSRIQQQgghlWRubi7S/09ISAjMzMyKfd2riLa2NoYPH85Nz507F46O/xve1sLCAgUFBVyCCACcnJywdu1apKSkQElJqULblpKSgpSUlEg5n88Hn0+D3xJCCCE/s8pcy+kugBBCCCGkknr37o1Lly4hISEBAJCSkoIzZ86gT58+3DxPnjzBX3/9xfUTEBoaiqysLK7+1q1bePDgAaZNm8aVubu7IyAggOszCAC8vLygo6MDJSWlcm+bEEIIIaQ8aHQzalJNCCGE/NR+hOt6VlYW2rZti7i4OLRr1w6enp6QkpLCgwcPuJi2bt2KSZMmIS8vD+Li4vDy8sLEiRPh7OyMxMREXLx4EcuXLxdKEhUUFKB379548+YNOnfujPDwcNy7dw+nT59Gx44dy73tsvwIx5AQQgghVaMy13VKEtGNECGEEPJT+1Gu6/n5+bh06RLevXsHAwMDdO/eXeiVrmfPnuHGjRtYsGABN5xtVFQUrly5Ah6Ph/bt28PIyEhkvYwx3Lp1C69evUKdOnXQrl07aGlpfde2y/KjHENCCCGEVB4lib4T3QgRQgghvw66rlceHUNCCCHk11GZ6/pv2XF1UV7s6/f7CSGEEPJzKrqe/4bPvaoM3RsRQgghv47K3Bv9lkmitLQ0AICenl4tR0IIIYSQqpKWlsZ15ky+D90bEUIIIb+eitwb/ZavmwkEAkRHR0NBQYHrE6AqpKamQk9PD1FRUdRUuxh0fEpHx6d0dHxKR8endHR8SvezHx/GGNLS0qCtrU3Dt1dQdd0b/ax+9s/E74DO0Y+Nzs+Pj87Rj68y56gy90a/ZUsiPp8PXV3dalu/oqIifdBKQcendHR8SkfHp3R0fEpHx6d0P/PxoRZElVPd90Y/q5/5M/G7oHP0Y6Pz8+Ojc/Tjq+g5qui9ET1uI4QQQgghhBBCCCGUJCKEEEIIIYQQQgghlCSqUlJSUli0aBGkpKRqO5QfEh2f0tHxKR0dn9LR8SkdHZ/S0fEhRBh9Jn58dI5+bHR+fnx0jn58tXWOfsuOqwkhhBBCCCGEEEKIMGpJRAghhBBCCCGEEEIoSUQIIYQQQgghhBBCKElECCGEEEIIIYQQQkBJou+Wl5cHf39/BAUFobzdOVVkmZ9Vbm4u/Pz88Pbt23Ivk5SUBD8/PyQkJFRjZD+G3NxcvHjxAsHBwd+9bGBgILy9vZGfn18Nkf0YsrOz8eLFC4SEhHzXcnFxcXjx4gWysrKqKbIfQ1ZWFp4/f46wsLByL5OcnAw/Pz+EhIT80n87AJCfn48nT5581/dPZmYmnj9/jnfv3lVjZD+G/Px8PH78+Lu+fz5//gx/f3+kpKRUY2SEVL8vX77Az88PSUlJJc5TdA0KDQ0ttj4zMxMvX74s9loTExPzXd89pHi5ubnw9vbG69evi63//PkzfH198fnz52Lr4+Li8Pbt22Lvt1+/fo34+Pgqjfd3k52dDT8/P8TExJQ4T2hoKF68eIHs7GyROsYYgoODERsbK1InEAjw33//oaCgoEpj/p2kpqYiICAAQUFByM3NLXaesn6rZWdn4+XLl8jIyBCpi4+PR2BgYJXG/KsLCgrCf//9V2I9Ywxv3ryBv79/iffpZc1TUFCA169f48uXLyJ1WVlZePz48fcHzki5PXjwgGlpaTEDAwOmrq7OrKysWHh4eJUv87O6c+cO09DQYEZGRkxNTY3Z2NiwyMjIEucPDAxkHTt2ZKqqqszOzo7Jycmx3r17s7S0tBqMuubcvHmTqaurMyMjI26fP378WK5lnzx5wqSkpBgAFh8fX82R1o7Lly8zVVVVZmJiwpSVlZmDgwOLjY0tdZmkpCTWs2dPJicnx+zt7Zm+vj47duxYDUVcs86fP8+UlJSYqakpU1JSYs2aNSv1b0EgELA///yTycjIMFtbW6avr890dXXZrVu3ajDqmpGRkcEWLVrE9PX1mYKCAuvdu3e5ljt58iRTVFRkZmZmTEFBgbm4uLDExMRqjrbmpaens4ULFzI9PT2moKDA3N3dy1zG29ubNWvWjNWpU4fZ2toyGRkZNm7cOJafn18DERNSdZ49e8Zat27N1NXVma2tLZOVlWWDBw9m2dnZQvN5eHgwFRUVZmpqypSVlZmjoyOLi4vj6l+9esXq1q3LLC0tma6uLgsJCeHq8vPzWePGjdn169drbL9+VdOmTWN8Pp+1b99epG7q1KlMSkqKNWjQgElJSbGpU6cK1e/YsYOpqKgwQ0ND1qFDB1ZQUMDVvX37lhkaGrKkpKTq3oVf1saNG5mioiJr0KABMzU1ZUOGDGE5OTlcfVxcHHN0dGTKysrM1NSUqaioMA8PD65eIBCwzp07MwMDA6aiosK2bNkitP4NGzaw/v3719j+/Grmz5/PZGVlmY2NDTM2NmZ16tRhZ8+eFZqnrN9qYWFhTE9Pj1laWjJNTU3m7+8vtHyrVq3YyZMna2R/fnYnT55kTZs2ZSoqKkxMTKzYeUJDQ1mDBg2YhoYG09fXZ3Xr1mXe3t7fNU9mZiZr2rQpMzMzY8rKyuzMmTNCy8+YMYPNnDnzu+OnJFE5paWlMQ0NDTZ9+nTGGGN5eXmsTZs2zMnJqUqX+VklJSUxFRUVNn/+fMYYYzk5Oaxly5bM1dW1xGUuXLjArl27xk1HR0czQ0ND9scff1R7vDXty5cvTElJif3999+MMcays7NZ8+bNWdu2bctcNjk5mZmamrKZM2f+skmi2NhYJi8vz1auXMkYK/zCa9KkCevatWupy7m6urImTZqwhIQExljhj+GDBw9We7w1LSoqisnIyLD169czxgq/W2xsbFjfvn1LXOb8+fOMx+Ox58+fM8YKb87Gjh3LNDU1ayTmmhQZGckWLVrEoqKiWPfu3cuVJAoPD2eSkpJs+/btjDHGUlJSWIMGDdiQIUOqO9wa9/79e7Z48WL26dMn1rlz53IliQ4cOMB8fHy46Tdv3jBlZWW2evXq6gyVkCp34sQJdu/ePW76/fv3TFNTk82bN48ri46OZrKysuzff/9ljBUmnhs1asR69uzJzdO3b1+2ZMkSxhhjM2fOZMOGDePq1qxZwwYNGlTNe/Lru3r1KrOwsGDdunUTSRIdPHiQycrKcj9aX7x4wWRkZNihQ4cYY4X3VQoKCszf35/l5OSwBg0asAsXLjDGCq9/LVu2FPnxRMpv7969TEpKit29e5crO378uNCDlR49ejB7e3uWkZHBGGNs1apVTFZWlsXExDDGCs9v/fr1WU5ODnv16hWTl5dn6enpjDHGPnz4wPT19YUSs6T8Hj58yACw27dvc2Xz5s1jMjIyXCKvPL/VxowZw6ZNm8YYY2z58uWse/fuXN2ePXtY586da2Bvfg2LFi1iPj4+7MCBAyUmiZo0acI6derEPYD7888/mZaWFvcZKs88u3btYs7OzkwgELAbN24wHR0dbtlnz54xMzMzofWVFyWJyun48eNMTEyM+zHKWGE2FgALCgqqsmV+Vvv372eSkpIsJSWFK7ty5QoDwN6/f1/u9UydOpVZW1tXQ4S1a/fu3UxaWlqoldTFixcZgFJbWzHGWL9+/djcuXPZ5cuXf9kk0ebNm5m8vDzLysriyk6ePMn4fH6JNwx3795lANjTp09rKsxa8++//zIVFRWWl5fHlR08eJCJi4uX+FR0165dTFZWVuhJ6oEDB5ikpKTQen415U0SLV26lNWpU0fo+OzcuZNJSUlxN62/ovImiYrTo0ePMhO3hPwMhg8fzlq2bMlNr1+/nikqKgq1ijh69KjQPVyDBg3Y/fv3GWOMXb9+nTVq1IgxVphw1tfX/yWvzTXp06dPTFtbm7148YK5u7uLJImcnZ1FWpn06dOHubi4MMYYCwkJYcrKylzduHHj2NKlSxljhS2Mvv6xS75PQUEB09HRYZMnTy5xnvj4eMbn84VamWRlZTEFBQW2YcMGxljhvczw4cO5ek1NTfby5UvGGGMdOnRgBw4cqJb4fwfnzp1jAIR+ZxT9bvjy5QtjrHy/1Zo1a8YuXrzIGGPs0aNHzNjYmDHGWExMDNPT0yvzNwsRVVKS6OXLlwyAUKug6OhoxufzuYR2eeaZPHky++uvvxhjjOXm5jIALDExkeXl5TE7O7sKv0FAfRKVk5+fHwwNDaGmpsaVOTg4cHVVtczPys/PD2ZmZlBUVOTKKrKvz549g6mpaZXHV9v8/PxQv359yMvLc2VFx8ff37/E5fbs2YN3795h6dKl1R1irfLz84OlpSWkpaW5MgcHBwgEAgQEBBS7zN27d6GpqYkmTZogODgYr1+/Rk5OTk2FXKP8/PzQsGFDiIuLc2UODg7Iz8/Hq1evil3G3d0d9evXx8iRI3Hr1i2cOHECK1aswIoVK4TW87vy8/ODnZ0d+Pz/XQYdHByQk5ODN2/e1GJkP6a8vDwEBAT8kt/P5PciEAjw4sULob9lPz8/WFtbQ1JSkitzcHBAQUEBXr58CQCQl5dHcnIygMK+FIvud8aNG4cVK1ZAXl6+xH48SOkEAgEGDx6MKVOmwM7Orth5/Pz80LhxY6EyBwcH7h5TXl4emZmZXD8sRecoOjoaK1euxLZt2xAfH483b95AIBBU7w79YoKDg/Hp0yd07doVCQkJePHihUjfJy9fvoRAIBA6R9LS0rC2thY6R0Wfofz8fKSnp0NRURHHjh1DXl4ehg0bhtDQUHz69KnG9u1X0blzZzg7O2PYsGG4ceMGzp49i3nz5mHevHlQVVUFUL7faiV9z02aNAmzZ8+GpqYmXr58ibS0tBrcu19T0TH/+jNTt25d6OrqcnXlmefrc5acnAw+nw85OTmsW7cODRs2hJubG4KCgkrsx60k9EuhnBITE4WSPQCgoKAACQkJJCYmVtkyP6vi9rXoS6m8+7p161Y8fvy41M69flbFHZ+i6ZKOz5s3bzB//nx4e3tDQkKi2mOsTRU5PtHR0VBXV0eHDh0QHh4OAEhISMCWLVswaNCg6g24hlXk+CgpKWHy5MmYNWsW/Pz8kJiYCCMjI3Tv3r3a4/0ZJCYmQkdHR6isrGP6O5s3bx6+fPmCyZMn13YohFTKypUrERoaihMnTnBl5fmO7d69O1atWgVxcXH8+++/GDZsGA4dOgQ+nw8nJyfUr18fCgoK+PLlC27fvg0rK6ua26mf3IoVK8AYw8yZM4utz8/PR1paWrHnKDU1FQUFBahbty5sbGwwa9YsODg44Pr161ixYgUmTpyIefPm4f79+5g0aRJUVVWho6ODO3fu/PL3VlUlOjoaAHD58mUMHjwYdevWxdu3b9GvXz/s3btX6HdNceeoqK5Tp06YM2cODh8+jICAAJiZmUFOTg4LFizA3bt3MWjQIHh7eyMzMxOzZs3CnDlzanZHf2JSUlKYPHkyJk6ciJCQEKSnp0NNTQ39+/fn5inPb7Xu3btj3bp1UFNTw6pVq9C9e3dcunQJnz59Qo8ePWBpaQlJSUnEx8fj8uXLaNq0ac3t5C8mMTERsrKyQg/IAeHPTHnm6datGzp16gQXFxdcvnwZHTt2RFRUFHbs2IEnT56gTZs2iIiIQGJiIjZt2oShQ4eWKz5qSVROEhISIr305+fnIz8/X+jJU2WX+VkVt69F0+XZ19OnT2P69OnYu3cvl9X+lRR3fIpGRynp+AwfPhw9evRAfHw8vL29udYNT548QURERPUGXMMqcnwkJCTw+vVrtG/fHiEhIQgJCcHChQsxcuRIvH//vtpjrkkVOT7Hjh3DH3/8gVu3biEgIACRkZFo3LgxWrVqRU+6UbFj+rtat24dtm3bhrNnz8LQ0LC2wyGkwvbu3YulS5fi2LFjaNCgAVdenu+D2bNno3Pnzti2bRvc3d3Rv39//P3339i5cydWrlwJd3d3BAYGYsKECVi8eHGN7dPPLigoCCtWrMDYsWPh4+MDb29vJCQkIDk5mUsYiImJgc/nF3uO+Hw+xMTEAADnz59HWloaTp8+jdOnT+Ply5f4/Pkzxo4di6lTp+LKlSsIDg5GVlYWTp06VRu7+1MqSqb5+fkhPDwcfn5+ePnyJTw8PLB+/XqheYo7R0WfIQMDA5w7dw7nz5/Hly9fcOnSJUyfPh1//vknoqOj8d9//yE4OBi+vr5YunQpPbD5Djdv3kT//v1x4sQJvHr1Cu/fv0ePHj3g4uLCjR5dnt9q48ePx5AhQ7Bt2za0bdsWkyZNwtSpU7Fnzx5s2LABrq6ueP36NRYuXIgFCxbU7E7+YiQkJJCTkyMyEuPXn5nyzNO0aVPs2LEDhw4dgpycHA4dOoSxY8di1apVuHfvHtLS0hASEoJr165h2rRp5R49kJJE5WRgYIDo6Gihk1Q0ra+vX2XL/KwMDAxEmocWTZe1r2fPnsWQIUOwc+dODBs2rNpirE0VOT5169ZFUFAQ5s6di7lz5+LQoUMAgGXLluH69evVG3ANq8jxKfqxOn78eK5s3LhxyM3NxZMnT6on0FpSkeNz5coVNGvWjGu6z+PxMGHCBERHR+P58+fVG/BPoDLfWb+TjRs34q+//sKFCxfQtm3b2g6HkArbv38//vzzTxw7dgw9e/YUqivP94G4uDgWLlyIa9euYe7cuZg+fTqmTJkCQ0NDBAYGwsXFBQDg6upa4mvARFRGRgbs7e2xbds27n4nICAAwcHBmDt3LuLi4sDj8aCnp1fsOfr6+1pXVxf79++Hh4cHnJycMGPGDOzZswefP39GSkoKnJycICYmBmdnZzpH36HofmvIkCGQlZUFAJiZmcHNzQ1eXl4ACj9DAMo8R23btsXFixdx8OBBBAUF4fXr15g2bRoCAwPh4OAAGRkZGBkZoW7duggODq6Bvfs1XLlyBQ0aNECrVq24sj///BPJycl4+PAhgPJ9z/H5fMyaNQvXr1/H4sWLsXDhQgwePBiWlpb0PVfFDAwMUFBQgLi4OK5MIBAgNjaWOx/lmQcA+vXrh8uXL2P79u3w8PCAtLQ0BgwYgMDAQLRo0QJiYmJwcnJCRkYG1zKwLJQkKqe2bdsiISEBjx494so8PDwgKyuL5s2bAyg8ad7e3tyJLM8yv4q2bdvi06dPePHiBVfm4eEBRUVFrilifn4+vL29ER8fz81z7tw5DBo0CNu3b8fIkSNrPO6a0rZtW0RERHB9GwCFx0dZWRlNmjQBUNjnR9ETtKJ6b29v7t/q1asBFF4Ivk6M/Aratm2L4OBghISEcGUeHh7Q0NCAjY0NACAnJwfe3t7ck6X27dsDEL4hKfq/hoZGTYVeI9q2bYuXL18KtSDz8PCAjo4OLCwsABQ+VfD29ubeS9bQ0BBJUkdFRXF1v5uMjAx4e3sjJSUFQOExffbsGWJiYrh5PDw8YGRkBBMTk9oKs9akp6fD29sbqampXNnmzZsxb948nD9/Hh06dKjF6AipnIMHD+KPP/7A0aNH0bdvX5H6tm3b4vXr13j37h1X5uHhAS0tLVhbW4vMf/36dYSFhWHKlCkASu7Hg5TN3t5e6F7H29sbbm5uaNq0Kby9vWFkZASg8BxduXKFu6YxxnD58uUSk9ezZs3CiBEjYGFhAQUFBeTn53OtaOkcfR99fX2Ym5sXm2Aoup+wtraGpqYmLl26xNWHhIQgKCio2HOUmZmJiRMnYs+ePRAXFxf6DDHGkJKSQufoO2hoaCAuLg55eXlc2bf3fOX5rfa1//77Dw8ePOBaDNH3XNVq2bIlpKSkhD4zDx48QHJyMveZKc88X4uLi8OSJUuwY8cOAMLnLCMjA7m5ueU/bxXq7vo31bdvX2ZsbMxOnDjBdu7cKTRkN2OFw1IDYHv27Cn3Mr+Srl27snr16rFTp06xbdu2CQ3ZzVjhyAcA2JEjRxhjjN24cYNJSEiwUaNGMS8vL+7f48ePa2sXqlXHjh2Zubk5O336NNuyZQuTlpZmmzdv5upjYmIYAHbixIlil/+VRzcTCASsdevWzNramp05c4Zt2LCBSUpKsl27dnHzvH//ngHghrRljLEBAwawRo0asfPnz7Nz586xRo0aMUdHx19u9K6CggLWvHlzZmdnx86dO8fWrFnDxMXFuaF/GWMsKCiIAWDXr19njBWOiCAtLc0GDx7Mrl+/zo4cOcKMjY1ZmzZtmEAgqK1dqTY+Pj7My8uLtWzZkrVq1Urku8TPz48BYJ6enowxxvLz81mTJk1YkyZN2Pnz59mqVauYuLg4O3XqVC3tQfX677//mJeXF2vWrBlzc3NjXl5e7MmTJ1y9r68vA8C8vLwYY4XDHQNgCxYsEPp+Lhp+mpCfxenTpxmfz2dTpkwR+lt+9uwZN49AIGAuLi7MxsaGnT17lq1bt45JSEiwffv2iawvLS2NmZiYCH0WNm3axGxtbdnly5eZo6MjW7ZsWY3s26+quNHN3r17x5SVldnQoUPZpUuX2JAhQ5iysjJ79+6dyPIPHjxgVlZWQqPVubq6sjFjxrCTJ08yJSUlblQtUj6XL19mioqKbOPGjezmzZtswoQJTEpKivn5+XHz7Nu3j0lKSrL169ezs2fPMmtra+bi4lLsPcf06dPZrFmzuOmYmBimoKDA9u7dy+bPn8/q1avHDflNyvb+/XumpKTEunfvzq5evcpOnjzJLC0tmb29PcvNzeXmK+u3WpGcnBxmaWnJ3RMwxtihQ4dYvXr12OXLl1nr1q3ZjBkzamTfflbBwcHMy8uLzZ8/n4mJiXHXnq9Hl1uyZAlTVFRku3fvZsePH2f6+vps4MCBQuspzzxF+vXrxzZt2sRNv3r1iikpKbETJ06wsWPHcqNBlgePsW9eciMlys3NxaZNm3Dnzh1ISUmhb9++GDJkCFeflZWFtm3bYu7cuejSpUu5lvmVZGdnY8OGDfD09ISMjAz69++PAQMGcPUpKSno3Lkz/v77b7Rr1w7btm0T6jiyiLKyMq5cuVKTodeIrKwsrF+/Hg8ePICsrCwGDBgAd3d3rj4xMRHdunXDkiVL4ObmJrK8j48PZs+ejatXr0JJSakmQ68RGRkZWLduHby8vCAvL48hQ4agV69eXH1sbCz69OmDVatWoWXLlgAKW19t374dN27cgKSkJJycnDB58mSuOfSvJC0tDWvWrMGjR4+gqKiIYcOGoVu3blx9ZGQkBg4ciHXr1nFPhN6+fYutW7ciNDQU8vLyaNGiBf744w+RDvB+Be3atUNmZqZQ2dffJWFhYRg+fDi2bNnCvYKXkpKCf//9F0+ePIGysjJGjRqFjh071njsNcHNzU1k9D81NTV4eHgAKBy9ZtSoUdixYwesra2xcOFCeHp6iqzH3Nwce/furZGYCakKq1evxuXLl0XKdXV1cfLkSW46IyMDa9aswX///QcFBQUMHToUPXr0EFnu4MGD+Pz5M2bPns2VCQQCrFmzBvfv34eTkxPmz59Po0hWwuLFi5Geno61a9cKlb99+xZr165FeHg4jI2NMXPmTJibm4ssP3r0aIwaNQpOTk5cWVxcHBYuXIjY2FiMGjWKBnGogPv372PXrl1ISEiAmZkZJk+eLHL8L168iMOHDyMtLQ3NmzfHrFmzICcnJzRPTEwMxo0bh5MnTwrdrz18+BAbN26ErKwsli5dCmNj4xrZr1/Fhw8fsHnzZgQFBUFaWhoODg6YOHEiFBQUuHnK+q1W5Ny5cwgICBAaXZkxhs2bN+PmzZuws7PD33//DSkpqRrZt5/RsmXLcPPmTZHyovusIgcPHsS5c+eQm5uLdu3aYdKkSSJ9Y5ZnntevX2P58uU4duyY0Mi9Hh4e2LdvHzQ1NbF8+XJoamqWK35KEhFCCCGEEEIIIYQQ6pOIEEIIIYQQQgghhFCSiBBCCCGEEEIIIYSAkkSEEEIIIYQQQgghBJQkIoQQQgghhBBCCCGgJBEhhBBCCCGEEEIIASWJCCGEEEIIIYQQQggoSUQIIYQQQgghhBBCQEkiQshPatOmTZg8eXJth1Hr1qxZg5kzZ9Z2GIQQQn4wN27cQEhISG2HwYmPj8fVq1dx8uRJ5OXl1XY4v6Tg4GDcvHmzRrb17t07XLt2rcz5nj59ijdv3tRARD+PgoICPHz4EKdOnUJISAhevnyJ+/fvl7pMeY93dalIzBXh7+8Pf3//Kl8v+T6UJCLkG+PGjYO9vT0OHz4sUrdkyRLY29tj1apVtRCZsPXr12PatGk1tr3c3Fzs3LkTPXr0QOvWrTFp0iSEhobW2Pa/FRUV9UPd/FZWRZNeERERCAsL+65l2rRpg1u3bn33tgghhJTu3r17OHnypMi/rKysat3utWvXRK7JM2fOrNUflV979eoVTE1NsWXLFly8eJGSRGUICgqq0HX68uXLmDNnTjVEJOr27duYPn16qfMkJCSgS5cukJCQqJGYSlLR41kdGGNo3749xo8fjwsXLuDdu3c4fvw4li9fXupy5TneFVGeY1PRmCuy7fz8fHTp0gXp6emVWjepHPHaDoCQH01wcDDCw8OxYcMGDB06lCvPysrChg0bwOfzERERUYsRFoqMjPzu5EBljB49Gurq6hg7diwkJCSwb98+ODg4wM/PD4aGhjUWx6+qokmv2bNnf/fNtr+/PxITE797W4QQQkq3dOlSREREoGnTpkLl7dq1g4yMTLVtd/r06Zg4cSLMzMy4so4dO6J+/frVts3vcfz4cTg6OuLGjRu1HcpPwcPDA2fPnkW7du2+azlzc3N06NChmqL6fv/88w/atm0r9HdZGyp6PKtDaGgo7t69i+joaNStWxcAkJycDAUFhVqJpzzHprpiLm7b9vb2aNCgAbZs2YJ58+ZVav2k4ihJREgxunTpAg8PD7x48QKNGjUCAJw5cwZGRkaQk5MTmf/cuXM4cuQIEhMTYW5ujjlz5sDExAQAEBISgoEDBwIApKSkYGpqiilTpnDrBYBBgwbBzc0Nnz9/xoMHDyAuLo6RI0eiZ8+exca3f/9+nDhxAjk5ObC3twcAbNy4ES1atMCXL1+wevVqPHr0CLKysujevTvGjx8PPr+w4eCCBQsgLS0NVVVV3LhxA5mZmRg0aBBGjhxZ6jHZvXs3pKWluWlXV1fIycnh9u3bGDNmTLHLrF+/HlFRUWjcuDGOHj2KgoIC3L59G5MnT4aPjw94PB40NTXh6uqKyZMnc0+abt26hX///RfLly/H9u3bERkZCVtbWyxevBjKysrFbisxMREjRoxAw4YNsWzZMpH6iIgI9O7dG9euXUOdOnW4cnt7e2zfvh0ODg549OgRpk2bhl27dmHjxo14//49GjRogCVLlkBDQ4Nb5sOHD1i/fj38/Pygo6ODKVOmwMnJiav38PDAoUOHkJCQgHr16mH27NmoV68eAMDb2xtz5szBpk2bsHr1akRFRaFnz544evQosrKyuPO5bt06uLi4cNNiYmIwMDDA0KFD0aVLF25bp06dQlxcHNauXQugMJnXqFEjZGVl4d69ewCAwYMHY8CAAQCAPn36ICUlBQsWLMDatWuhoKCA7du3Y+jQobh06RJ38QeA8+fPY/v27bh16xb391Mcb29vBAUFISUlRah8ypQptf70kBBCapqLiwsOHjwoUn758mVYWlpCWloaz549Q926ddGkSRPcv38fsbGx3DXRzs4OSkpKIsvn5+fD19cXCQkJaNy4MbS1tQEAd+/eRVpaGl68eIGTJ08CANzd3eHm5gZjY2OhdWRkZOC///5DVlYWmjRpwq3j2xgVFBTg5+cHGRkZODo6lvldXtp6b968iSdPniA7OxsnT55E3bp14eLiUux64uPj8ejRI2hqasLW1hZeXl7Q0dGBhYVFqccQKHzY8vz5c8jLy6NZs2aQlZXl1hsXFwdPT0/079+fK8vKyoKHhwc6d+4MBQUFJCcn48aNG+jZsyfCw8MRGhqKevXqwdzcXCjGnJwcPH78GBkZGbCzsxO6bn6taH29evXCmzdv8O7dOzg5OXHHJiwsDIGBgdDU1ESjRo0gJSUFAAgPD8fLly+RlJTEnU8HBweIiYnh0aNHAAA5OTk0aNCAu9csYmZmJnSuAgMDERcXh5YtWyIgIADx8fFo3LgxNDU1ReItKZ4iBQUF8Pb2RnZ2NmxtbYvd569lZ2dj3759OH36tEg8LVq0gL+/Pz5//gxHR0doaGggOzsb//33H/Lz8+Hk5ARFRUWRdT5//hwRERHQ09PjzvvXYmNj4e/vD1lZWdjb20NWVrbE4/ntZ6NIeHg4Xr16BV1dXTRq1Ag8Hq/cMZR1vENCQnDixAkAwJ07d7hzxePx0Lx5c6F1lfd4l3beyoqnPMemvDF/fW59fHyQkJCAvn37Vui8DBkyBAsWLMDcuXNFjj+pIYwQIsTFxYWNGzeOjR07lv35559cubOzM9uyZQtr3rw5GzduHFe+YsUKZmRkxA4dOsQePHjA5s2bxxQUFFhERARjjLHMzEzm6+vLfH19mZeXF1u0aBGTkpJiQUFB3DpsbGyYtLQ0mzNnDvP09GT//vsv4/F47PHjx8XGGB0dzQYMGMBatGjBrTs5OZnl5+czGxsb1rx5c3b16lV2+PBhpqGhwWbMmMEt6+7uzqSkpFinTp3YzZs32bZt25icnBzbtWvXdx2n+/fvMz6fz54+fVriPFOmTGFSUlKsQ4cO7MaNG8zf358xxlhISAjz9fVlT58+ZWfPnmXW1tZs9OjR3HInTpxgYmJizM7Ojp0/f57duHGD2djYsJ49e3LzzJgxg7Vv354xxtjHjx+ZpaUlGzBgAMvNzS02lqCgIAaARUVFCZUDYLdv32aMMXb9+nUGgFlZWbFTp06x27dvM0dHR+bq6srN//btW6aiosL69u3Lrl27xk6dOsVatGjBEhISGGOMrVu3junr67P9+/ezBw8esL///pvJycmx0NBQxhhjly9fZjwej5mbm7OTJ08yX19fFh4ezgYPHsycnJy485mUlMQYY9y0j48P27ZtG1NUVGRnz57l4vnzzz9Z9+7duenmzZszKSkpNnXqVObp6ck2bdrE+Hw+u3v3LmOMscDAQKakpMRWrFjBfH19mZ+fH2OMsXr16rHVq1cLHZtWrVoJ/a1/Ky0tjbVs2ZLp6+uz0aNHs+bNmzMATEdHh7m5uZW4HCGE/KpcXFzYsGHDiq0zMDBgbm5uzMDAgHXv3p277i5fvpy5u7uzfv36saZNmzJVVVV269YtoWVfv37NzMzMmIGBAevUqRMzMjLill+0aBFTUFBgjRo1Yu7u7szd3Z0VFBQwS0tLtmHDBm4d3t7eTF1dndnY2DBXV1cmIyPD1q1bJxJjmzZtmLGxMevSpQvT1dVl9vb2LDs7u8R9Lmu9c+bMYcbGxkxfX5+5u7uzVatWFbueGzduMDk5OWZvb89cXFyYlZUVMzQ0FJq/pGO4YsUKJiMjw9zc3JiVlRXT1NQUuj+5ffs2+/ZnT1RUFAPA3Y/5+fkxAKxjx47M3NyctWnThklLS7O///6bWyY0NJTp6OgwW1tb1rVrV2ZkZMTWr19f7P4Ura9z587MysqK9evXj7148YLl5eWxYcOGMQ0NDdalSxdma2vLzMzM2Js3bxhjjN27d481bNiQqaiocOfz3r17zMvLi5vu1KkTU1BQYBMnThTa5po1a5iNjQ03vWjRImZsbMxsbW2Zm5sbc3JyYvLy8uzBgwfcPGXFwxhjqamprGnTpkxLS4t17NiRaWtrs9atW7P69esXu+9Fx1xCQoJlZmYKxWNoaMhMTU1Zu3btmK2tLVNQUGAHDx5kZmZmrF27dszKyorp6emx6OhobrmMjAzm5ubGNDQ0WMeOHZmmpiZzcXFhqamp3DwHDhxg8vLyrE2bNszNzY3Vr1+fPXnypMTj+a3c3Fw2dOhQJicnx9q0acPs7e1Zu3btuPvK8sRQ1vG+du0ac3NzYwBY3759uXgsLCyE7pvKc7zLc97Kiqc8x6a8MRdty9ramrVu3ZoNHDiwwufl06dPDAB3j0pqHiWJCPlGUZLoyZMnTFlZmWVlZbGQkBAmLS3NEhMThZJEnz9/ZpKSkiKJki5durDp06eXuI1+/foJ1dvY2LDevXsLzdO8eXO2YMGCEtcxZcoU1rlzZ6GyI0eOMDk5Ofblyxeu7Ny5c0xCQoK72Lq7uzMNDQ2WlZXFzbN69WpWt25dJhAIStweY4w9efKENW7cmJmZmTFlZWV28eLFUuefMmUKU1VVZRkZGaXO9+LFC8bn87mYTpw4wQBwiRXGCpMrkpKSrKCggDH2vyRRcHAwMzAwYBMmTODqivM9SSJfX1+u3svLiwFgaWlpjDHG+vTpw1q0aCG0jvz8fJaXl8eSkpKYjIwMe/jwoVB9nz592IQJE7j9AMC8vb2F5vk66VWalStXstatW3PTxSWJvl1P+/bt2ZQpU7hpNTU1duLECaF5Vq9ezczNzbnpd+/eMR6Px548eVJiLMOHD2e2trbcDVJBQQGzs7NjQ4cOLXM/yuPRo0ds27ZtVbIuQgipCS4uLszZ2ZmdOHGC+1f0fW9gYMBMTU25hwAl2bRpEzM0NOSm8/PzmZmZGevbty/3gzU3N5ddu3aNm6d+/fpsy5YtQuv5OkmUm5vLzMzM2Pjx47n6s2fPMjExMfb69WuuzMDAgFlbW3PXvKSkJKaiosIOHz5cbKzlXe+oUaOYu7t7ifucm5vLDA0NhR5q7dmzhwEQSRJ9ewz9/PwYn89nN27cYIwxJhAI2NChQ5mVlRXLz89njH1fkqhnz57c/cTt27cZj8djAQEBjDHGpk6dyjp27MitIy8vj126dKnYfSpa3+jRo4Xur1auXCl07Sxab/PmzbnpVatWscaNG5d4vBhjLCIigikpKbH79+9zZcUliXg8ntAP/+HDhwvdR5QnnoULFzJTU1OWmJjIGCs8dmpqaqUmiVavXs3q1asnVFYUT1GSQiAQMCcnJyYmJsY9GC0oKGANGzZkCxcu5JZbvHgx09fXZ3FxcYwxxuLj45mhoSGbN28eN4+hoSHbvXs3Nx0TE8N8fHwYY+U7nkuWLGHq6upC9563bt3i7mHLE0N5jnfRfeXX9+Fz5swRSriU53iX57yVJ57yHJvyxLxo0SIGgF2+fFlo2YqeFxUVFaHlSM2i180IKYGDgwN0dXVx4cIF+Pv7o2fPnlBRURGax8fHB7m5uZg0aRKAwo7dGGOIjIyEQCDg5rt48SJOnjyJqKgo5OTk4OPHj0KvJwHgXisqoqOjg7i4uO+K+fnz57C3t4eqqipX1qFDB+Tl5eHVq1dck+jmzZsLvTrWtm1bzJkzB9HR0dDR0Slx/RYWFti5cye+fPmCffv2YcKECbC2ti6xyS4A2NjYCDX5BoBPnz5h06ZNCAgIQGJiIvLy8iAQCBAREcH1n6CkpARTU1Oh45Gbm4ukpCSoqakBKGwm26JFC4wfPx5Lly79jiNVMj6fDzs7O6HtAoVN1eXl5fHw4UPMnj1baBkxMTEAwJMnT5CVlYUZM2aAz+dzfw8fP37kmsoXbcPBwaFc8Tx+/Bi7du1CeHg4MjIykJiYCMZYqctU5G9p+PDh+Ouvv+Dj44NmzZrhwIEDaNCgQYlxJiYm4ujRo7hx4wb3Tjqfz0fjxo3x/v37cu1bWWJjY/Hy5csqWRchhNSUyMhIXLx4kZt2dHTkXssYMmRIsa9Nf/r0Ca9fv0ZSUhJ4PB4+fPiA+Ph4aGhowMfHB6Ghobh+/Tr3qoeEhAQ6duxY7phevHjB9StSpHfv3jAzM8PZs2fx999/c+WDBw+GvLw8AEBZWRk2NjYIDg6u9HpL8+zZM3z48AGzZs3iykaOHFlsJ8zfHsNTp07B1tYW7du3B1D4Gsz8+fNhbm6O169fo2HDhuWKoUjRNRwoHOjBzs4OZ86cQcOGDSEjI4O4uDh8/vwZderUgbi4OLp27Vrq+iZNmiT0ysyBAwfQrFkz3Lx5k7tPUFNTw6NHj5CdnS10f/at7OxsPH/+HDExMcjPz4e2tjaePn1a4ut7QGE/Ra6urtx0q1atsGDBgu+K5/Tp0xg9ejR3H6yrq4tBgwaVOpJaQkKCyH0zUHgv6ezsDKDwXDVt2hQ5OTlcP15F90hf99N48uRJjB49musuQF1dHePHj8euXbuwcuVKAICMjAyCgoKQl5cHCQkJaGlpQUtLq8T4vnXo0CGMHz9e6N6zbdu23xUDUPbxLo/yHO/y/h1VRTzlZWRkJNQlAlDx86KiooKEhIRqiZOUjZJEhJRi1KhR2LVrF4KDg3H06FGR+oyMDPD5fGzcuBHi4sIfp6J3qQ8fPoxJkyZh+fLlsLKygoKCAtauXYvk5GSh+b9dnsfjlZkM+FZaWppIn0nS0tIQFxdHWloaV/Zt0qZombS0NJw/f17oYrdlyxYuoaWgoMAlINq1a4cGDRpg48aN2Lx5c4kxfbutrKwsODs7w8bGBhMnToSGhgaSk5PRsWNHodFfijseAISOiZiYGHg8XpWOgMDj8bikT3HbzcrK4m6ev5WRkQGgsC+mb2/yvu7cT0JColx99Tx79gyurq6YOXMmBg0aBCUlJVy5cgW7du0qdbmK/C3VqVMHXbt2xf79++Ho6IhDhw6VOnrezZs3wefzRW5MP336BF1d3TL2jBBCfl0l9UkEoNj+a+bOnYstW7agSZMm0NDQ4AYj+Pz5MzQ0NBAZGQlxcfFSH8iUJSIiAuLi4tDT0xMqNzExERmM4+sHTUBhf4rZ2dmVXm9poqKiIC0tLdRXDp/Ph76+vsi83x7DiIgIkWNTNB0REfHdSaJvB+MwMjLi9mXGjBl4+/YtDAwMYGNjg/bt23P3MiX5Nt4PHz5AXV0dZ8+eFSrv27cvMjMzS0wS/ffff+jVqxfU1dVhYmICWVlZJCUl4fPnz6XuT1nnszzxREZGFntcSiMvL8/dF33t28SRlJRUsWXx8fHcdHHn2MTEBJGRkcFYreYAAA6DSURBVGCMgcfjYe/evRg/fjzU1dXRsmVL9OzZE8OHDxe6pytNZGQk139kccoTA/B9n5/SYinreJf376gq4imv4r7fKnpeMjIyaq0zb0JJIkJKNXjwYMyZMwfa2tpo3bq1SL2ZmRnXYujb1htFLly4gJEjR3KtjQBwTworo6ilytdMTU1x7949oYtVcHAw8vPzhTo3fPv2rdByQUFB4PP5MDQ0hJqaGnbu3MnVlXTB5PF40NDQwJcvX74r7pcvX+L9+/cIDAzkRnrx9PT8rnUUMTAwwNmzZ7knJOvXry9x3qJEWGZmJlcWExPz3dusV68eAgICiq0rGr0jPz+/xL+HkhR3Pq9evQpHR0ehjri/vRmoiOK2BRR2eu3u7o5OnTohNjYWgwcPLnEd0dHRUFJSEkpIJSYmwtPTk+uA8NKlS0hLS0NoaChyc3PRsWNHrF69GhISErC2tsasWbOgoKCAU6dOgcfjISAgABEREZgwYQKaNWsmtL2wsDCsW7cOK1asELnhIYSQn8W31/6QkBCsXr0aAQEBXDIjMDAQHh4e3Pe0srIy8vPzkZKSUuLgDWVRV1dHfn4+0tLShH54JSYmwtLSsmI7U4XrVVVVRXZ2tkhLmqSkJJF5vz2G6urqeP36tVBZ0XLq6uoAwLUMEggE3P9L+qGclJQk1Ko6KSmJS1apqanh/PnzSE1Nhbe3N9avX48jR44gJCRE5AFNSfEqKiqiR48eIq2SyzJr1iwMHjwY69at48rs7e2/+4Hit8oTj5qamsi5KO7cfK1evXqIiIgQOuYVpa6uLjIqa2JiItTU1Ljj26xZM7x8+RJRUVG4desWFi9ejICAgFIfZH5NWVm51Hva8sRQVcpzvCv6d1SdijsOFTkv6enpSEhI+GFGZ/wdVe4TS8gvTl1dHX5+frh3716xX3xNmjSBk5MTJk2aJJRwuHnzJq5evQqg8MYnICCAezJ44cIF3L59u9KxaWlp4ePHj0JlgwcPRmxsLHbs2AEAyMvLw7x58+Dg4AAbGxtuvhcvXuDMmTMAgNTUVKxYsQIDBw6EtLQ0NDQ0YG9vz/1TVFREbm4uVq5ciZycHG4dHh4eePToETp37vxdcauqqoIxhufPnwMovOhVptmrpaUl7t27h6NHj2LGjBklzqejowM1NTVuvwsKCrB48eLv3t6ECRNw8OBB3L9/H0BhC6P9+/cjMTER1tbWaNWqFaZOnSp0bu7evQsPD49S11vc+VRVVUVYWBjX6iwgIKDMVkTloaWlhaioKJHy9u3bQ0VFBePGjUPXrl1LfTJqYGCA+Ph47jzm5ORwI6sV/U2Eh4dj/vz50NLSQpcuXWBiYoLx48dj2LBhSElJwcyZMwEUDq26cOFCGBsbo2nTpujXr5/QTe+NGzcwduxYzJ07lxJEhJBfSmxsLPh8vtAQ4d8+DGjevDlkZWVx+PBhofKvW1rIy8uX2jrA1tYW8vLyOH/+PFcWEREBX19ftGjRosLxV9V67ezsICMjg0uXLnFlRQ8OylI0mlJsbCxXdubMGSgrK8PKygrA/14dDwsL4+Yp6QHV168KxsbGwsfHh3td8NOnTwAKf6B36tQJa9aswfv378tszfO1Dh06YP/+/cjNzRUqL1o3UPz5jI2NFfrRHBoaWiWvZJcnnhYtWggdF4FAIDRdnFatWiEjIwOvXr2qdIwtWrQQ+hsDCj8nX/+NFcWrp6eHUaNGYeTIkXj8+DGAsj8fQGEL+WPHjgl1F5GcnMzdv5cnhqpSnuNdnvNWHuU5NpVRkfPy+PFjSEpKioz4RmoOtSQipAwNGjQotf7ChQsYNWoUjIyMYGhoiLi4ODRr1gybNm0CAMyfPx8dOnSArq4u15rFzc2t0nG5u7tj8+bNMDIygpqaGjZu3IgWLVrg0KFD+OOPP7B69WqkpaVBV1cXZ86cEUpytWrVCsuWLcPcuXPx+fNnmJubY82aNSVuS0JCAvn5+dDV1YW6ujpSU1ORl5eHf//9FwMHDvyuuM3MzDBnzhy4ubnBxMQEHz9+RJ8+fbhhXSvCysoK9+7dQ+vWrcHj8bjh4L/G5/OxY8cOjBgxAgcPHkRaWho3LPz3GDlyJGJiYtClSxeoq6sjMzMT3bp1w5AhQwAU3piOGTMGJiYmMDIywufPn9GkSZMyn2T17dsXGzZsgKGhIdTV1bFu3TqMHDkSZ86cgYGBAerWrYsvX76gTZs28Pb2/u64vzZp0iRMmTIFx44dg6qqKnejzOfzMWLECCxZsgQjR44sdR3dunWDo6MjXF1d4ejoiKCgIGhqasLDw0PoaeqgQYMwfvx4AIU3uN7e3ggNDUVWVpbQzf+oUaMwatQoAMC+ffu4p3m3bt3C8+fP8eDBA5FXFwkh5GdXNJR9nz590Lt3b6Fh7IsoKytj8+bN+OOPPxASEgJbW1s8ffoU4uLi2L59O4DCFiVHjhyBhoYGpKSk4O7uLrQOVVVVLFmyBBMmTMCHDx+gqqqKzZs3o3Xr1mX2qVOaqlqvmpoaZs+ejTFjxiAsLAxycnLYvHkzFBUVy2yl0bdvX2zfvh2tW7fGn3/+idjYWKxZswYbNmzgWjfVq1cPTZs2xeDBgzF27Fh8+PABx44dK3Z927ZtQ1paGgwMDLB9+3bY2dmhV69eAIB//vkH7969Q7t27SAnJ8f1C1M0rH15rF69Gi1btkTTpk0xbNgwiIuLw9vbG1lZWdwDJXt7e7x9+xbr1q2Djo4OHBwc0KNHDyxZsgQ5OTnIy8vDhg0bquS6WJ54Fi1aBHt7ewwYMACurq64ePEioqOjuT4ii6OlpYWuXbvixIkTQg8qK2LZsmVo0qQJevfujQ4dOuD27dt48uQJnjx5ws3j6uqK1q1bw97eHsnJydixYwfXx1Vxx/PbV8dWrVqF5s2bw8XFBQMGDEBycjJOnjyJx48fQ0JColwxVJXyHO/ynLfyKM+xqYyKnJdTp05hwIABdN9XiyhJRMg3du3axb0GVZx9+/YJ1WtqauLKlSv48uULoqOjYWhoKNTk2sTEBG/fvsW7d+/A4/FgamqKT58+CfW/c/z4cZEL7cqVK0t9X9fAwADv37/H+/fvkZqayj2F7Nu3L3r06IGQkBDIyMgU+0VvYmICT09PfPz4EZmZmaW+gw0UNh/9+++/sWDBAu7mTVtbu8zmwzNmzBBqfVTkn3/+wcyZMxEdHQ0DAwPIyclhwoQJ3BOy9u3bC3WCCQD169eHr68v19R+6tSpQsfQysoKAQEB+PTpE3JyciAlJSWy3b59+6Jz586IiIiAgYEBZGVlMWjQIG67zZo1E7nYa2trw9fXV6i/hQULFmD69Ol49+4ddHV1hZr/q6ur48KFC0hMTMSnT59gYGDA9U8FAC1btoSPj49IbHp6eggPD+fOp6mpKeTk5PDw4UO8f/8eWVlZMDU1RXp6ulAroNmzZ3NPuYDCv89v3+FeuHAhCgoKuOkxY8agb9++Ih2sA4U3/Nra2ujQoYNIjF+TlJTEw4cPcefOHcTExMDU1BQtW7Ystll9kalTp0JbWxuDBw9GUlKSUGfjX98IiIuLIz8/H0BhQjMnJwdr1qzBokWLSo2JEEJqW+vWrUtshdm1a1ehV7+BwlehHz16hG3btuH+/fswMTGBt7c3Fi9eLHRtGTVqFGxsbHDixAk8fvwYTk5OGD58OFe/evVq7NixA56ensjOzka/fv3QsWNHoZYn06dPh6WlJTw8PLhOor9eR0kxtmrVSqTPoa+VZ70ODg5ltlZYtGgR6tWrh9u3b0NTUxOnT59G//79ha5pxcXH4/Fw4//au3+XVNs4juOfKBpSIYdoCDKQUIiSsMC5paVNyKjoL2hoM8GxxabAIcQKSiJocAiju4KWLPyRpNAShP0YE6IWiQZ7hjjy2OkcPNTzdM7p/RrlRr9e93B/+VzXfV2GoZWVFWUyGZlMJm1vb9dMyDU0NGh3d1fhcFipVEoOh0MHBwcKBAI1zynpZYVRPB5XNpvVxMREzcbT4XBYhmHIMAyVy2VNTU1VJ4les1qt8vl83/UjHR0dKhQKWltbUy6Xk8VikdfrldfrrV7j8Xi0ubmp/f19nZycqL29XfPz83I4HEqn0zKZTIrFYkqlUjX3xul01jy/v62k+jebzVYNveqtx+l0KpvNKhKJ6PT0VKOjo5qZmfnpxtWSFAwGNTIyomAwKLPZ/GY9Lpfru77F7XbX7MFjt9uVz+cVjUZ1eHio7u5uhUKhmmu+/Yd0Oq2Wlhatrq5WN3d/azxf98ednZ0qFApaXl5WJpORzWbTzs5OtT+pp4Z6xrutrU0+n6+mx389BvWMdz33rZ566hmbemp+67ekX78vFotF8Xj8zV4Z/5+G5/e+yArgjzI2Niaz2aylpaXPLgW/maenJ/X19WlyclLBYPDd37ewsKDHx0fNzs5KemkWNzY2ZLfb1dzcrLOzM11dXWlubk6tra2anp6W9DKzlEgklEqlZBiGFhcXFQgEVCqVFIlEfrjvAwDgz3V3d1fzSvH5+bl6enp0dHRUPfnqv5TP59Xf369SqVTdywjvFwqFNDAw8CGr6PH329ra0s3NTbUnxOcgJAK+GEIivMXv92t9fV1Wq1XHx8cfcqLE5eWlKpVKzaxvLpdTpVKRy+VSMpnU0NCQLi4u1NTUVD3JI5lManBwUPf397q9vVVvb2/1866uLk5PA4C/UCwWUzwe1/DwsB4eHhQOh+V2u3/p1Zn3ICQCgBeERMAXUywW1djYKJvN9tml4DdSLBZVLpfldDpZqQMA+BSJREJ7e3t6fn6Wx+PR+Pj4h58c9SPX19fy+/2KRqMcvQ3gSyMkAgAAAAAAgH6+6ywAAAAAAAC+BEIiAAAAAAAAEBIBAAAAAACAkAgAAAAAAAAiJAIAAAAAAIAIiQAAAAAAACBCIgAAAAAAAIiQCAAAAAAAACIkAgAAAAAAgKR/AHDrWvKuszksAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1150x420 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Spearman r_s (top-3 uncertainty vs NDCG@3): -0.503\n",
+      "Selective gain (top 30% vs all): +0.021\n"
+     ]
     }
-   },
-   "outputs": [],
+   ],
    "source": [
-    "def precision_score(y_true_group, y_pred_group, k=3):\n",
-    "    \"\"\"\n",
-    "    Calculate the precision score for a given group.\n",
-    "\n",
-    "    Parameters:\n",
-    "    y_true_group (pd.Series): True target values for the group.\n",
-    "    y_pred_group (pd.Series): Predicted target values for the group.\n",
-    "    k (int): Number of top elements to consider.\n",
-    "\n",
-    "    Returns:\n",
-    "    float: Precision score.\n",
-    "    \"\"\"\n",
-    "    top_k_pred = y_pred_group.nlargest(k).index\n",
-    "    top_k_true = y_true_group.nlargest(k).index\n",
-    "    true_positives = len(set(top_k_pred).intersection(set(top_k_true)))\n",
-    "    return true_positives / k"
+    "group_ids = X[ranking_groups].values\n",
+    "unique_groups = np.unique(group_ids)\n",
+    "cv_folds = GroupKFold(n_splits=5)\n",
+    "oof_scores = np.full(len(X), np.nan)\n",
+    "oof_ranks = np.full(len(X), np.nan)\n",
+    "oof_uncertainty = np.full(len(X), np.nan)\n",
+    "oof_score_ensembles = np.full((len(X), 10), np.nan)  # Store raw scores for topk analysis\n",
+    "\n",
+    "for train_idx, test_idx in cv_folds.split(X_features, y, groups=group_ids):\n",
+    "    fold_model = CatboostRankerMother(**RANKER_KWARGS)\n",
+    "    fold_model.fit(X=X_features.iloc[train_idx], y=y.iloc[train_idx], group_id=group_ids[train_idx])\n",
+    "    oof_scores[test_idx] = ranker_predict_for_groups(\n",
+    "        fold_model, X_features.iloc[test_idx], group_ids[test_idx], use_ranks=False\n",
+    "    )\n",
+    "    oof_ranks[test_idx] = ranker_predict_for_groups(\n",
+    "        fold_model, X_features.iloc[test_idx], group_ids[test_idx], use_ranks=True\n",
+    "    )\n",
+    "    oof_unc = ranker_predict_uncertainty_for_groups(\n",
+    "        fold_model, X_features.iloc[test_idx], group_ids[test_idx], n_ensembles=10, use_ranks=True\n",
+    "    )\n",
+    "    oof_uncertainty[test_idx] = oof_unc[\"knowledge_uncertainty\"].to_numpy()\n",
+    "\n",
+    "    # Get raw score ensembles for topk stability analysis\n",
+    "    _, fold_score_ensembles = fold_model.predict_uncertainty(X_features.iloc[test_idx], n_ensembles=10, return_raw=True)\n",
+    "    oof_score_ensembles[test_idx] = fold_score_ensembles\n",
+    "\n",
+    "# Per-group NDCG@k and mean top-k uncertainty\n",
+    "group_stats = []\n",
+    "for gid in unique_groups:\n",
+    "    mask = group_ids == gid\n",
+    "    topk_mask = mask & (oof_ranks <= TOP_K)\n",
+    "    group_stats.append(\n",
+    "        {\n",
+    "            \"group\": gid,\n",
+    "            \"ndcg_at_k\": np.clip(ndcg_score([y.to_numpy()[mask]], [oof_scores[mask]], k=TOP_K), 0, 1),\n",
+    "            \"mean_topk_rank_uncertainty\": oof_uncertainty[topk_mask].mean(),\n",
+    "            \"decision_regime\": data.loc[mask, \"regime\"].iloc[0],\n",
+    "        }\n",
+    "    )\n",
+    "stats_df = pd.DataFrame(group_stats)\n",
+    "\n",
+    "# Selective-ranking curve: retain groups from lowest to highest uncertainty\n",
+    "stats_sorted = stats_df.sort_values(\"mean_topk_rank_uncertainty\").reset_index(drop=True)\n",
+    "fractions = np.arange(0.1, 1.01, 0.1)\n",
+    "coverage_df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"coverage\": fractions,\n",
+    "        \"mean_ndcg_at_k\": [\n",
+    "            stats_sorted.iloc[: max(1, round(f * len(stats_sorted)))][\"ndcg_at_k\"].mean() for f in fractions\n",
+    "        ],\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "PALETTE = {\"separated\": \"#0072B2\", \"near_tie\": \"#D55E00\"}\n",
+    "correlation = stats_df[\"mean_topk_rank_uncertainty\"].corr(stats_df[\"ndcg_at_k\"], method=\"spearman\")\n",
+    "all_ndcg = stats_df[\"ndcg_at_k\"].mean()\n",
+    "top30_ndcg = coverage_df.loc[np.isclose(coverage_df[\"coverage\"], 0.3), \"mean_ndcg_at_k\"].iloc[0]\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(11.5, 4.2), layout=\"constrained\")\n",
+    "\n",
+    "for regime, subset in stats_df.groupby(\"decision_regime\", observed=True):\n",
+    "    axes[0].scatter(\n",
+    "        subset[\"mean_topk_rank_uncertainty\"],\n",
+    "        subset[\"ndcg_at_k\"],\n",
+    "        s=32,\n",
+    "        alpha=0.78,\n",
+    "        color=PALETTE[regime],\n",
+    "        edgecolors=\"white\",\n",
+    "        linewidths=0.45,\n",
+    "        label=\"Near-tie\" if regime == \"near_tie\" else \"Separated\",\n",
+    "    )\n",
+    "axes[0].set(\n",
+    "    xlabel=rf\"Mean top-{TOP_K} rank uncertainty $\\sigma_{{\\mathrm{{rank}}}}$\",\n",
+    "    ylabel=rf\"Out-of-fold NDCG@{TOP_K}\",\n",
+    ")\n",
+    "axes[0].legend(frameon=False, loc=\"lower left\")\n",
+    "axes[0].text(\n",
+    "    0.98, 0.04, rf\"Spearman $r_s={correlation:.2f}$\", transform=axes[0].transAxes, ha=\"right\", va=\"bottom\", fontsize=10\n",
+    ")\n",
+    "\n",
+    "axes[1].plot(coverage_df[\"coverage\"], coverage_df[\"mean_ndcg_at_k\"], \"o-\", color=\"#009E73\", lw=2.2, ms=5.5)\n",
+    "axes[1].axhline(all_ndcg, color=\"0.3\", lw=1.1, ls=\"--\")\n",
+    "axes[1].scatter([0.3], [top30_ndcg], s=75, color=\"#009E73\", edgecolor=\"white\", linewidth=1.2, zorder=3)\n",
+    "axes[1].annotate(\n",
+    "    f\"30% retained\\nNDCG@{TOP_K} {top30_ndcg:.3f}\",\n",
+    "    xy=(0.3, top30_ndcg),\n",
+    "    xytext=(0.44, top30_ndcg - 0.007),\n",
+    "    arrowprops={\"arrowstyle\": \"-\", \"color\": \"0.35\"},\n",
+    "    fontsize=9,\n",
+    ")\n",
+    "axes[1].set(\n",
+    "    xlabel=\"Fraction of groups retained (most confident first)\", ylabel=rf\"Mean NDCG@{TOP_K}\", xlim=(0.07, 1.03)\n",
+    ")\n",
+    "axes[1].set_xticks(np.arange(0.2, 1.1, 0.2), [f\"{v:.0%}\" for v in np.arange(0.2, 1.1, 0.2)])\n",
+    "axes[1].text(\n",
+    "    0.98, 0.04, f\"All groups: {all_ndcg:.3f}\", transform=axes[1].transAxes, ha=\"right\", va=\"bottom\", fontsize=9\n",
+    ")\n",
+    "\n",
+    "for ax, label in zip(axes, [\"A\", \"B\"]):\n",
+    "    ax.set_title(label, loc=\"left\", fontweight=\"bold\")\n",
+    "    ax.grid(axis=\"y\", alpha=0.25)\n",
+    "    ax.grid(axis=\"x\", visible=False)\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"Spearman r_s (top-{TOP_K} uncertainty vs NDCG@{TOP_K}): {correlation:.3f}\")\n",
+    "print(f\"Selective gain (top 30% vs all): {top30_ndcg - all_ndcg:+.3f}\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Simplest Case: Fit and Predict on the Same Data\n",
+    "## 4b. Groupwise Top-k Stability on Out-of-Fold Predictions\n",
+    "\n",
+    "This section builds the out-of-fold uncertainty table and applies `groupwise_topk_analysis` to identify which **groups** have unstable top-$k$ boundaries. This is the practical screening step: before committing synthesis resources, flag groups where the top-$k$ membership is ambiguous.\n",
     "\n",
-    "Training and testing on the same data using `fit` and `predict` is quick but unreliable. It risks overfitting and provides overly optimistic metrics, failing to reflect real-world performance. Cross-validation or a separate test set is recommended for robust evaluation.\n"
+    "`topk_disagreement_prob` is a true disagreement probability: it is zero when all ensembles agree about membership, whether an item is always in or always out of the top-$k$. Higher values indicate unstable membership. A negative Spearman correlation with NDCG is therefore expected when disagreement is associated with lower ranking quality."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-17T16:11:22.021007Z",
-     "start_time": "2025-09-17T16:11:05.373Z"
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "High-risk groups (unstable top-3 selection): 120/120\n",
+      "\n",
+      "Risk indicators:\n",
+      "  • min_topk_disagreement_prob > 0.3: at least one top-3 candidate has ensemble disagreement\n",
+      "  • n_bubble_candidates > 0: compounds just outside top-3 could swap in\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1150x420 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Key insight: mean top-k disagreement correlates with NDCG@3 (r_s=-0.26)\n",
+      "Groups with higher disagreement have genuinely worse ranking quality, indicating real instability.\n"
+     ]
     }
-   },
-   "outputs": [],
+   ],
    "source": [
-    "# Fit the model to the data\n",
-    "mother_cat_rank_pipeline.fit(\n",
-    "    X=X.drop(columns=[ranking_groups, \"tanimoto-group\", \"smiles\"]), y=y, group_id=X[ranking_groups]\n",
+    "# Build OOF uncertainty DataFrame\n",
+    "oof_uncertainty_df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"mean_predictions\": oof_scores,\n",
+    "        \"knowledge_uncertainty\": oof_uncertainty,\n",
+    "    },\n",
+    "    index=X.index,\n",
+    ")\n",
+    "\n",
+    "# Apply groupwise top-k analysis\n",
+    "oof_topk = groupwise_topk_analysis(\n",
+    "    uncertainty_df=oof_uncertainty_df,\n",
+    "    score_ensembles=oof_score_ensembles,\n",
+    "    group_ids=group_ids,\n",
+    "    k=TOP_K,\n",
+    ")\n",
+    "\n",
+    "# Compute per-group top-k stability metrics\n",
+    "group_topk_stats = []\n",
+    "for gid in unique_groups:\n",
+    "    mask = group_ids == gid\n",
+    "    topk_members = oof_topk[mask & oof_topk[\"topk_member\"]]\n",
+    "\n",
+    "    min_topk_disagreement_prob = topk_members[\"topk_disagreement_prob\"].min() if len(topk_members) > 0 else np.nan\n",
+    "    mean_topk_disagreement_prob = topk_members[\"topk_disagreement_prob\"].mean() if len(topk_members) > 0 else np.nan\n",
+    "    max_topk_score_var = topk_members[\"topk_score_var\"].max() if len(topk_members) > 0 else np.nan\n",
+    "\n",
+    "    # Check for bubble candidates (low disagreement but not in consensus top-k)\n",
+    "    bubble = oof_topk[mask & (~oof_topk[\"topk_member\"]) & (oof_topk[\"topk_disagreement_prob\"] < 0.3)]\n",
+    "    n_bubble = len(bubble)\n",
+    "\n",
+    "    group_topk_stats.append(\n",
+    "        {\n",
+    "            \"group\": gid,\n",
+    "            \"min_topk_disagreement_prob\": min_topk_disagreement_prob,\n",
+    "            \"mean_topk_disagreement_prob\": mean_topk_disagreement_prob,\n",
+    "            \"max_topk_score_var\": max_topk_score_var,\n",
+    "            \"n_bubble_candidates\": n_bubble,\n",
+    "            \"regime\": data.loc[mask, \"regime\"].iloc[0],\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "topk_stats_df = pd.DataFrame(group_topk_stats)\n",
+    "topk_stats_df = topk_stats_df.merge(stats_df[[\"group\", \"ndcg_at_k\"]], on=\"group\")\n",
+    "\n",
+    "# Identify high-risk groups (high disagreement or bubble candidates)\n",
+    "high_risk = topk_stats_df[\n",
+    "    (topk_stats_df[\"min_topk_disagreement_prob\"] > 0.3) | (topk_stats_df[\"n_bubble_candidates\"] > 0)\n",
+    "]\n",
+    "\n",
+    "print(f\"High-risk groups (unstable top-{TOP_K} selection): {len(high_risk)}/{len(topk_stats_df)}\")\n",
+    "print(f\"\\nRisk indicators:\")\n",
+    "print(f\"  • min_topk_disagreement_prob > 0.3: at least one top-{TOP_K} candidate has ensemble disagreement\")\n",
+    "print(f\"  • n_bubble_candidates > 0: compounds just outside top-{TOP_K} could swap in\\n\")\n",
+    "\n",
+    "# Compare disagreement with NDCG quality\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(11.5, 4.2), layout=\"constrained\")\n",
+    "\n",
+    "for regime, subset in topk_stats_df.groupby(\"regime\", observed=True):\n",
+    "    axes[0].scatter(\n",
+    "        subset[\"mean_topk_disagreement_prob\"],\n",
+    "        subset[\"ndcg_at_k\"],\n",
+    "        s=32,\n",
+    "        alpha=0.78,\n",
+    "        color=PALETTE[regime],\n",
+    "        edgecolors=\"white\",\n",
+    "        linewidths=0.45,\n",
+    "        label=\"Near-tie\" if regime == \"near_tie\" else \"Separated\",\n",
+    "    )\n",
+    "axes[0].axvline(0.3, color=\"red\", linestyle=\"--\", linewidth=1.0, alpha=0.5, label=\"Risk threshold\")\n",
+    "axes[0].set(\n",
+    "    xlabel=rf\"Mean top-{TOP_K} disagreement (topk_disagreement_prob)\",\n",
+    "    ylabel=rf\"Out-of-fold NDCG@{TOP_K}\",\n",
     ")\n",
+    "axes[0].legend(frameon=False, loc=\"lower right\")\n",
+    "corr_disagreement = topk_stats_df[\"mean_topk_disagreement_prob\"].corr(topk_stats_df[\"ndcg_at_k\"], method=\"spearman\")\n",
+    "axes[0].text(\n",
+    "    0.02,\n",
+    "    0.96,\n",
+    "    rf\"Spearman $r_s={corr_disagreement:.2f}$\",\n",
+    "    transform=axes[0].transAxes,\n",
+    "    ha=\"left\",\n",
+    "    va=\"top\",\n",
+    "    fontsize=10,\n",
+    ")\n",
+    "\n",
+    "# Distribution of minimum disagreement by regime\n",
+    "separated = topk_stats_df[topk_stats_df[\"regime\"] == \"separated\"][\"min_topk_disagreement_prob\"].dropna()\n",
+    "neartie = topk_stats_df[topk_stats_df[\"regime\"] == \"near_tie\"][\"min_topk_disagreement_prob\"].dropna()\n",
+    "\n",
+    "axes[1].hist(separated, bins=15, alpha=0.7, color=PALETTE[\"separated\"], label=\"Separated\", edgecolor=\"white\")\n",
+    "axes[1].hist(neartie, bins=15, alpha=0.7, color=PALETTE[\"near_tie\"], label=\"Near-tie\", edgecolor=\"white\")\n",
+    "axes[1].axvline(0.3, color=\"red\", linestyle=\"--\", linewidth=1.0, alpha=0.5)\n",
+    "axes[1].set(xlabel=rf\"Minimum top-{TOP_K} disagreement across group\", ylabel=\"Number of groups\")\n",
+    "axes[1].legend(frameon=False)\n",
+    "axes[1].text(\n",
+    "    0.98, 0.96, f\"Risk threshold: 0.3\", transform=axes[1].transAxes, ha=\"right\", va=\"top\", fontsize=9, color=\"red\"\n",
+    ")\n",
+    "\n",
+    "for ax, label in zip(axes, [\"C\", \"D\"]):\n",
+    "    ax.set_title(label, loc=\"left\", fontweight=\"bold\")\n",
+    "    ax.grid(axis=\"y\", alpha=0.25)\n",
+    "    ax.grid(axis=\"x\", visible=False)\n",
+    "plt.show()\n",
     "\n",
-    "# Predict targets\n",
-    "targets_pred = mother_cat_rank_pipeline.predict(X.drop(columns=[ranking_groups, \"tanimoto-group\", \"smiles\"]))"
+    "print(f\"\\nKey insight: mean top-k disagreement correlates with NDCG@{TOP_K} (r_s={corr_disagreement:.2f})\")\n",
+    "print(f\"Groups with higher disagreement have genuinely worse ranking quality, indicating real instability.\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Visualising Ranking Results\n",
-    "To visualize the ranking results, you can create a plot that shows the predicted ranking positions for each group. This allows you to assess how well the model has ranked the items within each group and identify any discrepancies or patterns in the ranking.\n",
+    "## 3b. Top-k Stability Analysis\n",
     "\n",
-    "For example, you can use a scatter plot where the x-axis represents the true ranking positions (or group IDs) and the y-axis represents the predicted ranking positions. This visualization helps in understanding the alignment between the true and predicted rankings, as well as identifying any outliers or inconsistencies."
+    "Get raw ensemble scores with `return_raw=True`, then use utility functions to analyze top-k stability:\n",
+    "- `topk_rank_disagreement`: pairwise probability that two ensemble members disagree about each item's top-k membership\n",
+    "- `topk_score_variance`: score variance for consensus top-k members\n",
+    "- `groupwise_topk_analysis`: combined analysis across all groups\n",
+    "\n",
+    "`topk_disagreement_prob` is zero when all ensembles agree that an item is in or out of the top-k, and is higher when membership is unstable."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-17T16:11:22.125936Z",
-     "start_time": "2025-09-17T16:11:22.113206Z"
-    }
-   },
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Group 0 top-3 stability:\n",
+      "\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -714,171 +997,158 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>groups_to_rank</th>\n",
-       "      <th>predicted_expt</th>\n",
-       "      <th>predicted_rank</th>\n",
-       "      <th>rankable_condition</th>\n",
+       "      <th>mean_predictions</th>\n",
+       "      <th>topk_disagreement_prob</th>\n",
+       "      <th>topk_score_var</th>\n",
+       "      <th>topk_member</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-10.043097</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>A</td>\n",
+       "      <td>-3.452</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>14.137115</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>B</td>\n",
+       "      <td>-3.447</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>0.071</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.423023</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>C</td>\n",
+       "      <td>-3.467</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>11.653076</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>B</td>\n",
+       "      <td>-3.493</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>-13.351583</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>C</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142</th>\n",
-       "      <td>49.0</td>\n",
-       "      <td>13.429029</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>A</td>\n",
+       "      <td>-3.465</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>143</th>\n",
-       "      <td>49.0</td>\n",
-       "      <td>0.484454</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>B</td>\n",
+       "      <th>5</th>\n",
+       "      <td>-3.417</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>144</th>\n",
-       "      <td>50.0</td>\n",
-       "      <td>0.179325</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>B</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>145</th>\n",
-       "      <td>50.0</td>\n",
-       "      <td>14.044130</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>A</td>\n",
+       "      <th>6</th>\n",
+       "      <td>-3.451</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>146</th>\n",
-       "      <td>50.0</td>\n",
-       "      <td>-14.388412</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>C</td>\n",
+       "      <th>7</th>\n",
+       "      <td>-3.448</td>\n",
+       "      <td>0.32</td>\n",
+       "      <td>0.072</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>147 rows × 4 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "     groups_to_rank  predicted_expt  predicted_rank rankable_condition\n",
-       "0               1.0      -10.043097             3.0                  A\n",
-       "1               1.0       14.137115             1.0                  B\n",
-       "2               1.0        0.423023             2.0                  C\n",
-       "3               2.0       11.653076             1.0                  B\n",
-       "4               2.0      -13.351583             3.0                  C\n",
-       "..              ...             ...             ...                ...\n",
-       "142            49.0       13.429029             1.0                  A\n",
-       "143            49.0        0.484454             2.0                  B\n",
-       "144            50.0        0.179325             2.0                  B\n",
-       "145            50.0       14.044130             1.0                  A\n",
-       "146            50.0      -14.388412             3.0                  C\n",
-       "\n",
-       "[147 rows x 4 columns]"
+       "   mean_predictions  topk_disagreement_prob  topk_score_var  topk_member\n",
+       "0            -3.452                    0.00           0.000        False\n",
+       "1            -3.447                    0.18           0.071         True\n",
+       "2            -3.467                    0.00           0.000        False\n",
+       "3            -3.493                    0.00           0.000        False\n",
+       "4            -3.465                    0.00           0.000        False\n",
+       "5            -3.417                    0.00           0.075         True\n",
+       "6            -3.451                    0.42           0.000        False\n",
+       "7            -3.448                    0.32           0.072         True"
       ]
      },
-     "execution_count": 25,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Unstable top-3 candidates (topk_disagreement_prob > 0.3): 30/360\n"
+     ]
     }
    ],
    "source": [
-    "# Add predictions to the data\n",
-    "data[\"predicted_expt\"] = targets_pred\n",
+    "# Get raw score ensembles\n",
+    "unc_full, score_ensembles = ranker_model.predict_uncertainty(X_features, n_ensembles=10, return_raw=True)\n",
+    "\n",
+    "# Apply groupwise top-k analysis\n",
+    "topk_analysis = groupwise_topk_analysis(\n",
+    "    uncertainty_df=unc_full,\n",
+    "    score_ensembles=score_ensembles,\n",
+    "    group_ids=X[ranking_groups].values,\n",
+    "    k=TOP_K,\n",
+    ")\n",
     "\n",
-    "# Group by ranking groups and rank the predictions\n",
-    "data[\"predicted_rank\"] = data.groupby(ranking_groups)[\"predicted_expt\"].rank(ascending=False)\n",
+    "# Inspect first group\n",
+    "group_0_mask = X[ranking_groups] == 0\n",
+    "print(f\"Group 0 top-{TOP_K} stability:\\n\")\n",
+    "display(\n",
+    "    topk_analysis[group_0_mask][[\"mean_predictions\", \"topk_disagreement_prob\", \"topk_score_var\", \"topk_member\"]].round(\n",
+    "        3\n",
+    "    )\n",
+    ")\n",
     "\n",
-    "data[[\"groups_to_rank\", \"predicted_expt\", \"predicted_rank\", \"rankable_condition\"]]"
+    "# Count unstable selections (topk_disagreement_prob > 0.3)\n",
+    "unstable = topk_analysis[topk_analysis[\"topk_member\"] & (topk_analysis[\"topk_disagreement_prob\"] > 0.3)]\n",
+    "print(\n",
+    "    f\"\\nUnstable top-{TOP_K} candidates (topk_disagreement_prob > 0.3): {len(unstable)}/{(topk_analysis['topk_member']).sum()}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4b. Groupwise Top-k Stability on Out-of-Fold Predictions\n",
+    "\n",
+    "Now we apply `groupwise_topk_analysis` to the entire OOF dataset to identify which **groups** have unstable top-$k$ boundaries. This is the practical screening step: before committing synthesis resources, flag groups where the top-$k$ membership is ambiguous.\n",
+    "\n",
+    "`topk_disagreement_prob` is a true disagreement probability: it is zero when all ensembles agree about membership, whether an item is always in or always out of the top-$k$. Higher values indicate unstable membership. A negative Spearman correlation with NDCG is therefore expected when disagreement is associated with lower ranking quality."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-17T16:11:22.917641Z",
-     "start_time": "2025-09-17T16:11:22.159192Z"
-    }
-   },
+   "execution_count": 19,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean NDCG Score: 1.0000\n",
-      "Mean Precision Score k=1: 1.0000\n",
-      "Mean Precision Score k=3: 1.0000\n"
+      "High-risk groups (unstable top-3 selection): 120/120\n",
+      "\n",
+      "Risk indicators:\n",
+      "  • min_topk_disagreement_prob > 0.3: at least one top-3 candidate has ensemble disagreement\n",
+      "  • n_bubble_candidates > 0: compounds just outside top-3 could swap in\n",
+      "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1400x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1400x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1400x600 with 1 Axes>"
+       "<Figure size 1150x420 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -888,293 +1158,175 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Ranking performance evaluation completed.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.metrics import ndcg_score\n",
-    "\n",
-    "\n",
-    "def plot_metrics_individual(features, ndcg_scores, precision_scores_1, precision_scores_3):\n",
-    "    \"\"\"\n",
-    "    Plot NDCG and Precision scores for each SMILES.\n",
-    "\n",
-    "    Parameters:\n",
-    "    features (pd.DataFrame): DataFrame containing feature information including SMILES.\n",
-    "    ndcg_scores (list): List of NDCG scores for each SMILES.\n",
-    "    precision_scores_1 (list): List of Precision@1 scores for each SMILES.\n",
-    "    precision_scores_3 (list): List of Precision@3 scores for each SMILES.\n",
-    "    \"\"\"\n",
-    "    unique_ingredients = features[\"smiles\"].unique()\n",
-    "\n",
-    "    # Plot NDCG Scores\n",
-    "    plt.figure(figsize=(14, 6))\n",
-    "    plt.bar(unique_ingredients, ndcg_scores, color=\"blue\", alpha=0.7)\n",
-    "    plt.title(\"NDCG Scores by SMILES\", fontsize=16)\n",
-    "    plt.xlabel(\"SMILES\", fontsize=14)\n",
-    "    plt.ylabel(\"NDCG Score\", fontsize=14)\n",
-    "    plt.xticks(rotation=90, fontsize=10)\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()\n",
-    "\n",
-    "    # Plot Precision@1 Scores\n",
-    "    plt.figure(figsize=(14, 6))\n",
-    "    plt.bar(unique_ingredients, precision_scores_1, color=\"green\", alpha=0.7)\n",
-    "    plt.title(\"Precision@1 Scores by SMILES\", fontsize=16)\n",
-    "    plt.xlabel(\"SMILES\", fontsize=14)\n",
-    "    plt.ylabel(\"Precision@1 Score\", fontsize=14)\n",
-    "    plt.xticks(rotation=90, fontsize=10)\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()\n",
-    "\n",
-    "    # Plot Precision@3 Scores\n",
-    "    plt.figure(figsize=(14, 6))\n",
-    "    plt.bar(unique_ingredients, precision_scores_3, color=\"orange\", alpha=0.7)\n",
-    "    plt.title(\"Precision@3 Scores by SMILES\", fontsize=16)\n",
-    "    plt.xlabel(\"SMILES\", fontsize=14)\n",
-    "    plt.ylabel(\"Precision@3 Score\", fontsize=14)\n",
-    "    plt.xticks(rotation=90, fontsize=10)\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()\n",
-    "\n",
-    "\n",
-    "def evaluate_ranking_performance(y_true, y_pred, features, group_string=\"group_id\"):\n",
-    "    \"\"\"\n",
-    "    Evaluate the ranking performance using Normalized Discounted Cumulative Gain (NDCG) and Precision.\n",
-    "\n",
-    "    Parameters:\n",
-    "    y_true (np.ndarray): True target values.\n",
-    "    y_pred (np.ndarray): Predicted target values.\n",
-    "    features (pd.DataFrame): DataFrame containing feature information including group_string.\n",
-    "\n",
-    "    Returns:\n",
-    "    float: NDCG score.\n",
-    "    \"\"\"\n",
-    "    y_true = y_true.reset_index(drop=True)\n",
-    "    y_pred = pd.Series(y_pred).reset_index(drop=True)\n",
-    "    ndcg_scores = []\n",
-    "    precision_scores_1 = []\n",
-    "    precision_scores_3 = []\n",
-    "\n",
-    "    unique_groups = features[group_string].unique()\n",
-    "    for group in unique_groups:\n",
-    "        group_indices = features[features[group_string] == group].index\n",
-    "        y_true_group = y_true[group_indices]\n",
-    "        y_pred_group = y_pred[group_indices]\n",
-    "\n",
-    "        if len(y_true_group) > 1:  # NDCG is not defined for groups with a single item\n",
-    "            ndcg = ndcg_score([y_true_group], [y_pred_group], k=3)\n",
-    "            ndcg_scores.append(ndcg)\n",
-    "\n",
-    "            precision_1 = precision_score(y_true_group, y_pred_group, 1)\n",
-    "            precision_scores_1.append(precision_1)\n",
-    "            precision_3 = precision_score(y_true_group, y_pred_group, 3)\n",
-    "            precision_scores_3.append(precision_3)\n",
-    "\n",
-    "            # Add metrics to the features DataFrame\n",
-    "            features.loc[group_indices, \"ndcg_score\"] = ndcg\n",
-    "            features.loc[group_indices, \"precision_score_k1\"] = precision_1\n",
-    "            features.loc[group_indices, \"precision_score_k3\"] = precision_3\n",
-    "\n",
-    "    mean_ndcg = np.mean(ndcg_scores)\n",
-    "    mean_precision_1 = np.mean(precision_scores_1)\n",
-    "    mean_precision_3 = np.mean(precision_scores_3)\n",
-    "\n",
-    "    print(f\"Mean NDCG Score: {mean_ndcg:.4f}\")\n",
-    "    print(f\"Mean Precision Score k=1: {mean_precision_1:.4f}\")\n",
-    "    print(f\"Mean Precision Score k=3: {mean_precision_3:.4f}\")\n",
-    "    # Call the function to plot metrics by group\n",
-    "    plot_metrics_individual(features, ndcg_scores, precision_scores_1, precision_scores_3)\n",
-    "    return {\n",
-    "        \"mean_ndcg\": mean_ndcg,\n",
-    "        \"mean_precision_1\": mean_precision_1,\n",
-    "        \"mean_precision_3\": mean_precision_3,\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "# Evaluate the ranking performance\n",
-    "ranking_performance = evaluate_ranking_performance(y, targets_pred, X, group_string=ranking_groups)\n",
-    "print(\"Ranking performance evaluation completed.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-17T13:42:41.891706Z",
-     "start_time": "2025-09-17T13:42:41.469365Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'seaborn'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[14]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m seaborn \u001b[38;5;28;01mas\u001b[39;00m sns\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m sorted_data = data.sort_values(by=[\u001b[33m\"groups_to_rank\"\u001b[39m, \u001b[33m\"predicted_rank\"\u001b[39m]).reset_index(drop=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m      4\u001b[39m \n",
-      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'seaborn'"
+      "\n",
+      "Key insight: mean top-k disagreement correlates with NDCG@3 (r_s=-0.26)\n",
+      "Groups with higher disagreement have genuinely worse ranking quality, indicating real instability.\n"
      ]
     }
    ],
    "source": [
-    "import seaborn as sns\n",
-    "\n",
-    "sorted_data = data.sort_values(by=[\"groups_to_rank\", \"predicted_rank\"]).reset_index(drop=True)\n",
-    "\n",
-    "# Pivot the data to create separate columns for each rankable_condition based on predicted_rank\n",
-    "pivot_data = sorted_data.pivot_table(\n",
-    "    index=\"groups_to_rank\", columns=\"rankable_condition\", values=\"predicted_rank\", aggfunc=\"mean\"\n",
-    ").fillna(0)\n",
-    "\n",
-    "# Create a stacked bar chart\n",
-    "plt.figure(figsize=(20, 6))\n",
-    "pivot_data.plot(kind=\"bar\", stacked=True, colormap=\"viridis\", width=0.8)\n",
-    "\n",
-    "# Set plot title and labels\n",
-    "plt.title(\"Showing ranking order\")\n",
-    "plt.xlabel(\"Ranking groups e.g. Molecule\")\n",
-    "plt.ylabel(\"Predicted Rank\")\n",
-    "plt.xticks(rotation=90)\n",
-    "plt.legend(title=\"Rankable Condition\")\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Cross-Validation Pooling\n",
-    "\n",
-    "For Group K fold cross-validation, the entries will be separated according to tanimoto groups.\n",
-    "\n",
-    "Note 1: When performing cross validation, if the number of groups is greater than the number of folds (n_splits), resulting folds will contain more than one group and will be treated as belonging to the same group\n",
+    "# Compute per-group top-k stability metrics\n",
+    "group_topk_stats = []\n",
+    "for gid in unique_groups:\n",
+    "    mask = group_ids == gid\n",
+    "    topk_members = oof_topk[mask & oof_topk[\"topk_member\"]]\n",
+    "\n",
+    "    min_topk_disagreement_prob = topk_members[\"topk_disagreement_prob\"].min() if len(topk_members) > 0 else np.nan\n",
+    "    mean_topk_disagreement_prob = topk_members[\"topk_disagreement_prob\"].mean() if len(topk_members) > 0 else np.nan\n",
+    "    max_topk_score_var = topk_members[\"topk_score_var\"].max() if len(topk_members) > 0 else np.nan\n",
+    "\n",
+    "    # Check for bubble candidates (low disagreement but not in consensus top-k)\n",
+    "    bubble = oof_topk[mask & (~oof_topk[\"topk_member\"]) & (oof_topk[\"topk_disagreement_prob\"] < 0.7)]\n",
+    "    n_bubble = len(bubble)\n",
+    "\n",
+    "    group_topk_stats.append(\n",
+    "        {\n",
+    "            \"group\": gid,\n",
+    "            \"min_topk_disagreement_prob\": min_topk_disagreement_prob,\n",
+    "            \"mean_topk_disagreement_prob\": mean_topk_disagreement_prob,\n",
+    "            \"max_topk_score_var\": max_topk_score_var,\n",
+    "            \"n_bubble_candidates\": n_bubble,\n",
+    "            \"regime\": data.loc[mask, \"regime\"].iloc[0],\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "topk_stats_df = pd.DataFrame(group_topk_stats)\n",
+    "topk_stats_df = topk_stats_df.merge(stats_df[[\"group\", \"ndcg_at_k\"]], on=\"group\")\n",
+    "\n",
+    "# Identify high-risk groups (high disagreement or bubble candidates)\n",
+    "high_risk = topk_stats_df[\n",
+    "    (topk_stats_df[\"min_topk_disagreement_prob\"] > 0.3) | (topk_stats_df[\"n_bubble_candidates\"] > 0)\n",
+    "]\n",
+    "\n",
+    "print(f\"High-risk groups (unstable top-{TOP_K} selection): {len(high_risk)}/{len(topk_stats_df)}\")\n",
+    "print(f\"\\nRisk indicators:\")\n",
+    "print(f\"  • min_topk_disagreement_prob > 0.3: at least one top-{TOP_K} candidate has ensemble disagreement\")\n",
+    "print(f\"  • n_bubble_candidates > 0: compounds just outside top-{TOP_K} could swap in\\n\")\n",
+    "\n",
+    "# Compare disagreement with NDCG quality\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(11.5, 4.2), layout=\"constrained\")\n",
+    "\n",
+    "for regime, subset in topk_stats_df.groupby(\"regime\", observed=True):\n",
+    "    axes[0].scatter(\n",
+    "        subset[\"mean_topk_disagreement_prob\"],\n",
+    "        subset[\"ndcg_at_k\"],\n",
+    "        s=32,\n",
+    "        alpha=0.78,\n",
+    "        color=PALETTE[regime],\n",
+    "        edgecolors=\"white\",\n",
+    "        linewidths=0.45,\n",
+    "        label=\"Near-tie\" if regime == \"near_tie\" else \"Separated\",\n",
+    "    )\n",
+    "axes[0].axvline(0.3, color=\"red\", linestyle=\"--\", linewidth=1.0, alpha=0.5, label=\"Risk threshold\")\n",
+    "axes[0].set(\n",
+    "    xlabel=rf\"Mean top-{TOP_K} disagreement (topk_disagreement_prob)\",\n",
+    "    ylabel=rf\"Out-of-fold NDCG@{TOP_K}\",\n",
+    ")\n",
+    "axes[0].legend(frameon=False, loc=\"lower right\")\n",
+    "corr_disagreement = topk_stats_df[\"mean_topk_disagreement_prob\"].corr(topk_stats_df[\"ndcg_at_k\"], method=\"spearman\")\n",
+    "axes[0].text(\n",
+    "    0.02,\n",
+    "    0.96,\n",
+    "    rf\"Spearman $r_s={corr_disagreement:.2f}$\",\n",
+    "    transform=axes[0].transAxes,\n",
+    "    ha=\"left\",\n",
+    "    va=\"top\",\n",
+    "    fontsize=10,\n",
+    ")\n",
     "\n",
-    "Note 2: The Tanimoto groups are only to be used as the \"groups\" parameter of sklearn's cross-validation methods (cross_val_score, cross_validate) and are NOT to be interchangeable with the previously created ranking groups. Ranking groups are to be used for generating ranking queries. When working with (Mother)CatBoostRanker models, ranking groups are to be passed as the \"group_id\" parameter."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
+    "# Distribution of minimum disagreement by regime\n",
+    "separated = topk_stats_df[topk_stats_df[\"regime\"] == \"separated\"][\"min_topk_disagreement_prob\"].dropna()\n",
+    "neartie = topk_stats_df[topk_stats_df[\"regime\"] == \"near_tie\"][\"min_topk_disagreement_prob\"].dropna()\n",
+    "\n",
+    "axes[1].hist(separated, bins=15, alpha=0.7, color=PALETTE[\"separated\"], label=\"Separated\", edgecolor=\"white\")\n",
+    "axes[1].hist(neartie, bins=15, alpha=0.7, color=PALETTE[\"near_tie\"], label=\"Near-tie\", edgecolor=\"white\")\n",
+    "axes[1].axvline(0.3, color=\"red\", linestyle=\"--\", linewidth=1.0, alpha=0.5)\n",
+    "axes[1].set(xlabel=rf\"Minimum top-{TOP_K} disagreement across group\", ylabel=\"Number of groups\")\n",
+    "axes[1].legend(frameon=False)\n",
+    "axes[1].text(\n",
+    "    0.98, 0.96, f\"Risk threshold: 0.3\", transform=axes[1].transAxes, ha=\"right\", va=\"top\", fontsize=9, color=\"red\"\n",
+    ")\n",
     "\n",
-    "### Importance of Group Control in Cross-Validation\n",
+    "for ax, label in zip(axes, [\"C\", \"D\"]):\n",
+    "    ax.set_title(label, loc=\"left\", fontweight=\"bold\")\n",
+    "    ax.grid(axis=\"y\", alpha=0.25)\n",
+    "    ax.grid(axis=\"x\", visible=False)\n",
+    "plt.show()\n",
     "\n",
-    "Depending on the use case, it might be crucial to have tight control over which groups are included in each fold during cross-validation. This is particularly important when working with datasets where certain groups of data points share inherent similarities, such as molecular structures or time-series data. Without proper handling, the model might inadvertently learn patterns specific to certain groups, leading to overfitting and overly optimistic performance metrics.\n"
+    "print(f\"\\nKey insight: mean top-k disagreement correlates with NDCG@{TOP_K} (r_s={corr_disagreement:.2f})\")\n",
+    "print(f\"Groups with higher disagreement have genuinely worse ranking quality, indicating real instability.\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## 7. Practical Workflow: Multi-Target Compound Selection\n",
     "\n",
-    "### Cross-Validation example using chemical similarity\n",
+    "This section demonstrates a complete production workflow integrating the ranking utility functions for multi-target drug discovery campaigns.\n",
     "\n",
-    "To ensure that the cross-validation process respects the chemical similarity of molecules, we utilize Tanimoto similarity-based grouping. This approach ensures that molecules with similar chemical structures are grouped together, preventing data leakage and providing a more realistic evaluation of the model's performance.\n",
+    "### Interpreting top-k disagreement\n",
     "\n",
-    "#### Steps:\n",
-    "1. **Tanimoto Grouping**: Molecules are grouped based on their Tanimoto similarity using the `TanimotoGroupingFromMols` class. A similarity threshold of 0.3 is used to define the groups.\n",
-    "2. **Group Assignment**: Each molecule is assigned a \"tanimoto-group\" identifier, which is then used as the `groups` parameter in cross-validation methods.\n",
-    "3. **Group K-Fold Cross-Validation**: The `GroupKFold` method is employed to split the data into folds, ensuring that all molecules within the same Tanimoto group are kept in the same fold.\n",
+    "- `topk_disagreement_prob` is the fraction of virtual ensembles that disagree with top-$k$ membership.\n",
+    "- Low `topk_disagreement_prob` indicates stable, reliable top-$k$ membership.\n",
+    "- High `topk_disagreement_prob` indicates unstable membership and higher selection risk.\n",
+    "- Flag consensus top-$k$ candidates where `topk_disagreement_prob > 0.3` and inspect non-members with `topk_disagreement_prob < 0.7` as bubble candidates.\n",
     "\n",
-    "This method provides a robust framework for evaluating the model while accounting for the inherent chemical similarities in the dataset.\n"
+    "The sign of the correlation is important: NDCG is better for higher-quality rankings, while `topk_disagreement_prob` is higher when rankings disagree. Thus, a negative Spearman correlation means that more disagreement is associated with worse ranking quality."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-17T13:42:41.926141Z",
-     "start_time": "2025-09-17T13:42:41.910189Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Ensure combined_groups matches the length of data\n",
-    "combined_groups = list(zip(data[\"tanimoto-group\"], data[ranking_groups]))\n",
-    "\n",
-    "# Reset the index of X and y to ensure alignment\n",
-    "X = X.reset_index(drop=True)\n",
-    "y = y.reset_index(drop=True)\n",
-    "\n",
-    "# Initialize GroupKFold with the desired number of splits\n",
-    "cv = GroupKFold(n_splits=5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2025-09-17T13:42:42.039374Z"
-    }
-   },
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/workspaces/MotherML/src/mother/optimization/core.py:255: FutureWarning: `optuna.terminator` module has been deprecated in v4.9.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v4.9.0.\n",
-      "  report_cross_validation_scores(trial, list(cv_score_not_na))\n",
-      "/workspaces/MotherML/src/mother/optimization/core.py:255: FutureWarning: `optuna.terminator` module has been deprecated in v4.9.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v4.9.0.\n",
-      "  report_cross_validation_scores(trial, list(cv_score_not_na))\n",
-      "/workspaces/MotherML/src/mother/optimization/core.py:255: FutureWarning: `optuna.terminator` module has been deprecated in v4.9.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v4.9.0.\n",
-      "  report_cross_validation_scores(trial, list(cv_score_not_na))\n",
-      "/workspaces/MotherML/src/mother/optimization/core.py:255: FutureWarning: `optuna.terminator` module has been deprecated in v4.9.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v4.9.0.\n",
-      "  report_cross_validation_scores(trial, list(cv_score_not_na))\n",
-      "/workspaces/MotherML/src/mother/optimization/core.py:255: FutureWarning: `optuna.terminator` module has been deprecated in v4.9.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v4.9.0.\n",
-      "  report_cross_validation_scores(trial, list(cv_score_not_na))\n",
-      "/workspaces/MotherML/src/mother/optimization/core.py:255: FutureWarning: `optuna.terminator` module has been deprecated in v4.9.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v4.9.0.\n",
-      "  report_cross_validation_scores(trial, list(cv_score_not_na))\n"
+      "Risk Assessment Summary:\n",
+      "  Unstable top-3 compounds (topk_disagreement_prob > 0.3): 24\n",
+      "  Bubble candidates (topk_disagreement_prob < 0.3, not in top-3): 577\n",
+      "  High variance in top-3: 0\n"
      ]
     }
    ],
    "source": [
-    "# Extract group codes for cross-validation\n",
-    "cv_groupcodes = X[\"tanimoto-group\"].values\n",
-    "\n",
-    "\n",
-    "model_tuned = tuner.optimize(\n",
-    "    mother_cat_rank_pipeline,\n",
-    "    X.drop(columns=[\"groups_to_rank\", \"tanimoto-group\", \"smiles\"]),\n",
-    "    data[\"expt\"],\n",
-    "    cv,\n",
-    "    groups=X[\"tanimoto-group\"],\n",
-    "    hyperparameter_space_function=lambda trial, X, y: {\n",
-    "        \"model__learning_rate\": trial.suggest_float(\"ml_model__learning_rate\", 0.01, 0.3, log=True),\n",
-    "        \"model__l2_leaf_reg\": trial.suggest_float(\"ml_model__l2_leaf_reg\", 1, 10),\n",
-    "        \"model__random_strength\": trial.suggest_float(\"ml_model__random_strength\", 1, 10),\n",
-    "    },\n",
-    "    ranking_groups=X[\"groups_to_rank\"].values,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-17T13:37:58.318020Z",
-     "start_time": "2025-04-11T07:27:37.713608Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "print(f\"Best Parameters:\")\n",
-    "print(tuner.study.best_trial.params)\n",
-    "print(\"Best Score:\")\n",
-    "print(tuner.study.best_value)"
+    "# === STEP 1: Flag High-Risk Compounds ===\n",
+    "\n",
+    "# Apply groupwise analysis to full OOF dataset\n",
+    "oof_topk_full = groupwise_topk_analysis(\n",
+    "    uncertainty_df=oof_uncertainty_df,\n",
+    "    score_ensembles=oof_score_ensembles,\n",
+    "    group_ids=group_ids,\n",
+    "    k=TOP_K,\n",
+    ")\n",
+    "\n",
+    "# Add metadata\n",
+    "oof_topk_full[\"group\"] = group_ids\n",
+    "oof_topk_full[\"regime\"] = data[\"regime\"].values\n",
+    "oof_topk_full[\"activity\"] = y.values\n",
+    "\n",
+    "# Criterion 1: High ensemble disagreement among consensus top-k members\n",
+    "unstable_topk = oof_topk_full[\"topk_member\"] & (oof_topk_full[\"topk_disagreement_prob\"] > 0.3)\n",
+    "\n",
+    "# Criterion 2: Bubble candidates (not in consensus top-k but close)\n",
+    "bubble = ~oof_topk_full[\"topk_member\"] & (oof_topk_full[\"topk_disagreement_prob\"] < 0.3)\n",
+    "\n",
+    "# Criterion 3: High score variance\n",
+    "score_range = oof_score_ensembles.max() - oof_score_ensembles.min()\n",
+    "high_var = (oof_topk_full[\"topk_score_var\"] > 0.1 * score_range) & oof_topk_full[\"topk_member\"]\n",
+    "\n",
+    "print(f\"Risk Assessment Summary:\")\n",
+    "print(f\"  Unstable top-{TOP_K} compounds (topk_disagreement_prob > 0.3): {unstable_topk.sum()}\")\n",
+    "print(f\"  Bubble candidates (topk_disagreement_prob < 0.3, not in top-{TOP_K}): {bubble.sum()}\")\n",
+    "print(f\"  High variance in top-{TOP_K}: {high_var.sum()}\")"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "mother-ml",
+   "display_name": "mother-ml (3.12.13)",
    "language": "python",
    "name": "python3"
   },
@@ -1188,7 +1340,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.14"
+   "version": "3.12.13"
   }
  },
  "nbformat": 4,
diff --git a/src/mother/ml/models/m_catboost.py b/src/mother/ml/models/m_catboost.py
index 61673f6..b470f7c 100644
--- a/src/mother/ml/models/m_catboost.py
+++ b/src/mother/ml/models/m_catboost.py
@@ -22,6 +22,7 @@
 - CatboostRegressorMother: Extended CatBoostRegressor with uncertainty estimation and quantile support.
 - CatboostGaussianProcessRegressorMother: CatBoost-based Gaussian Process regressor for epistemic uncertainty.
 - CatboostClassifierMother: Unified classifier for binary and multiclass tasks with loss-specific tuning.
+- CatboostRankerMother: Extended CatBoostRanker with rank prediction, uncertainty estimation, and Optuna tuning support.
 
 All classes provide methods for Optuna-based hyperparameter optimization, uncertainty-aware prediction, and
 Mother framework compatibility.
@@ -29,12 +30,17 @@
 
 import copy
 import logging
+import re
+from functools import wraps
+from typing import Any, Callable, Optional, Union, cast
 
 import catboost
 import numpy as np
 import pandas as pd
 from catboost import CatBoostClassifier, CatBoostRanker, CatBoostRegressor
 from optuna.trial import Trial
+from sklearn import get_config as skl_get_config
+from sklearn import set_config as skl_set_config
 from sklearn.base import BaseEstimator
 from sklearn.utils import check_X_y
 
@@ -47,6 +53,56 @@
 DEFAULT_QUANTILES: list[float] = [0.25, 0.5, 0.75]
 
 
+scores_to_ranks = utils.scores_to_ranks
+scores_matrix_to_ranks = utils.scores_matrix_to_ranks
+
+
+def _validate_ranking_group_id(group_id: np.ndarray, n_samples: int) -> np.ndarray:
+    """Validate and normalise a group ID array to 1-D, aligned with X rows."""
+    group_arr = np.asarray(group_id).reshape(-1)
+    if group_arr.shape[0] != n_samples:
+        raise ValueError(f"group_id length must match number of rows in X ({n_samples}), got {group_arr.shape[0]}.")
+    return group_arr
+
+
+def _iter_ranking_group_indices(group_id: np.ndarray) -> list[np.ndarray]:
+    """Return stable, input-order index arrays (positional) for each unique group."""
+    return [np.flatnonzero(group_id == g) for g in pd.unique(group_id)]
+
+
+def ensure_metadata_routing(func: Callable) -> Callable:
+    """
+    Decorator to ensure metadata routing is enabled before executing a function.
+
+    This decorator checks if sklearn's metadata routing is enabled and activates it
+    if necessary. It's particularly useful for initializing ranking models that require
+    metadata routing for passing additional parameters like group_id.
+
+    Parameters
+    ----------
+    func : Callable
+        The function to be decorated (typically __init__ of a ranking model)
+
+    Returns
+    -------
+    Callable
+        The wrapped function with metadata routing ensured
+    """
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        use_metadata_routing: bool = bool(skl_get_config().get("enable_metadata_routing", False))
+        if not use_metadata_routing:
+            module_logger.warning(
+                "Metadata routing is not enabled, enabling it now. This may cause issues in passing "
+                "training arguments to other sklearn objects."
+            )
+            skl_set_config(enable_metadata_routing=True)  # NOSONAR
+        return func(*args, **kwargs)
+
+    return wrapper
+
+
 class _CatboostModelMotherBase(AbstractMotherPipeline):
     def __sklearn_clone__(self):
         """Custom clone that uses content equality instead of identity.
@@ -126,6 +182,10 @@ class _CatboostHyperParams(AbstractMotherPipeline):
         Whether to include the "grow_policy" parameter in the hyperparameter space for tuning.
         If False Symmetric Trees are used which allows the use of i.e. object importance or
         monotonic constraints.
+    tune_loss_function : bool
+        Whether to include the loss function in the hyperparameter search space.
+        If False, the loss function set on the model at construction time is kept fixed
+        throughout tuning and ``suggested_params_loss`` is not called.
 
     Methods
     -------
@@ -135,7 +195,12 @@ class _CatboostHyperParams(AbstractMotherPipeline):
         Adds loss-specific hyperparameters to the suggested parameters based on the target type.
     """
 
-    def __init__(self, tune_boosting_type: bool = False, tune_tree_structure_type: bool = True):
+    def __init__(
+        self,
+        tune_boosting_type: bool = False,
+        tune_tree_structure_type: bool = True,
+        tune_loss_function: bool = True,
+    ):
         """
         Initialize the _CatboostHyperParams.
 
@@ -146,9 +211,14 @@ def __init__(self, tune_boosting_type: bool = False, tune_tree_structure_type: b
                 Whether to include the "grow_policy" parameter in the hyperparameter space for tuning.
                 If False Symmetric Trees are used which allows the use of i.e. object importance or
                 monotonic constraints.
+            tune_loss_function : bool, optional
+                Whether to include the loss function in the hyperparameter search space.
+                If False, the loss function set on the model at construction time is kept fixed
+                throughout tuning and ``suggested_params_loss`` is not called. Defaults to ``True``.
         """
         self.tune_boosting_type = tune_boosting_type
         self.tune_tree_structure_type = tune_tree_structure_type
+        self.tune_loss_function = tune_loss_function
 
     def get_hyperparameter_space(self, X, y, trial: Trial, prefix: str = "") -> dict:
         min_depth, max_depth = utils.calc_range_tree_depth(X)
@@ -187,7 +257,9 @@ def get_hyperparameter_space(self, X, y, trial: Trial, prefix: str = "") -> dict
         else:
             suggested_params[prefix + "max_depth"] = trial.suggest_int(prefix + "max_depth", min_depth, max_depth)
 
-        suggested_params = self.suggested_params_loss(trial, suggested_params, y, prefix)
+        if self.tune_loss_function:
+            suggested_params = self.suggested_params_loss(trial, suggested_params, y, prefix)
+
         module_logger.info(f"Suggested parameters in trial {trial.number}: {suggested_params}")
 
         return suggested_params
@@ -239,6 +311,7 @@ def __init__(
         target_type: props.TargetType = "single_target",
         tune_tree_structure_type: bool = True,
         tune_boosting_type: bool = False,
+        tune_loss_function: bool = True,
         quantiles: list[float] | None = None,
         data_uncertainty: bool = False,
         model_type: props.ModelType = "regression",
@@ -254,6 +327,11 @@ def __init__(
                 Whether to include the "grow_policy" parameter in hyperparameter tuning.
             tune_boosting_type : bool, optional
                 Whether to tune boosting_type.
+            tune_loss_function : bool, optional
+                Whether to include the loss function in the hyperparameter search space.
+                If ``False``, the loss function is fixed at construction time and
+                ``suggested_params_loss`` is not called during Optuna tuning.
+                Defaults to ``True``.
             quantiles : list[float] or None, optional
                 Quantiles for multi-quantile regression.
             data_uncertainty : bool, optional
@@ -265,7 +343,7 @@ def __init__(
                 Additional CatBoostRegressor parameters.
         """
         # Initialize hyperparameter tuning configuration
-        _CatboostHyperParams.__init__(self, tune_boosting_type, tune_tree_structure_type)
+        _CatboostHyperParams.__init__(self, tune_boosting_type, tune_tree_structure_type, tune_loss_function)
 
         # set the correct model_type
         if model_type != "regression":
@@ -334,6 +412,7 @@ def __getstate__(self):
             {
                 "target_type": self.target_type,
                 "tune_boosting_type": self.tune_boosting_type,
+                "tune_loss_function": self.tune_loss_function,
                 "model_type": self.model_type,
                 "quantiles": self.quantiles,
                 "_quantiles_processed": getattr(self, "_quantiles_processed", None),
@@ -346,6 +425,7 @@ def __getstate__(self):
     def __setstate__(self, state):
         self.target_type = state.pop("target_type", "single_target")
         self.tune_boosting_type = state.pop("tune_boosting_type", False)
+        self.tune_loss_function = state.pop("tune_loss_function", True)
         self.model_type = state.pop("model_type", "regression")
         self.quantiles = state.pop("quantiles", None)
         self._quantiles_processed = state.pop("_quantiles_processed", None)
@@ -371,6 +451,7 @@ def get_params(self, deep=True):
                 "quantiles": self.quantiles,
                 "target_type": self.target_type,
                 "tune_boosting_type": self.tune_boosting_type,
+                "tune_loss_function": self.tune_loss_function,
                 "model_type": self.model_type,
                 "data_uncertainty": self.data_uncertainty,
                 "tune_tree_structure_type": self.tune_tree_structure_type,
@@ -393,6 +474,7 @@ def set_params(self, **params):
         our_params = [
             "target_type",
             "tune_boosting_type",
+            "tune_loss_function",
             "model_type",
             "quantiles",
             "data_uncertainty",
@@ -717,8 +799,6 @@ def __init__(
         random_strength: float = 0.1,
         random_score_type: str = "Gumbel",
         eps: float = 1e-4,
-        tune_boosting_type: bool = False,
-        tune_tree_structure_type: bool = True,
         verbose: bool = False,
         model_type: str = "regression",
         target_type: props.TargetType = "single_target",
@@ -746,10 +826,6 @@ def __init__(
                 Type of random score for splits.
             eps : float, default=1e-4
                 Numerical stability parameter.
-            tune_boosting_type : bool, default=False
-                Whether to include boosting type in hyperparameter optimization.
-            tune_tree_structure_type : bool, default=True
-                Whether to include the "grow_policy" parameter in hyperparameter tuning.
             verbose : bool, default=False
                 Whether to print training logs during fitting.
             model_type : str, default="regression"
@@ -759,8 +835,13 @@ def __init__(
             **kwargs : dict
                 Additional parameters for CatBoost's `sample_gaussian_process` method.
         """
-        # Initialize hyperparameter tuning configuration
-        _CatboostHyperParams.__init__(self, tune_boosting_type, tune_tree_structure_type)
+        # GP posterior sampling does not support hyperparameter tuning.
+        _CatboostHyperParams.__init__(
+            self,
+            tune_boosting_type=False,
+            tune_tree_structure_type=False,
+            tune_loss_function=False,
+        )
 
         # Check for 'model_type'
         if model_type != "regression":
@@ -836,8 +917,6 @@ def get_params(self, deep=True):
         custom_params = {
             "model_type": self.model_type,
             "target_type": self.target_type,
-            "tune_boosting_type": self.tune_boosting_type,
-            "tune_tree_structure_type": self.tune_tree_structure_type,
             "samples": self.samples,
             "prior_iterations": self.prior_iterations,
             "sigma": self.sigma,
@@ -863,8 +942,6 @@ def set_params(self, **params):
         custom_param_names = {
             "model_type",
             "target_type",
-            "tune_boosting_type",
-            "tune_tree_structure_type",
             "samples",
             "prior_iterations",
             "sigma",
@@ -1092,7 +1169,8 @@ def __setstate__(self, state):
         self.model_type = state.pop("model_type", "regression")
         self.target_type = state.pop("target_type", "single_target")
         self.tune_boosting_type = state.pop("tune_boosting_type", False)
-        self.tune_tree_structure_type = state.pop("tune_tree_structure_type", True)
+        self.tune_tree_structure_type = state.pop("tune_tree_structure_type", False)
+        self.tune_loss_function = state.pop("tune_loss_function", False)
         self.samples = state.pop("samples", 10)
         self.prior_iterations = state.pop("prior_iterations", 100)
         self.sigma = state.pop("sigma", 0.1)
@@ -1152,6 +1230,7 @@ def __init__(
         tune_boosting_type: bool = False,
         model_type: props.ModelType = "classification_binary",
         tune_tree_structure_type: bool = True,
+        tune_loss_function: bool = True,
         **kwargs,
     ):
         """
@@ -1162,11 +1241,14 @@ def __init__(
             tune_boosting_type (bool): Whether to tune boosting_type.
             model_type (str): Model type ("classification_binary" or "classification_multiclass").
             tune_tree_structure_type (bool): Whether to include the "grow_policy" parameter in hyperparameter tuning.
+            tune_loss_function (bool): Whether to include the loss function in the hyperparameter search space.
+                If ``False``, the loss function is fixed at construction time and
+                ``suggested_params_loss`` is not called during Optuna tuning. Defaults to ``True``.
             **kwargs: Additional CatBoostClassifier parameters.
         """
 
         # Initialize hyperparameter tuning configuration
-        _CatboostHyperParams.__init__(self, tune_boosting_type, tune_tree_structure_type)
+        _CatboostHyperParams.__init__(self, tune_boosting_type, tune_tree_structure_type, tune_loss_function)
 
         self.model_type = model_type
         self.target_type = target_type
@@ -1214,6 +1296,7 @@ def get_params(self, deep=True):
             {
                 "target_type": self.target_type,
                 "tune_boosting_type": self.tune_boosting_type,
+                "tune_loss_function": self.tune_loss_function,
                 "model_type": self.model_type,
                 "tune_tree_structure_type": self.tune_tree_structure_type,
             }
@@ -1233,6 +1316,7 @@ def set_params(self, **params):
         our_params = [
             "target_type",
             "tune_boosting_type",
+            "tune_loss_function",
             "model_type",
             "tune_tree_structure_type",
         ]
@@ -1320,6 +1404,7 @@ def __getstate__(self):
             {
                 "target_type": self.target_type,
                 "tune_boosting_type": self.tune_boosting_type,
+                "tune_loss_function": self.tune_loss_function,
                 "model_type": self.model_type,
                 "tune_tree_structure_type": self.tune_tree_structure_type,
             }
@@ -1329,6 +1414,7 @@ def __getstate__(self):
     def __setstate__(self, state):
         self.target_type = state.pop("target_type", "single_target")
         self.tune_boosting_type = state.pop("tune_boosting_type", False)
+        self.tune_loss_function = state.pop("tune_loss_function", True)
         self.model_type = state.pop("model_type", "classification_binary")
         self.tune_tree_structure_type = state.pop("tune_tree_structure_type", True)
         super(CatBoostClassifier, self).__setstate__(state)
@@ -1430,6 +1516,27 @@ class CatboostRankerMother(CatBoostRanker, _CatboostModelMotherBase, _CatboostHy
         Whether to include the "boosting_type" parameter in the hyperparameter space for tuning.
     tune_tree_structure_type : bool
         Whether to include the "grow_policy" parameter in the hyperparameter space for tuning.
+    tune_pairwise_type : bool
+        Whether to include Pairwise loss functions (``YetiRankPairwise``,
+        ``PairLogitPairwise``) in the hyperparameter space for tuning.
+        Pairwise losses require ``grow_policy="SymmetricTree"`` and
+        ``boosting_type="Plain"``; therefore pairwise tuning is only possible
+        when both ``tune_tree_structure_type=False`` and
+        ``tune_boosting_type=False``.
+
+        If an incompatible combination is passed at construction time (e.g.
+        ``tune_pairwise_type=True`` together with
+        ``tune_tree_structure_type=True``), a warning is emitted and
+        ``tune_pairwise_type`` is automatically set to ``False``.
+    top : Optional[int]
+        Maximum number of top samples to consider for ranking metrics (e.g., NDCG@k).
+        Only works with YetiRank loss function. Default is 0 (disabled).
+    max_pairs : Optional[int]
+        Maximum number of pairs to generate for PairLogit losses. This can significantly
+        reduce computation time for large ranking tasks. Only applies to PairLogit and
+        PairLogitPairwise loss functions. Default is ``None`` (no limit). Also used during
+        hyperparameter tuning to set an upper limit on the number of pairs when evaluating
+        PairLogit losses.
 
     Methods
     -------
@@ -1438,106 +1545,814 @@ class CatboostRankerMother(CatBoostRanker, _CatboostModelMotherBase, _CatboostHy
 
     Notes
     -----
-    Pass ``group_id`` via ``fit_params`` (or via the ``params`` argument when sklearn
-    metadata routing is enabled) when using this model with cross-validation helpers.
+        - This class does not automatically enable sklearn metadata routing.
+            Callers must enable it explicitly via
+            ``sklearn.set_config(enable_metadata_routing=True)`` before using
+            ``group_id`` during training.
+    - Passing an explicit Pairwise ``loss_function`` (e.g. ``"YetiRankPairwise"``) together
+      with an incompatible ``grow_policy`` or ``boosting_type`` raises a ``ValueError``
+      immediately at construction time.
     """
 
     def __init__(
         self,
         target_type: props.TargetType = "single_target",
+        tune_pairwise_type: bool = False,
         tune_boosting_type: bool = False,
         tune_tree_structure_type: bool = True,
+        tune_loss_function: bool = True,
+        model_type: props.ModelType = "ranking",
+        top: Optional[int] = 0,
+        max_pairs: Optional[int] = None,
         **kwargs,
     ):
         """
         Initialize CatboostRankerMother.
 
+        Validates compatibility between pairwise tuning flags and tree/boosting
+        configuration at construction time:
+
+        - If ``tune_pairwise_type=True`` is combined with
+          ``tune_tree_structure_type=True`` or ``tune_boosting_type=True``, a
+          warning is logged and ``tune_pairwise_type`` is set to ``False``
+          (pairwise losses require fixed ``SymmetricTree`` + ``Plain`` and
+          cannot coexist with dynamic Optuna categoricals for those params).
+        - If an explicit Pairwise ``loss_function`` (e.g.
+          ``"YetiRankPairwise"``) is passed together with an incompatible
+          ``grow_policy`` or ``boosting_type``, a ``ValueError`` is raised.
+
         Args:
             target_type : props.TargetType, optional
-                Target type ("single_target" for ranking).
+                Target type (``"single_target"`` for ranking).
+            tune_pairwise_type : bool, optional
+                Whether to include Pairwise loss functions in the
+                hyperparameter search space.  Requires both
+                ``tune_tree_structure_type=False`` and
+                ``tune_boosting_type=False``; otherwise automatically
+                disabled with a warning.
             tune_boosting_type : bool, optional
-                Whether to tune boosting_type.
+                Whether to tune ``boosting_type``.
             tune_tree_structure_type : bool, optional
-                Whether to include the "grow_policy" parameter in hyperparameter tuning.
+                Whether to include the ``grow_policy`` parameter in
+                hyperparameter tuning.
+            tune_loss_function : bool, optional
+                Whether to include the loss function in the hyperparameter
+                search space.  If ``False``, the loss function is fixed at
+                construction time and ``suggested_params_loss`` is not called
+                during Optuna tuning.  Defaults to ``True``.
+            top : Optional[int], optional
+                Maximum number of top samples for NDCG calculation.
+                Only works with YetiRank loss function.
+            max_pairs : Optional[int], optional
+                Maximum number of pairs to generate for PairLogit losses.
             **kwargs
                 Additional CatBoostRanker parameters.
-        """
-        self.model_type: props.ModelType = "ranking"
-        self.target_type: props.TargetType = target_type
 
+        Raises:
+            ValueError
+                If an explicit Pairwise ``loss_function`` is passed with an
+                incompatible ``grow_policy`` (not ``SymmetricTree``) or
+                ``boosting_type`` (not ``Plain``).
+        """
         # Initialize hyperparameter tuning configuration
-        _CatboostHyperParams.__init__(self, tune_boosting_type, tune_tree_structure_type)
+        _CatboostHyperParams.__init__(self, tune_boosting_type, tune_tree_structure_type, tune_loss_function)
+
+        if model_type != "ranking":
+            raise ValueError("model_type for CatboostRankerMother must be 'ranking'.")
+
+        self.model_type: props.ModelType = model_type
+        self.target_type: props.TargetType = target_type
+        self.tune_pairwise_type: bool = tune_pairwise_type
+        self.top: Optional[int] = top
+        self.max_pairs: Optional[int] = max_pairs
+
+        # --- Validate pairwise tuning compatibility ---
+        # Pairwise losses (YetiRankPairwise, PairLogitPairwise) require SymmetricTree + Plain
+        # boosting.  When tune_tree_structure_type or tune_boosting_type is True the tree
+        # structure / boosting type varies across Optuna trials, so we cannot guarantee
+        # compatibility.  Adding pairwise losses to a dynamic categorical list would also
+        # break Optuna (it requires a fixed set of choices per parameter name).
+        if self.tune_pairwise_type:
+            conflicts: list[str] = []
+            if self.tune_tree_structure_type:
+                conflicts.append("tune_tree_structure_type=True")
+            if self.tune_boosting_type:
+                conflicts.append("tune_boosting_type=True")
+
+            if conflicts:
+                module_logger.warning(
+                    "tune_pairwise_type=True is incompatible with %s. "
+                    "Pairwise losses require SymmetricTree grow_policy and Plain boosting_type, "
+                    "but these settings vary across Optuna trials when their tuning flags are "
+                    "enabled.  Pairwise losses will be excluded from the search space. "
+                    "To enable pairwise tuning, set %s.",
+                    " and ".join(conflicts),
+                    " and ".join(c.replace("True", "False") for c in conflicts),
+                )
+                self.tune_pairwise_type = False
+
+        # Validate explicit pairwise loss_function against grow_policy / boosting_type
+        explicit_loss = kwargs.get("loss_function", "")
+        if "Pairwise" in str(explicit_loss):
+            explicit_grow = kwargs.get("grow_policy", "SymmetricTree")
+            explicit_boost = kwargs.get("boosting_type", "Plain")
+            incompatible: list[str] = []
+            if explicit_grow != "SymmetricTree":
+                incompatible.append(f"grow_policy='{explicit_grow}' (must be 'SymmetricTree')")
+            if explicit_boost != "Plain":
+                incompatible.append(f"boosting_type='{explicit_boost}' (must be 'Plain')")
+            if incompatible:
+                raise ValueError(
+                    f"Pairwise loss '{explicit_loss}' requires SymmetricTree grow_policy "
+                    f"and Plain boosting_type, but got {', '.join(incompatible)}."
+                )
+            # A fixed Pairwise loss also requires fixed tree structure and boosting type
+            # during Optuna tuning; disable the flags if they would vary across trials.
+            tuning_conflicts: list[str] = []
+            if self.tune_tree_structure_type:
+                tuning_conflicts.append("tune_tree_structure_type")
+            if self.tune_boosting_type:
+                tuning_conflicts.append("tune_boosting_type")
+            if tuning_conflicts:
+                module_logger.warning(
+                    "Explicit Pairwise loss '%s' requires fixed SymmetricTree + Plain boosting, "
+                    "but %s are True. Disabling them to prevent incompatible Optuna trials.",
+                    explicit_loss,
+                    " and ".join(tuning_conflicts),
+                )
+                self.tune_tree_structure_type = False
+                self.tune_boosting_type = False
 
         if "loss_function" not in list(kwargs):
-            kwargs["loss_function"] = utils.default_loss_function("ranking", target_type)
+            if self.top is not None and self.top > 0:
+                kwargs["loss_function"] = f"YetiRank:mode=NDCG;top={self.top}"
+            else:
+                kwargs["loss_function"] = "YetiRank:mode=Classic"
+
+        elif (
+            "PairLogit" in kwargs["loss_function"]
+            and "max_pairs" not in kwargs["loss_function"]
+            and isinstance(self.max_pairs, int)
+            and not isinstance(self.max_pairs, bool)
+            and self.max_pairs > 0
+        ):
+            if ":" not in kwargs["loss_function"] and ";" in kwargs["loss_function"]:
+                kwargs["loss_function"] = kwargs["loss_function"].replace(";", ":", 1)
+            sep = ";" if ":" in kwargs["loss_function"] else ":"
+            kwargs["loss_function"] += f"{sep}max_pairs={self.max_pairs}"
+
+        # Always enable posterior_sampling for uncertainty estimation
+        if "posterior_sampling" not in kwargs:
+            kwargs["posterior_sampling"] = True
 
+        # Apply defaults, excluding building block parameters that are only for Optuna
+        # These parameters (base_loss, mode, dcg_denominator, dcg_type) are combined into loss_function
+        tuning_building_blocks = ("base_loss", "mode", "dcg_denominator", "dcg_type")
         for key, val in self.default_parameters().items():
-            if key not in list(kwargs):
+            if key not in list(kwargs) and key not in tuning_building_blocks:
                 kwargs[key] = val
 
         CatBoostRanker.__init__(self, **kwargs)
 
-    # def get_params(self, deep=True):
-    #     """
-    #     Override get_params to include custom parameters like target_type.
-    #     """
-    #     params = super().get_params(deep=deep)
-    #     params.update(
-    #         {
-    #             "target_type": self.target_type,
-    #             "tune_boosting_type": self.tune_boosting_type,
-    #             "tune_tree_structure_type": self.tune_tree_structure_type,
-    #         }
-    #     )
-    #     return params
-
-    # def set_params(self, **params):
-    #     """
-    #     Override set_params to handle custom parameters like target_type.
-    #     """
-    #     for param in ["target_type", "tune_boosting_type", "tune_tree_structure_type"]:
-    #         if param in params:
-    #             setattr(self, param, params[param])
-    #             params.pop(param, None)
-    #     return super().set_params(**params)
-
-    # def __getstate__(self):
-    #     """
-    #     Custom getstate to handle pickling of the model.
-    #     This ensures all necessary attributes are included when the object is pickled.
-
-    #     Returns:
-    #         dict: A dictionary containing the state of the object.
-    #     """
-    #     state = super().__getstate__()
-    #     state.update(
-    #         {
-    #             "target_type": self.target_type,
-    #             "tune_boosting_type": self.tune_boosting_type,
-    #             "tune_tree_structure_type": self.tune_tree_structure_type,
-    #         }
-    #     )
-    #     return state
-
-    # def __setstate__(self, state):
-    #     """
-    #     Custom setstate to handle unpickling of the model.
-    #     This ensures all attributes are properly restored when the object is unpickled.
-
-    #     Args:
-    #         state (dict): A dictionary containing the state of the object.
-    #     """
-    #     self.target_type = state.pop("target_type", "single_target")
-    #     self.tune_boosting_type = state.pop("tune_boosting_type", False)
-    #     self.tune_tree_structure_type = state.pop("tune_tree_structure_type", True)
-    #     super().__setstate__(state)
+    def get_params(self, deep: bool = True) -> dict:
+        """
+        Override get_params to include custom parameters like target_type.
+        """
+        params = super().get_params(deep=deep)
+        params.update(
+            {
+                "target_type": self.target_type,
+                "model_type": self.model_type,
+                "tune_pairwise_type": self.tune_pairwise_type,
+                "tune_boosting_type": self.tune_boosting_type,
+                "tune_tree_structure_type": self.tune_tree_structure_type,
+                "tune_loss_function": self.tune_loss_function,
+                "top": self.top,
+                "max_pairs": self.max_pairs,
+            }
+        )
+        return params
 
-    def default_parameters(self, prefix: str = "") -> dict:
-        return {
+    def set_params(self, **params) -> "CatboostRankerMother":
+        """
+        Override set_params to handle custom parameters like target_type.
+        """
+        explicit_loss_function = "loss_function" in params
+        top_changed = "top" in params
+        max_pairs_changed = "max_pairs" in params
+        if "model_type" in params and params["model_type"] != "ranking":
+            raise ValueError("model_type for CatboostRankerMother must be 'ranking'.")
+        params.pop("model_type", None)
+        self.model_type = "ranking"
+
+        for param in [
+            "target_type",
+            "tune_pairwise_type",
+            "tune_boosting_type",
+            "tune_tree_structure_type",
+            "tune_loss_function",
+            "top",
+            "max_pairs",
+        ]:
+            if param in params:
+                setattr(self, param, params.pop(param))
+
+        if self.tune_pairwise_type and (self.tune_tree_structure_type or self.tune_boosting_type):
+            module_logger.warning(
+                "tune_pairwise_type=True is incompatible with tune_tree_structure_type=True or "
+                "tune_boosting_type=True; disabling tune_pairwise_type."
+            )
+            self.tune_pairwise_type = False
+
+        if top_changed or max_pairs_changed:
+            current_loss = str(self.get_params(deep=False).get("loss_function", ""))
+            effective_loss = str(params.get("loss_function", current_loss))
+            updated_loss = effective_loss
+
+            if top_changed and not explicit_loss_function and "YetiRank" in updated_loss:
+                updated_loss = re.sub(r";top=[^;]+", "", updated_loss)
+                if self.top is not None and self.top > 0:
+                    updated_loss = re.sub(r"mode=Classic", "mode=NDCG", updated_loss)
+                    if "mode=" not in updated_loss:
+                        updated_loss += ":mode=NDCG"
+                    updated_loss += f";top={self.top}"
+                elif "mode=Classic" in updated_loss:
+                    updated_loss = re.sub(r"[;:]dcg_(?:denominator|type)=[^;:]*", "", updated_loss)
+
+            if "PairLogit" in updated_loss and max_pairs_changed and not explicit_loss_function:
+                updated_loss = re.sub(r";max_pairs=[^;]+|:max_pairs=[^;]+", "", updated_loss)
+
+            if "PairLogit" in updated_loss and max_pairs_changed:
+                has_max_pairs = re.search(r"[;:]max_pairs=[^;:]+", updated_loss) is not None
+                valid_max_pairs = (
+                    isinstance(self.max_pairs, int) and not isinstance(self.max_pairs, bool) and self.max_pairs > 0
+                )
+                if valid_max_pairs and not has_max_pairs:
+                    if ":" not in updated_loss and ";" in updated_loss:
+                        updated_loss = updated_loss.replace(";", ":", 1)
+                    separator = ";" if ":" in updated_loss else ":"
+                    updated_loss += f"{separator}max_pairs={self.max_pairs}"
+
+            if updated_loss != effective_loss:
+                params["loss_function"] = updated_loss
+
+        # Keep set_params invariant with __init__: explicit Pairwise losses
+        # require SymmetricTree grow_policy and Plain boosting_type.
+        current_params = self.get_params(deep=False)
+        effective_loss = str(params.get("loss_function", current_params.get("loss_function", "")))
+        if "Pairwise" in effective_loss:
+            if self.tune_tree_structure_type or self.tune_boosting_type:
+                module_logger.warning(
+                    "Pairwise loss requires fixed SymmetricTree grow_policy and Plain boosting_type; "
+                    "disabling pairwise-incompatible tuning flags."
+                )
+                self.tune_tree_structure_type = False
+                self.tune_boosting_type = False
+
+            effective_grow = params.get("grow_policy", current_params.get("grow_policy", "SymmetricTree"))
+            effective_boost = params.get("boosting_type", current_params.get("boosting_type", "Plain"))
+            incompatible: list[str] = []
+            if effective_grow != "SymmetricTree":
+                incompatible.append(f"grow_policy='{effective_grow}' (must be 'SymmetricTree')")
+            if effective_boost != "Plain":
+                incompatible.append(f"boosting_type='{effective_boost}' (must be 'Plain')")
+            if incompatible:
+                raise ValueError(
+                    f"Pairwise loss '{effective_loss}' requires SymmetricTree grow_policy "
+                    f"and Plain boosting_type, but got {', '.join(incompatible)}."
+                )
+
+        return super().set_params(**params)
+
+    def __getstate__(self) -> dict:
+        """
+        Custom getstate to handle pickling of the model.
+        This ensures all necessary attributes are included when the object is pickled.
+
+        Returns:
+            dict: A dictionary containing the state of the object.
+        """
+        state = super().__getstate__()
+        state.update(
+            {
+                "target_type": self.target_type,
+                "model_type": self.model_type,
+                "tune_pairwise_type": self.tune_pairwise_type,
+                "tune_boosting_type": self.tune_boosting_type,
+                "tune_tree_structure_type": self.tune_tree_structure_type,
+                "tune_loss_function": self.tune_loss_function,
+                "top": self.top,
+                "max_pairs": self.max_pairs,
+            }
+        )
+        return state
+
+    def __setstate__(self, state: dict) -> None:
+        """
+        Custom setstate to handle unpickling of the model.
+        This ensures all attributes are properly restored when the object is unpickled.
+
+        Args:
+            state (dict): A dictionary containing the state of the object.
+        """
+        self.target_type = state.pop("target_type", "single_target")
+        self.model_type = state.pop("model_type", "ranking")
+        self.tune_pairwise_type = state.pop("tune_pairwise_type", False)
+        self.tune_boosting_type = state.pop("tune_boosting_type", False)
+        self.tune_tree_structure_type = state.pop("tune_tree_structure_type", True)
+        self.tune_loss_function = state.pop("tune_loss_function", True)
+        self.top = state.pop("top", 0)
+        self.max_pairs = state.pop("max_pairs", None)
+        super().__setstate__(state)
+
+    def predict(
+        self,
+        X: pd.DataFrame,
+        ntree_start: int = 0,
+        ntree_end: int = 0,
+        thread_count: int = -1,
+        verbose: Optional[bool] = None,
+        use_ranks: bool = False,
+        normalize_by_group_size: bool = False,
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        Predict scores or ranks for a single query group.
+
+        When ``use_ranks`` is False (default) the method returns raw scores as produced
+        by CatBoost.  When ``use_ranks`` is True the method returns 1-based integer ranks
+        where rank 1 corresponds to the highest score (descending order).  The output
+        order matches the input row order.
+
+        For datasets containing multiple query groups, use the module-level helper
+        :func:`ranker_predict_for_groups` which calls this method per group.
+
+        Note on Ranking Convention
+        ---------------------------
+        CatBoost assigns higher scores to better-ranked samples, so rank 1 is given to
+        the sample with the highest score (descending order). This matches the standard
+        "rank 1 = best" interpretation used throughout the Mother framework.
+
+        Parameters
+        ----------
+        X : pd.DataFrame or array-like
+            Features for the samples (n_samples, n_features).
+        ntree_start, ntree_end, thread_count, verbose : passed to CatBoost ``predict``.
+        use_ranks : bool
+            If True, return 1-based integer ranks (rank 1 = highest score).
+        normalize_by_group_size : bool
+            If True and ``use_ranks`` is True, divide ranks by ``len(X)``.
+            Has no effect when ``use_ranks`` is False.
+        **kwargs
+            Additional keyword arguments forwarded to CatBoost ``predict``.
+
+        Returns
+        -------
+        np.ndarray
+            If ``use_ranks`` is False: array of raw scores (floats) in the same order as ``X``.
+            If ``use_ranks`` is True: array of integer ranks (1-based) corresponding to rows
+            in ``X``, optionally normalised by group size.
+        """
+        preds = super().predict(
+            X, ntree_start=ntree_start, ntree_end=ntree_end, thread_count=thread_count, verbose=verbose, **kwargs
+        )
+
+        if not use_ranks:
+            return preds
+
+        rank_values = utils.scores_to_ranks(preds).astype(float)
+        if normalize_by_group_size:
+            return np.round(rank_values / len(X), 4)
+        return rank_values.astype(int)
+
+    def predict_uncertainty(
+        self,
+        X: pd.DataFrame,
+        n_ensembles: int = 10,
+        n_threads: int = 1,
+        use_ranks: bool = False,
+        uncertainty_for_opt: bool = False,
+        normalize_by_group_size: bool = False,
+        return_quantiles: bool = False,
+        return_raw: bool = False,
+    ) -> pd.DataFrame | tuple[pd.DataFrame, np.ndarray]:
+        """
+        Estimate rank uncertainty using virtual ensembles.
+
+        Uses CatBoost's ``virtual_ensembles_predict`` through the shared Mother utility.
+        Operates on a single ranking group; for multi-group datasets use the module-level
+        helper :func:`ranker_predict_uncertainty_for_groups`.
+
+        This implementation exposes ``use_ranks`` (not ``ranks``), computes
+        rank/score uncertainty from virtual-ensemble dispersion, and sets
+        ``data_uncertainty`` / ``total_uncertainty`` to ``None`` for ranking.
+
+        Args:
+            X : pd.DataFrame
+                Feature matrix (n_samples, n_features) for a single ranking group.
+            n_ensembles : int, optional
+                Number of virtual ensembles used by CatBoost for uncertainty estimation.
+                Must be >= 1.
+            n_threads : int, optional
+                Number of threads passed to ``virtual_ensembles_predict``.
+            use_ranks : bool, optional
+                If True, convert virtual-ensemble scores to per-ensemble ranks and
+                compute ``mean_predictions`` as the mean of those per-ensemble ranks,
+                with ``knowledge_uncertainty`` as their standard deviation.
+                If False (default), quantities stay on the raw score scale.
+            uncertainty_for_opt : bool, optional
+                If True, return only ``knowledge_uncertainty`` for optimisation.
+            normalize_by_group_size : bool, optional
+                If True, divide ``knowledge_uncertainty`` and ``mean_predictions``
+                by ``len(X)``.  Default is ``False``.
+            return_quantiles : bool, optional
+                If True, append score-quantile columns derived from
+                ``DEFAULT_QUANTILES`` (defaults: ``score_q25``, ``score_q50``,
+                ``score_q75``), computed across virtual ensembles on raw scores
+                when ``use_ranks=False`` and on ensemble ranks when
+                ``use_ranks=True``.
+                Ignored when ``uncertainty_for_opt=True``.  Default is ``False``.
+            return_raw : bool, optional
+                If True, return a tuple ``(uncertainty_df, raw_scores)`` where
+                ``raw_scores`` is the raw score matrix from virtual ensembles,
+                shape ``(n_samples, n_ensembles)``. Useful for downstream analysis
+                like ``topk_score_variance`` and ``groupwise_topk_analysis``.
+                For rankers, do not set this to True when using ``mother_cv``:
+                cross-validation expects one uncertainty DataFrame per fold,
+                not the ``(uncertainty_df, raw_scores)`` tuple.
+                Ignored when ``uncertainty_for_opt=True``.  Default is ``False``.
+
+        Returns:
+            pd.DataFrame | tuple[pd.DataFrame, np.ndarray]:
+                If ``return_raw=False`` (default): returns pd.DataFrame with columns:
+                    - ``pred``: full-model prediction (raw score or 1-based rank).
+                    - ``mean_predictions``: mean of per-ensemble scores (``use_ranks=False``)
+                      or mean of per-ensemble ranks (``use_ranks=True``).
+                    - ``knowledge_uncertainty``: std of per-ensemble scores or ranks;
+                      optionally normalised by group size.
+                    - ``data_uncertainty``: ``None`` (not available for ranking).
+                    - ``total_uncertainty``: ``None`` (not available for ranking).
+                    - Quantile columns from ``DEFAULT_QUANTILES`` *(only when*
+                        ``return_quantiles=True`` *; defaults: ``score_q25``,
+                        ``score_q50``, ``score_q75``)*: empirical quantiles of score
+                        or rank distributions.
+
+                If ``return_raw=True``: returns tuple ``(uncertainty_df, raw_scores)``
+                where ``raw_scores`` is np.ndarray of shape ``(n_samples, n_ensembles)``
+                containing raw scores from virtual ensembles.
+        """
+        if n_ensembles < 1:
+            raise ValueError(f"n_ensembles must be >= 1, got {n_ensembles}.")
+
+        result_index = X.index
+
+        quantile_levels = sorted({float(q) for q in DEFAULT_QUANTILES})
+        quantile_columns = [f"score_q{int(q * 100):02d}" for q in quantile_levels]
+
+        if uncertainty_for_opt:
+            result_df = pd.DataFrame(index=result_index, columns=["knowledge_uncertainty"])
+        else:
+            base_cols = ["pred", "mean_predictions", "knowledge_uncertainty", "data_uncertainty", "total_uncertainty"]
+            if return_quantiles:
+                base_cols += quantile_columns
+            result_df = pd.DataFrame(index=result_index, columns=base_cols)
+
+        uncertainty_df, raw_scores = cast(
+            tuple[pd.DataFrame, np.ndarray],
+            utils.get_virtual_prediction(
+                X=X,
+                model=cast(Any, self),
+                virtual_ensembles_count=n_ensembles,
+                thread_count=n_threads,
+            ),
+        )
+
+        mean_scores = np.asarray(uncertainty_df["mean_predictions"], dtype=float)
+
+        if use_ranks:
+            raw_scores_arr = np.asarray(raw_scores, dtype=float)
+            if raw_scores_arr.ndim != 2:
+                raise ValueError(f"Expected 2D raw_scores, got {raw_scores_arr.ndim}D.")
+
+            # Reuse the matrix helper, which internally applies scores_to_ranks per ensemble column.
+            rank_ensembles = utils.scores_matrix_to_ranks(raw_scores_arr)
+            mean_predictions = rank_ensembles.mean(axis=1)
+            ddof = 1 if rank_ensembles.shape[1] > 1 else 0
+            knowledge_uncertainty = np.nan_to_num(rank_ensembles.std(axis=1, ddof=ddof), nan=0.0)
+            quantile_source = rank_ensembles
+        else:
+            mean_predictions = mean_scores
+            knowledge_uncertainty = np.asarray(uncertainty_df["knowledge_uncertainty"], dtype=float)
+            quantile_source = np.asarray(raw_scores, dtype=float)
+
+        if quantile_source.ndim != 2:
+            raise ValueError(f"Expected 2D quantile_source, got {quantile_source.ndim}D.")
+
+        if normalize_by_group_size:
+            n = len(X)
+            knowledge_uncertainty = np.round(knowledge_uncertainty / n, 4)
+            mean_predictions = np.round(mean_predictions / n, 4)
+
+        if uncertainty_for_opt:
+            result_df["knowledge_uncertainty"] = knowledge_uncertainty
+        else:
+            result_df["pred"] = self.predict(
+                X,
+                use_ranks=use_ranks,
+                normalize_by_group_size=normalize_by_group_size,
+            )
+            result_df["mean_predictions"] = mean_predictions
+            result_df["knowledge_uncertainty"] = knowledge_uncertainty
+            result_df["data_uncertainty"] = None
+            result_df["total_uncertainty"] = None
+
+            if return_quantiles:
+                quantile_values = np.percentile(quantile_source, [q * 100 for q in quantile_levels], axis=1)
+                if normalize_by_group_size:
+                    quantile_values = np.round(quantile_values / len(X), 4)
+                for col_name, col_values in zip(quantile_columns, quantile_values):
+                    result_df[col_name] = col_values
+
+        # Keep numeric columns numeric after incremental assignment through loc.
+        for col in result_df.columns:
+            if col not in {"data_uncertainty", "total_uncertainty"}:
+                result_df[col] = pd.to_numeric(result_df[col], errors="coerce")
+
+        # Return raw scores if requested (for downstream analysis functions)
+        if return_raw and not uncertainty_for_opt:
+            return result_df, np.asarray(raw_scores, dtype=float)
+
+        return result_df
+
+    def suggested_params_loss(
+        self,
+        trial: Trial,
+        suggested_params: dict[str, Any],
+        y: Union[pd.DataFrame, pd.Series],
+        prefix: str,
+    ) -> dict[str, Any]:
+        """
+        Add ranking-loss-specific hyperparameters to the suggested parameters.
+
+        Builds a composite ``loss_function`` string from individually suggested
+        building-block parameters (``base_loss``, ``mode``, ``dcg_denominator``,
+        ``dcg_type``) and removes the building blocks afterwards so that only
+        the final ``loss_function`` is passed to CatBoost.
+
+        Pairwise losses (``YetiRankPairwise``, ``PairLogitPairwise``) are only
+        included in the Optuna search space when ``tune_pairwise_type=True``
+        **and** the current trial's ``grow_policy`` / ``boosting_type`` are
+        compatible (``SymmetricTree`` + ``Plain``).  The ``__init__`` method
+        already guarantees ``tune_pairwise_type=False`` when the tuning flags
+        would conflict, so the runtime check here only inspects the actual
+        trial values.
+
+        Args:
+            trial : optuna.trial.Trial
+                Optuna trial object.
+            suggested_params : dict[str, Any]
+                Current suggested parameters (mutated in place).
+            y : Union[pd.DataFrame, pd.Series]
+                Target data.
+            prefix : str
+                Parameter prefix.
+
+        Returns:
+            dict[str, Any]: Updated suggested parameters.
+        """
+        if self.target_type == "multi_target":
+            return suggested_params
+
+        # Check if target is binary (only contains 0 and 1)
+        is_binary: bool = bool(np.array_equal(np.unique(y), [0, 1]))
+
+        # Check if the current trial's tree structure and boosting type are
+        # compatible with Pairwise losses (require SymmetricTree + Plain).
+        # Note: __init__ already guarantees tune_pairwise_type=False when
+        # tune_tree_structure_type or tune_boosting_type are True, so we only
+        # need to verify the actual values chosen for this trial.
+        grow_policy: Optional[str] = suggested_params.get(prefix + "grow_policy")
+        boosting_type: str = suggested_params.get(
+            prefix + "boosting_type",
+            self.get_params(deep=False).get("boosting_type", "Plain"),
+        )
+        can_use_pairwise: bool = grow_policy == "SymmetricTree" and boosting_type == "Plain"
+
+        # Build list of possible loss functions
+        has_top: bool = self.top is not None and self.top > 0
+
+        # When top is specified, only use YetiRank (top parameter only works with YetiRank)
+        suggested_base_loss: str
+        if has_top:
+            if self.tune_pairwise_type and can_use_pairwise:
+                suggested_base_loss = trial.suggest_categorical(prefix + "base_loss", ["YetiRank", "YetiRankPairwise"])
+            else:
+                suggested_base_loss = "YetiRank"
+        else:
+            base_losses: list[str] = ["YetiRank", "PairLogit"]
+
+            # Add QuerySoftMax if the target y is binary, otherwise use QueryRMSE
+            if is_binary:
+                base_losses.append("QuerySoftMax")
+            else:
+                base_losses.append("QueryRMSE")
+
+            # Only add Pairwise losses when pairwise tuning is enabled and the
+            # current trial's tree/boosting settings are compatible
+            if self.tune_pairwise_type and can_use_pairwise:
+                base_losses.extend(["YetiRankPairwise", "PairLogitPairwise"])
+
+            suggested_base_loss = trial.suggest_categorical(prefix + "base_loss", base_losses)
+
+        if suggested_base_loss in ["YetiRank", "YetiRankPairwise"]:
+            # Suggest mode (NDCG or Classic)
+            suggested_mode: str
+            if has_top:
+                if is_binary:
+                    suggested_mode = trial.suggest_categorical(prefix + "mode", ["NDCG", "MAP"])
+                else:
+                    suggested_mode = "NDCG"
+            else:
+                if is_binary:
+                    suggested_mode = trial.suggest_categorical(prefix + "mode", ["NDCG", "Classic", "MAP"])
+                else:
+                    suggested_mode = trial.suggest_categorical(prefix + "mode", ["NDCG", "Classic"])
+
+            # Build loss function string
+            suggested_loss_function: str = f"{suggested_base_loss}:mode={suggested_mode}"
+
+            # Add NDCG-specific parameters
+            if suggested_mode == "NDCG":
+                suggested_dcg_denominator: str = trial.suggest_categorical(
+                    prefix + "dcg_denominator", ["LogPosition", "Position"]
+                )
+                suggested_loss_function += f";dcg_denominator={suggested_dcg_denominator}"
+
+                suggested_dcg_type: str = trial.suggest_categorical(prefix + "dcg_type", ["Base", "Exp"])
+                suggested_loss_function += f";dcg_type={suggested_dcg_type}"
+
+            if suggested_mode != "Classic" and has_top:
+                suggested_loss_function += f";top={self.top}"
+
+            suggested_params[prefix + "loss_function"] = suggested_loss_function
+
+        else:
+            # Non-YetiRank losses (QueryRMSE, QuerySoftMax, PairLogit, PairLogitPairwise)
+            loss_function: str = suggested_base_loss
+
+            # Add max_pairs parameter for PairLogit losses if specified
+            if (
+                "PairLogit" in suggested_base_loss
+                and isinstance(self.max_pairs, int)
+                and not isinstance(self.max_pairs, bool)
+                and self.max_pairs > 0
+            ):
+                loss_function += f":max_pairs={self.max_pairs}"
+
+            suggested_params[prefix + "loss_function"] = loss_function
+
+        # Remove building block parameters — only loss_function should be passed to CatBoost
+        # (similar to how Focal loss removes alpha/gamma after building the loss string)
+        for param in ["base_loss", "mode", "dcg_denominator", "dcg_type"]:
+            suggested_params.pop(prefix + param, None)
+
+        return suggested_params
+
+    def default_parameters(self, prefix: str = "") -> dict[str, Any]:
+        """
+        Returns the default recommended parameters for the CatBoostRanker.
+
+        The returned dictionary includes Optuna building-block keys (``base_loss``,
+        ``mode``, ``dcg_denominator``, ``dcg_type``) that are used by the tuning
+        logic to compose the composite ``loss_function`` string.  These building-block
+        keys are **not** passed directly to CatBoost — they are consumed and removed
+        by :meth:`suggested_params_loss`.
+
+        When ``top`` is set, the default mode is ``NDCG`` with
+        ``dcg_denominator=Position`` and ``dcg_type=Base``; otherwise the default
+        mode is ``Classic``.
+
+        Args:
+            prefix : str, optional
+                Optional prefix for parameter names (default: ``""``).
+
+        Returns:
+            dict[str, Any]: Default parameters for the ranker.
+        """
+        defaults: dict[str, Any] = {
             prefix + "learning_rate": 0.03,
             prefix + "bootstrap_type": "MVS",
             prefix + "random_strength": 1,
             prefix + "grow_policy": "SymmetricTree",
             prefix + "boosting_type": "Plain",
             prefix + "max_depth": 6,
+            prefix + "base_loss": "YetiRank",
         }
+
+        if self.top is not None and self.top > 0:
+            defaults[prefix + "mode"] = "NDCG"
+            defaults[prefix + "dcg_denominator"] = "Position"
+            defaults[prefix + "dcg_type"] = "Base"
+        else:
+            defaults[prefix + "mode"] = "Classic"
+
+        return defaults
+
+
+def ranker_predict_for_groups(
+    model: "CatboostRankerMother",
+    X: pd.DataFrame,
+    group_id: np.ndarray,
+    use_ranks: bool = False,
+    normalize_by_group_size: bool = False,
+) -> np.ndarray:
+    """Predict scores or ranks for a dataset containing multiple ranking groups.
+
+    Calls :meth:`CatboostRankerMother.predict` independently for each group so
+    that when ``use_ranks=True`` ranks are assigned within each group rather than
+    globally across the whole dataset.
+
+    Parameters
+    ----------
+    model : CatboostRankerMother
+        A fitted ranker.
+    X : pd.DataFrame
+        Feature matrix (n_samples, n_features).
+    group_id : np.ndarray
+        Group IDs aligned with ``X`` rows (one entry per row).
+    use_ranks : bool
+        Forwarded to :meth:`~CatboostRankerMother.predict`.
+    normalize_by_group_size : bool
+        Forwarded to :meth:`~CatboostRankerMother.predict`.
+
+    Returns
+    -------
+    np.ndarray
+        Predictions in the original row order of ``X``.
+    """
+    group_arr = _validate_ranking_group_id(group_id, len(X))
+    result_dtype = int if use_ranks and not normalize_by_group_size else float
+    result = np.empty(len(X), dtype=result_dtype)
+
+    for idx in _iter_ranking_group_indices(group_arr):
+        X_group = X.iloc[idx] if isinstance(X, pd.DataFrame) else X[idx]
+        group_pred = model.predict(
+            X_group,
+            use_ranks=use_ranks,
+            normalize_by_group_size=normalize_by_group_size,
+        )
+        if result_dtype is int:
+            result[idx] = np.asarray(group_pred, dtype=int)
+        else:
+            result[idx] = group_pred
+
+    return result
+
+
+def ranker_predict_uncertainty_for_groups(
+    model: "CatboostRankerMother",
+    X: pd.DataFrame,
+    group_id: np.ndarray,
+    **kwargs,
+) -> pd.DataFrame:
+    """Estimate uncertainty for a dataset containing multiple ranking groups.
+
+    Calls :meth:`CatboostRankerMother.predict_uncertainty` independently for
+    each group so that rank conversion and normalisation are applied within
+    group boundaries.
+
+    Parameters
+    ----------
+    model : CatboostRankerMother
+        A fitted ranker.
+    X : pd.DataFrame
+        Feature matrix (n_samples, n_features).
+    group_id : np.ndarray
+        Group IDs aligned with ``X`` rows.
+    **kwargs
+        Forwarded to :meth:`~CatboostRankerMother.predict_uncertainty`.
+
+    Returns
+    -------
+    pd.DataFrame
+        Concatenated uncertainty DataFrame in the original row order of ``X``,
+        with the same columns as :meth:`~CatboostRankerMother.predict_uncertainty`.
+    """
+    if kwargs.get("return_raw", False):
+        raise ValueError(
+            "ranker_predict_uncertainty_for_groups does not support return_raw=True; "
+            "call predict_uncertainty separately for each group to access raw scores."
+        )
+
+    group_arr = _validate_ranking_group_id(group_id, len(X))
+    frames: list[pd.DataFrame] = []
+
+    for idx in _iter_ranking_group_indices(group_arr):
+        X_group = X.iloc[idx] if isinstance(X, pd.DataFrame) else X[idx]
+        group_frame = model.predict_uncertainty(X_group, **kwargs)
+        group_frame.index = idx
+        frames.append(group_frame)
+
+    result = pd.concat(frames).sort_index()
+    result.index = X.index
+    return result
diff --git a/src/mother/ml/utils.py b/src/mother/ml/utils.py
index 9517568..5edab42 100644
--- a/src/mother/ml/utils.py
+++ b/src/mother/ml/utils.py
@@ -4,7 +4,7 @@
 
 import numpy as np
 import pandas as pd
-from catboost import CatBoost, CatBoostClassifier, CatBoostRegressor
+from catboost import CatBoost, CatBoostClassifier, CatBoostRanker, CatBoostRegressor
 from optuna.trial import Trial
 from scipy.sparse import csr_matrix, issparse
 from scipy.stats import rankdata
@@ -33,6 +33,25 @@ def decorator(func):
 module_logger: logging.Logger = logging.getLogger(__name__)
 
 
+def scores_to_ranks(scores: np.ndarray) -> np.ndarray:
+    """Convert scores to 1-based ranks, with rank 1 assigned to the highest score."""
+    scores = np.asarray(scores).reshape(-1)
+    order = np.argsort(-scores, kind="mergesort")
+    ranks = np.empty_like(order)
+    ranks[order] = np.arange(1, scores.size + 1)
+    return ranks
+
+
+def scores_matrix_to_ranks(score_matrix: np.ndarray) -> np.ndarray:
+    """Convert score matrix columns to per-ensemble 1-based ranks."""
+    arr = np.asarray(score_matrix)
+    if arr.ndim != 2:
+        raise ValueError(f"Expected 2D score_matrix, got {arr.ndim}D.")
+    return np.column_stack([scores_to_ranks(arr[:, ensemble_idx]) for ensemble_idx in range(arr.shape[1])]).astype(
+        float
+    )
+
+
 def default_loss_function(
     model_type: props.ModelType = "classification_binary",
     target_type: props.TargetType = "single_target",
@@ -411,15 +430,34 @@ def default_parameters(self, prefix: str = "") -> dict:
             )
 
 
+@typing.overload
+def get_virtual_prediction(
+    X: pd.DataFrame,
+    model: CatBoostRanker,
+    virtual_ensembles_count: int = 10,
+    thread_count: int = 1,
+) -> typing.Tuple[pd.DataFrame, np.ndarray]: ...
+
+
+@typing.overload
+def get_virtual_prediction(
+    X: pd.DataFrame,
+    model: typing.Union[CatBoostRegressor, CatBoostClassifier],
+    virtual_ensembles_count: int = 10,
+    thread_count: int = 1,
+) -> pd.DataFrame: ...
+
+
 def get_virtual_prediction(
     X: pd.DataFrame,
     model: typing.Union[
         CatBoostRegressor,
         CatBoostClassifier,
+        CatBoostRanker,
     ],
     virtual_ensembles_count: int = 10,
     thread_count: int = 1,
-) -> pd.DataFrame:
+) -> typing.Union[pd.DataFrame, typing.Tuple[pd.DataFrame, np.ndarray]]:
     """
     Generates virtual ensemble predictions using CatBoost's built-in uncertainty prediction.
 
@@ -430,34 +468,91 @@ def get_virtual_prediction(
     ----------
     X : pd.DataFrame
         DataFrame containing the features for prediction.
-    model : typing.Union[CatBoostRegressor, CatBoostClassifier]
-        Trained CatBoost model (either regressor or classifier).
+    model : typing.Union[CatBoostRegressor, CatBoostClassifier, CatBoostRanker]
+        Trained CatBoost model (regressor, classifier, or ranker).
     virtual_ensembles_count : int, optional
         Number of virtual ensembles to use for uncertainty estimation. Default is 10.
     thread_count : int, optional
-        Number of threads is equal to the number of processor cores. Default is 1 (use all available threads).
-
+        Number of threads. Use -1 to use all available threads. Default is 1.
     Returns
     -------
-    pd.DataFrame
-        A DataFrame containing the predictions and uncertainty components with the following columns:
-        - 'mean_predictions': Mean prediction values (None for classifiers)
-        - 'knowledge_uncertainty': Model's epistemic uncertainty (uncertainty in model parameters)
-        - 'data_uncertainty': Aleatoric uncertainty (inherent data noise/variability)
-        - 'total_uncertainty': Sum of knowledge and data uncertainty components
+    pd.DataFrame or tuple[pd.DataFrame, np.ndarray]
+        For ``CatBoostRegressor`` and ``CatBoostClassifier``: a DataFrame with columns
+        ``mean_predictions``, ``knowledge_uncertainty``, ``data_uncertainty``, ``total_uncertainty``.
+
+        For ``CatBoostRanker``: a tuple ``(uncertainty_df, raw_scores)`` where
+        ``uncertainty_df`` has the same columns as above and ``raw_scores`` is a float
+        array of shape ``(n_samples, virtual_ensembles_count)`` with the raw score from
+        each virtual ensemble.  Callers can apply ``scores_to_ranks`` column-wise to
+        ``raw_scores`` to obtain per-ensemble rank distributions.
 
     Notes:
     ---------
     Regression:
-        CatBoost returns preedictive uncertainties as variances. We convert them to standard deviations
-        to improve interpretability.For regression models not using 'RMSEWithUncertainty' loss,
+        CatBoost returns predictive uncertainties as variances. We convert them to standard deviations
+        to improve interpretability. For regression models not using 'RMSEWithUncertainty' loss,
         only mean_predictions and knowledge_uncertainty will have values.
     Classification:
         Uncertainties are entropy-based as defined by CatBoost and are returned without transformation.
+    Ranking:
+        Returns mean raw ranking scores and epistemic uncertainty from virtual ensembles.
+        Knowledge uncertainty is the standard deviation of the virtual-ensemble scores.
     """
 
+    if (
+        not isinstance(virtual_ensembles_count, (int, np.integer))
+        or isinstance(virtual_ensembles_count, bool)
+        or virtual_ensembles_count < 1
+    ):
+        raise ValueError(f"virtual_ensembles_count must be a positive integer, got {virtual_ensembles_count}.")
+    virtual_ensembles_count = int(virtual_ensembles_count)
+
+    if not isinstance(model, (CatBoostRegressor, CatBoostClassifier, CatBoostRanker)):
+        raise ValueError("model must be an instance of CatBoostRegressor, CatBoostClassifier, or CatBoostRanker.")
+
     module_logger.info("Using catboost's builtin uncertainty prediction")
 
+    if isinstance(model, CatBoostRanker):
+        # For rankers, VirtEnsembles gives one score column per virtual ensemble
+        # (shape: n_samples × virtual_ensembles_count), which lets us compute
+        # mean and std directly without a separate TotalUncertainty call.
+        raw_scores = np.asarray(
+            model.virtual_ensembles_predict(
+                X,
+                prediction_type="VirtEnsembles",
+                ntree_end=0,
+                virtual_ensembles_count=virtual_ensembles_count,
+                thread_count=thread_count,
+                verbose=None,
+            )
+        )
+
+        # CatBoost may return either (n_samples, n_ensembles) or
+        # (n_samples, n_ensembles, 1). Handle both shapes safely.
+        if raw_scores.ndim == 3 and raw_scores.shape[-1] == 1:
+            raw_scores = raw_scores[..., 0]
+        elif raw_scores.ndim != 2:
+            raise ValueError(
+                "Unexpected shape returned by CatBoostRanker.virtual_ensembles_predict("
+                f"prediction_type='VirtEnsembles'): {raw_scores.shape}. "
+                "Expected (n_samples, n_ensembles) or (n_samples, n_ensembles, 1)."
+            )
+
+        ddof = 1 if virtual_ensembles_count > 1 else 0
+        knowledge_uncertainty = np.nan_to_num(raw_scores.std(axis=1, ddof=ddof), nan=0.0)
+
+        uncertainty_df = pd.DataFrame(
+            {
+                "mean_predictions": raw_scores.mean(axis=1),
+                "knowledge_uncertainty": knowledge_uncertainty,
+                "data_uncertainty": None,
+                "total_uncertainty": None,
+            },
+            index=X.index,
+        )
+
+        return uncertainty_df, raw_scores
+
     virtual_prediction = model.virtual_ensembles_predict(
         X,
         prediction_type="TotalUncertainty",
@@ -509,7 +604,7 @@ def get_virtual_prediction(
         )
 
     else:
-        raise ValueError("The model must inherit either CatboostClassifier or CatboostRegressor")
+        raise ValueError("The model must inherit CatBoostClassifier, CatBoostRegressor, or CatBoostRanker")
 
 
 def single_group_rank_pred(
@@ -592,3 +687,171 @@ def avg_ndcg_score(
     if verbose:
         print(f"List of every group ndcg score: {ndcg_list}")
     return np.average(ndcg_list)
+
+
+def topk_rank_disagreement(
+    rank_ensembles: np.ndarray,
+    k: int,
+) -> np.ndarray:
+    """Compute per-item probability of disagreement about top-k membership.
+
+    For each sample, let ``p`` be the fraction of ensemble members that place
+    it in the top-k positions. This returns ``2 * p * (1 - p)``, the probability
+    that two independently selected ensemble members disagree about membership.
+    A value of 0.0 means all ensembles agree, whether the item is in or out of
+    the top-k; larger values indicate less stable membership.
+
+    Parameters
+    ----------
+    rank_ensembles : np.ndarray, shape (n_samples, n_ensembles)
+        Rank matrix where each column contains 1-based ranks from one virtual ensemble.
+    k : int
+        Number of top positions to consider.
+
+    Returns
+    -------
+    np.ndarray, shape (n_samples,)
+        Pairwise disagreement probability for each item's top-k membership
+        (range [0, 0.5]).
+    """
+    arr = np.asarray(rank_ensembles)
+    if arr.ndim != 2:
+        raise ValueError(f"Expected 2D rank_ensembles, got {arr.ndim}D.")
+    if k < 1:
+        raise ValueError(f"k must be >= 1, got {k}.")
+    if k > arr.shape[0]:
+        raise ValueError(f"k must be <= the number of items ({arr.shape[0]}), got {k}.")
+    if not np.isfinite(arr).all():
+        raise ValueError("rank_ensembles must contain only finite values.")
+    if (arr < 1).any() or (arr > arr.shape[0]).any():
+        raise ValueError(f"rank_ensembles must be 1-based values in [1, {arr.shape[0]}].")
+    in_topk = arr <= k
+    membership_probability = in_topk.mean(axis=1)
+    return 2.0 * membership_probability * (1.0 - membership_probability)
+
+
+def topk_score_variance(
+    score_ensembles: np.ndarray,
+    k: int,
+    reference_ranks: typing.Optional[np.ndarray] = None,
+) -> typing.Tuple[np.ndarray, np.ndarray]:
+    """Compute score variance for items in the top-k of a reference ranking.
+
+    Identifies items that are in the top-k of a reference ranking (or the
+    ensemble-mean ranking if not provided) and returns their score variance
+    across ensemble members.
+
+    Parameters
+    ----------
+    score_ensembles : np.ndarray, shape (n_samples, n_ensembles)
+        Raw score matrix from virtual ensembles.
+    k : int
+        Number of top positions to select.
+    reference_ranks : np.ndarray, shape (n_samples,), optional
+        1-based ranks to determine which items are top-k. If None, ranks
+        are derived from the mean of ``score_ensembles``.
+
+    Returns
+    -------
+    topk_mask : np.ndarray, shape (n_samples,), dtype bool
+        Boolean mask indicating items in the top-k.
+    variances : np.ndarray, shape (n_samples,)
+        Score variance across ensembles (0.0 for items outside top-k).
+    """
+    arr = np.asarray(score_ensembles, dtype=float)
+    if arr.ndim != 2:
+        raise ValueError(f"Expected 2D score_ensembles, got {arr.ndim}D.")
+    if k < 1:
+        raise ValueError(f"k must be >= 1, got {k}.")
+    if k > arr.shape[0]:
+        raise ValueError(f"k must be <= the number of items ({arr.shape[0]}), got {k}.")
+    if not np.isfinite(arr).all():
+        raise ValueError("score_ensembles must contain only finite values.")
+
+    if reference_ranks is None:
+        mean_scores = arr.mean(axis=1)
+        reference_ranks = scores_to_ranks(mean_scores)
+    reference_ranks = np.asarray(reference_ranks).reshape(-1)
+    if len(reference_ranks) != arr.shape[0]:
+        raise ValueError("reference_ranks must have one entry per score_ensembles row.")
+    if not np.isfinite(reference_ranks).all():
+        raise ValueError("reference_ranks must contain only finite values.")
+    if (reference_ranks < 1).any() or (reference_ranks > arr.shape[0]).any():
+        raise ValueError(f"reference_ranks must be 1-based values in [1, {arr.shape[0]}].")
+
+    topk_mask = reference_ranks <= k
+    variances = np.zeros(arr.shape[0])
+    if topk_mask.any():
+        ddof = 1 if arr.shape[1] > 1 else 0
+        variances[topk_mask] = arr[topk_mask].var(axis=1, ddof=ddof)
+    return topk_mask, variances
+
+
+def groupwise_topk_analysis(
+    uncertainty_df: pd.DataFrame,
+    score_ensembles: np.ndarray,
+    group_ids: np.ndarray,
+    k: int,
+) -> pd.DataFrame:
+    """Perform groupwise top-k uncertainty analysis across ranking groups.
+
+    For each ranking group, computes:
+    - ``topk_disagreement_prob``: pairwise probability that ensembles disagree about each item's top-k membership
+    - ``topk_score_var``: score variance for items in the top-k (0 otherwise)
+    - ``topk_member``: whether the item is in the consensus top-k (based on mean rank)
+
+    Parameters
+    ----------
+    uncertainty_df : pd.DataFrame
+        Output from ``predict_uncertainty`` containing ``mean_predictions`` and
+        ``knowledge_uncertainty`` columns.
+    score_ensembles : np.ndarray, shape (n_samples, n_ensembles)
+        Raw score matrix from virtual ensembles (obtained from ``get_virtual_prediction``).
+    group_ids : np.ndarray
+        Group IDs aligned with rows.
+    k : int
+        Number of top positions to analyse.
+
+    Returns
+    -------
+    pd.DataFrame
+        Copy of ``uncertainty_df`` with added columns ``topk_disagreement_prob`` (the
+        disagreement probability),
+        ``topk_score_var``, and ``topk_member``.
+    """
+    result = uncertainty_df.copy()
+    result["topk_disagreement_prob"] = 0.0
+    result["topk_score_var"] = 0.0
+    result["topk_member"] = False
+
+    group_arr = np.asarray(group_ids).reshape(-1)
+    score_arr = np.asarray(score_ensembles, dtype=float)
+    if score_arr.ndim != 2:
+        raise ValueError(f"Expected 2D score_ensembles, got {score_arr.ndim}D.")
+    if len(uncertainty_df) != len(group_arr) or score_arr.shape[0] != len(group_arr):
+        raise ValueError("uncertainty_df, score_ensembles, and group_ids must have the same number of rows.")
+    if pd.isna(group_arr).any():
+        raise ValueError("group_ids must not contain missing values.")
+    if k < 1:
+        raise ValueError(f"k must be >= 1, got {k}.")
+    if not np.isfinite(score_arr).all():
+        raise ValueError("score_ensembles must contain only finite values.")
+
+    for g in pd.unique(group_arr):
+        idx = np.flatnonzero(group_arr == g)
+        if k > len(idx):
+            raise ValueError(f"k must be <= the number of items in every group; group {g!r} has {len(idx)} items.")
+        group_scores = score_arr[idx]
+        group_ranks = scores_matrix_to_ranks(group_scores)
+
+        result.iloc[idx, result.columns.get_loc("topk_disagreement_prob")] = topk_rank_disagreement(group_ranks, k)
+
+        # Consensus ordering is the mean of the per-ensemble ranks; negated because
+        # rank 1 is best while scores_to_ranks expects higher-is-better.
+        mean_ranks = group_ranks.mean(axis=1)
+        ref_ranks = scores_to_ranks(-mean_ranks)
+        _, var = topk_score_variance(group_scores, k, reference_ranks=ref_ranks)
+        result.iloc[idx, result.columns.get_loc("topk_score_var")] = var
+        result.iloc[idx, result.columns.get_loc("topk_member")] = ref_ranks <= k
+
+    return result
diff --git a/src/mother/pipeline_utils.py b/src/mother/pipeline_utils.py
index 5ee0904..06b9ebd 100644
--- a/src/mother/pipeline_utils.py
+++ b/src/mother/pipeline_utils.py
@@ -614,6 +614,7 @@ def mother_cv(
     groups: Optional[pd.DataFrame] = None,
     prediction_prefix: str = "pred_",
     return_estimators: Literal[False] = False,
+    **kwargs: Any,
 ) -> pd.DataFrame: ...
 
 
@@ -627,6 +628,7 @@ def mother_cv(
     groups: Optional[pd.DataFrame] = None,
     prediction_prefix: str = "pred_",
     return_estimators: Literal[True],
+    **kwargs: Any,
 ) -> tuple[pd.DataFrame, dict[str, Any]]: ...
 
 
@@ -644,6 +646,7 @@ def mother_cv(
     groups: Optional[pd.DataFrame] = None,
     prediction_prefix: str = "pred_",
     return_estimators: Literal[False] = False,
+    **kwargs: Any,
 ) -> pd.DataFrame: ...
 
 
@@ -661,6 +664,7 @@ def mother_cv(
     groups: Optional[pd.DataFrame] = None,
     prediction_prefix: str = "pred_",
     return_estimators: Literal[True],
+    **kwargs: Any,
 ) -> tuple[pd.DataFrame, dict[str, Any]]: ...
 
 
@@ -677,6 +681,7 @@ def mother_cv(
     default_parameters: Optional[dict] = None,
     prediction_prefix: str = "pred_",
     return_estimators: bool = False,
+    **kwargs: Any,
 ) -> Union[pd.DataFrame, tuple[pd.DataFrame, dict[str, Any]]]:
     """
     Runs nested cross validation if a tuner is provided, otherwise
@@ -713,6 +718,11 @@ def mother_cv(
     return_estimators:
         If True, return a tuple (performance_data, estimators_dict) containing
         fitted/optimized estimators for each fold. If False, return only the DataFrame.
+    **kwargs: Additional parameters to be passed to the estimator's predict_uncertainty method.
+        Parameters that make the estimator return a tuple are not supported,
+        because cross-validation requires a single uncertainty DataFrame per fold.
+        For example, CatBoost ranker ``return_raw=True`` returns auxiliary raw
+        scores alongside the uncertainty DataFrame and is not supported here.
     Returns
     -------
     If return_estimators=False: A dataframe containing the results of cross-validation.
@@ -772,7 +782,13 @@ def mother_cv(
 
         module_logger.debug("The target values are being predicted")
 
-        intermediate_performance_data: pd.DataFrame = val_estimator.predict_uncertainty(X.iloc[test_idx, :])
+        intermediate_performance_data: pd.DataFrame = val_estimator.predict_uncertainty(X.iloc[test_idx, :], **kwargs)
+
+        if isinstance(intermediate_performance_data, tuple):
+            raise TypeError(
+                "mother_cv cannot process tuple-valued predict_uncertainty outputs; "
+                "predict_uncertainty must return a pandas DataFrame or array-like."
+            )
 
         if not isinstance(intermediate_performance_data, pd.DataFrame):
             # model returns non-pd.Data Frame
diff --git a/test/unit/test_catboost_ranker.py b/test/unit/test_catboost_ranker.py
new file mode 100644
index 0000000..f20dddc
--- /dev/null
+++ b/test/unit/test_catboost_ranker.py
@@ -0,0 +1,924 @@
+import pickle
+from unittest.mock import patch
+
+import numpy as np
+import pandas as pd
+import pytest
+import sklearn.base as skl_base
+from sklearn import set_config as skl_set_config
+from sklearn.datasets import make_regression
+from sklearn.model_selection import KFold
+
+import mother.ml.models.m_catboost as m_catboost
+from mother.ml.core import AbstractMotherPipeline
+from mother.ml.models.m_catboost import CatboostRankerMother
+from mother.pipeline_utils import mother_cv
+
+pytestmark = pytest.mark.usefixtures("preserve_metadata_routing")
+
+
+# ---------------------------------------------------------------------------
+# scores_to_ranks unit tests (no model needed)
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    "scores, expected",
+    [
+        # Descending: higher score -> better (lower) rank
+        (np.array([0.5, 0.9, 0.1, 0.7]), np.array([3, 1, 4, 2])),
+        (np.array([10, 5, 20, 15]), np.array([3, 4, 1, 2])),
+        # Identical scores - stable sort preserves input order
+        (np.array([1.0, 1.0, 1.0]), np.array([1, 2, 3])),
+        # Single value
+        (np.array([5.0]), np.array([1])),
+        # Negative values: least negative = highest = rank 1
+        (np.array([-1.0, -5.0, -3.0]), np.array([1, 3, 2])),
+        # Mixed positive/negative
+        (np.array([1.0, -1.0, 0.0, 2.0]), np.array([2, 4, 3, 1])),
+        # Zeros with one positive and one negative
+        (np.array([0.0, 1.0, -1.0, 0.0]), np.array([2, 1, 4, 3])),
+    ],
+)
+def test_scores_to_ranks(scores, expected):
+    """scores_to_ranks uses descending order: highest score -> rank 1."""
+    result = m_catboost.scores_to_ranks(scores)
+    np.testing.assert_array_equal(result, expected)
+
+
+def test_scores_to_ranks_preserves_input_order():
+    """Output positions correspond to input positions (not sorted positions)."""
+    scores = np.array([0.3, 0.7, 0.1, 0.9, 0.5])
+    ranks = m_catboost.scores_to_ranks(scores)
+
+    assert len(ranks) == len(scores)
+    assert set(ranks) == set(range(1, len(scores) + 1))
+
+    # 0.9 -> 1, 0.7 -> 2, 0.5 -> 3, 0.3 -> 4, 0.1 -> 5
+    np.testing.assert_array_equal(ranks, np.array([4, 2, 5, 1, 3]))
+
+
+def test_scores_matrix_to_ranks_matches_columnwise_scores_to_ranks():
+    score_matrix = np.array(
+        [
+            [0.4, 0.1, 0.3],
+            [0.9, 0.7, 0.8],
+            [0.1, 0.3, 0.2],
+            [0.6, 0.9, 0.4],
+        ]
+    )
+
+    expected = np.column_stack(
+        [m_catboost.scores_to_ranks(score_matrix[:, i]) for i in range(score_matrix.shape[1])]
+    ).astype(float)
+    got = m_catboost.scores_matrix_to_ranks(score_matrix)
+    np.testing.assert_array_equal(got, expected)
+
+
+def test_scores_matrix_to_ranks_rejects_non_2d_input():
+    with pytest.raises(ValueError, match="Expected 2D score_matrix"):
+        _ = m_catboost.scores_matrix_to_ranks(np.array([0.1, 0.2, 0.3]))
+
+
+# ---------------------------------------------------------------------------
+# Helpers shared by the model tests
+# ---------------------------------------------------------------------------
+
+
+def _make_ranker_data(n_samples: int = 100, n_features: int = 5, n_groups: int = 10, seed: int = 42):
+    """Return (X, y, group_ids) suitable for fitting CatboostRankerMother."""
+    X, y = make_regression(n_samples=n_samples, n_features=n_features, random_state=seed)
+    X = pd.DataFrame(X, columns=[f"feature_{i}" for i in range(n_features)])
+    y = pd.Series(y, name="target")
+
+    group_ids = np.repeat(np.arange(n_groups), int(np.ceil(n_samples / n_groups)))[:n_samples]
+    # Sort by group so CatBoost receives contiguous groups
+    order = np.argsort(group_ids, kind="stable")
+    X = X.iloc[order].reset_index(drop=True)
+    y = y.iloc[order].reset_index(drop=True)
+    group_ids = group_ids[order]
+    return X, y, group_ids
+
+
+def _fit_ranker(num_trees: int = 20) -> tuple:
+    skl_set_config(enable_metadata_routing=True)
+    X, y, groups = _make_ranker_data()
+    model = CatboostRankerMother(target_type="single_target", num_trees=num_trees).set_fit_request(group_id="group_id")
+    model.fit(X, y, group_id=groups, verbose=False)
+    return model, X, y, groups
+
+
+@pytest.fixture
+def fitted_ranker_data():
+    model, X, y, groups = _fit_ranker()
+    mask = groups == 0
+    return {
+        "model": model,
+        "X": X,
+        "y": y,
+        "groups": groups,
+        "X_group": X[mask],
+    }
+
+
+@pytest.fixture
+def mock_ranker_uncertainty_inputs():
+    mock_X = pd.DataFrame(
+        {
+            "feature_0": [0.1, 0.2, 0.3, 0.4],
+            "feature_1": [1.0, 0.5, -0.2, 0.0],
+        },
+        index=["a", "b", "c", "d"],
+    )
+    mock_helper_output = pd.DataFrame(
+        {
+            "mean_predictions": [0.4, 0.9, 0.1, 0.6],
+            "knowledge_uncertainty": [0.25, 0.5, 0.1, 0.75],
+            "data_uncertainty": [None, None, None, None],
+            "total_uncertainty": [None, None, None, None],
+        },
+        index=mock_X.index,
+    )
+    return {
+        "mock_X": mock_X,
+        "mock_helper_output": mock_helper_output,
+    }
+
+
+# ---------------------------------------------------------------------------
+# CatboostRankerMother.predict tests
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.slow
+def test_predict_returns_scores_by_default(fitted_ranker_data):
+    model = fitted_ranker_data["model"]
+    X_group = fitted_ranker_data["X_group"]
+    scores = model.predict(X_group)
+    assert isinstance(scores, np.ndarray)
+    assert len(scores) == len(X_group)
+    assert np.issubdtype(scores.dtype, np.floating)
+
+
+@pytest.mark.slow
+def test_predict_ranks_returns_1_based_integers(fitted_ranker_data):
+    model = fitted_ranker_data["model"]
+    X_group = fitted_ranker_data["X_group"]
+    ranks = model.predict(X_group, use_ranks=True)
+    assert isinstance(ranks, np.ndarray)
+    assert len(ranks) == len(X_group)
+    n = len(X_group)
+    assert set(ranks) == set(range(1, n + 1))
+
+
+@pytest.mark.slow
+def test_predict_ranks_highest_score_gets_rank_1(fitted_ranker_data):
+    model = fitted_ranker_data["model"]
+    X_group = fitted_ranker_data["X_group"]
+    scores = model.predict(X_group)
+    ranks = model.predict(X_group, use_ranks=True)
+    best_idx = int(np.argmax(scores))
+    assert ranks[best_idx] == 1
+
+
+@pytest.mark.slow
+def test_predict_normalize_by_group_size(fitted_ranker_data):
+    model = fitted_ranker_data["model"]
+    X_group = fitted_ranker_data["X_group"]
+    n = len(X_group)
+    ranks_norm = model.predict(X_group, use_ranks=True, normalize_by_group_size=True)
+    ranks_raw = model.predict(X_group, use_ranks=True)
+    assert (ranks_norm > 0).all()
+    assert (ranks_norm <= 1).all()
+    np.testing.assert_array_almost_equal(ranks_norm, np.round(ranks_raw / n, 4))
+
+
+@pytest.mark.slow
+def test_predict_normalize_no_effect_without_ranks(fitted_ranker_data):
+    model = fitted_ranker_data["model"]
+    X_group = fitted_ranker_data["X_group"]
+    scores_plain = model.predict(X_group)
+    scores_norm = model.predict(X_group, normalize_by_group_size=True)
+    np.testing.assert_array_equal(scores_plain, scores_norm)
+
+
+@pytest.mark.slow
+def test_predict_groupwise_ranks_for_multiple_groups(fitted_ranker_data):
+    model = fitted_ranker_data["model"]
+    X = fitted_ranker_data["X"]
+    groups = fitted_ranker_data["groups"]
+    mask = np.isin(groups, [0, 1])
+    X_multi = X[mask]
+    groups_multi = groups[mask]
+
+    scores = model.predict(X_multi)
+    ranks = m_catboost.ranker_predict_for_groups(model, X_multi, groups_multi, use_ranks=True)
+
+    expected = np.empty(len(scores), dtype=float)
+    for group in np.unique(groups_multi):
+        idx = np.flatnonzero(groups_multi == group)
+        expected[idx] = m_catboost.scores_to_ranks(scores[idx])
+
+    np.testing.assert_array_equal(ranks, expected)
+
+
+# ---------------------------------------------------------------------------
+# CatboostRankerMother.predict_uncertainty tests
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.slow
+def test_output_schema(fitted_ranker_data):
+    model = fitted_ranker_data["model"]
+    X_group = fitted_ranker_data["X_group"]
+    result = model.predict_uncertainty(X_group)
+    assert isinstance(result, pd.DataFrame)
+    for col in ("pred", "mean_predictions", "knowledge_uncertainty", "data_uncertainty", "total_uncertainty"):
+        assert col in result.columns
+    assert len(result) == len(X_group)
+
+
+@pytest.mark.slow
+def test_pred_column_matches_predict_scores_when_use_ranks_false(fitted_ranker_data):
+    model = fitted_ranker_data["model"]
+    X_group = fitted_ranker_data["X_group"]
+    result = model.predict_uncertainty(X_group)
+    expected_pred = model.predict(X_group, use_ranks=False)
+    np.testing.assert_array_equal(result["pred"].values, expected_pred)
+
+
+@pytest.mark.slow
+def test_pred_column_matches_predict_ranks_when_use_ranks_true(fitted_ranker_data):
+    model = fitted_ranker_data["model"]
+    X_group = fitted_ranker_data["X_group"]
+    result = model.predict_uncertainty(X_group, use_ranks=True)
+    expected_pred = model.predict(X_group, use_ranks=True)
+    np.testing.assert_array_equal(result["pred"].values, expected_pred)
+
+
+@pytest.mark.slow
+def test_knowledge_uncertainty_non_negative(fitted_ranker_data):
+    model = fitted_ranker_data["model"]
+    X_group = fitted_ranker_data["X_group"]
+    result = model.predict_uncertainty(X_group)
+    assert (result["knowledge_uncertainty"] >= 0).all()
+
+
+@pytest.mark.slow
+def test_total_uncertainty_is_none(fitted_ranker_data):
+    model = fitted_ranker_data["model"]
+    X_group = fitted_ranker_data["X_group"]
+    result = model.predict_uncertainty(X_group)
+    assert result["total_uncertainty"].isna().all()
+
+
+@pytest.mark.slow
+def test_data_uncertainty_is_none(fitted_ranker_data):
+    model = fitted_ranker_data["model"]
+    X_group = fitted_ranker_data["X_group"]
+    result = model.predict_uncertainty(X_group)
+    assert result["data_uncertainty"].isna().all()
+
+
+@pytest.mark.slow
+def test_uncertainty_for_opt_returns_only_knowledge_uncertainty_on_fitted_model(fitted_ranker_data):
+    model = fitted_ranker_data["model"]
+    X_group = fitted_ranker_data["X_group"]
+    result_opt = model.predict_uncertainty(X_group, uncertainty_for_opt=True)
+    result_full = model.predict_uncertainty(X_group)
+    assert list(result_opt.columns) == ["knowledge_uncertainty"]
+    pd.testing.assert_series_equal(result_opt["knowledge_uncertainty"], result_full["knowledge_uncertainty"])
+
+
+@pytest.mark.slow
+def test_normalize_by_group_size_scales_uncertainty(fitted_ranker_data):
+    model = fitted_ranker_data["model"]
+    X_group = fitted_ranker_data["X_group"]
+    n = len(X_group)
+    result_norm = model.predict_uncertainty(X_group, normalize_by_group_size=True)
+    result_raw = model.predict_uncertainty(X_group)
+    np.testing.assert_array_almost_equal(
+        result_norm["knowledge_uncertainty"].values,
+        np.round(result_raw["knowledge_uncertainty"].values / n, 4),
+    )
+    np.testing.assert_array_almost_equal(
+        result_norm["mean_predictions"].values,
+        np.round(result_raw["mean_predictions"].values / n, 4),
+    )
+
+
+def test_normalize_by_group_size_scales_quantiles(mock_ranker_uncertainty_inputs):
+    model = CatboostRankerMother()
+    mock_X = mock_ranker_uncertainty_inputs["mock_X"]
+    mock_helper_output = mock_ranker_uncertainty_inputs["mock_helper_output"]
+
+    raw_scores = np.array(
+        [
+            [1.0, 2.0, 3.0, 4.0],
+            [2.0, 4.0, 6.0, 8.0],
+            [1.0, 1.0, 1.0, 1.0],
+            [10.0, 20.0, 30.0, 40.0],
+        ],
+        dtype=float,
+    )
+
+    with patch(
+        "mother.ml.models.m_catboost.utils.get_virtual_prediction",
+        return_value=(mock_helper_output, raw_scores),
+    ):
+        with patch.object(model, "predict", return_value=np.array([0.2, 0.9, 0.1, 0.6])):
+            result_raw = model.predict_uncertainty(mock_X, return_quantiles=True)
+            result_norm = model.predict_uncertainty(mock_X, normalize_by_group_size=True, return_quantiles=True)
+
+    n = len(mock_X)
+    for col in ("score_q25", "score_q50", "score_q75"):
+        np.testing.assert_allclose(
+            result_norm[col].to_numpy(),
+            np.round(result_raw[col].to_numpy() / n, 4),
+        )
+
+
+@pytest.mark.slow
+def test_normalize_uncertainty_in_0_1_range(fitted_ranker_data):
+    model = fitted_ranker_data["model"]
+    X_group = fitted_ranker_data["X_group"]
+    result = model.predict_uncertainty(X_group, use_ranks=True, normalize_by_group_size=True)
+    assert (result["knowledge_uncertainty"] >= 0).all()
+    assert (result["knowledge_uncertainty"] <= 1).all()
+
+
+@pytest.mark.slow
+def test_index_preserved(fitted_ranker_data):
+    model = fitted_ranker_data["model"]
+    X_group = fitted_ranker_data["X_group"]
+    result = model.predict_uncertainty(X_group)
+    pd.testing.assert_index_equal(result.index, X_group.index)
+
+
+@pytest.mark.slow
+def test_n_ensembles_one_has_finite_knowledge_uncertainty(fitted_ranker_data):
+    model = fitted_ranker_data["model"]
+    X_group = fitted_ranker_data["X_group"]
+    result = model.predict_uncertainty(X_group, n_ensembles=1)
+    assert np.isfinite(result["knowledge_uncertainty"].to_numpy()).all()
+
+
+def test_virtual_ensemble_helper_is_called_with_forwarded_parameters(mock_ranker_uncertainty_inputs):
+    model = CatboostRankerMother()
+    mock_X = mock_ranker_uncertainty_inputs["mock_X"]
+    mock_helper_output = mock_ranker_uncertainty_inputs["mock_helper_output"]
+
+    dummy_raw = np.zeros((4, 7))
+    with patch(
+        "mother.ml.models.m_catboost.utils.get_virtual_prediction",
+        return_value=(mock_helper_output, dummy_raw),
+    ) as mocked:
+        with patch.object(model, "predict", return_value=np.array([0.1, 0.2, 0.3, 0.4])):
+            _ = model.predict_uncertainty(mock_X, n_ensembles=7, n_threads=3)
+
+    mocked.assert_called_once()
+    _, called_kwargs = mocked.call_args
+    pd.testing.assert_frame_equal(called_kwargs["X"], mock_X)
+    assert called_kwargs["model"] is model
+    assert called_kwargs["virtual_ensembles_count"] == 7
+    assert called_kwargs["thread_count"] == 3
+
+
+def test_virtual_ensemble_mean_scores_remain_scores_by_default(mock_ranker_uncertainty_inputs):
+    model = CatboostRankerMother()
+    mock_X = mock_ranker_uncertainty_inputs["mock_X"]
+    mock_helper_output = mock_ranker_uncertainty_inputs["mock_helper_output"]
+
+    dummy_raw = np.zeros((4, 10))
+    with patch(
+        "mother.ml.models.m_catboost.utils.get_virtual_prediction",
+        return_value=(mock_helper_output, dummy_raw),
+    ):
+        with patch.object(model, "predict", return_value=np.array([0.2, 0.9, 0.1, 0.6])):
+            result = model.predict_uncertainty(mock_X)
+
+    np.testing.assert_allclose(result["mean_predictions"].to_numpy(), np.array([0.4, 0.9, 0.1, 0.6]))
+    np.testing.assert_array_equal(result["pred"].to_numpy(), np.array([0.2, 0.9, 0.1, 0.6]))
+    np.testing.assert_allclose(result["knowledge_uncertainty"].to_numpy(), np.array([0.25, 0.5, 0.1, 0.75]))
+    assert result["total_uncertainty"].isna().all()
+    assert result["data_uncertainty"].isna().all()
+
+
+def test_use_ranks_converts_mean_and_uncertainty_to_rank_scale(mock_ranker_uncertainty_inputs):
+    model = CatboostRankerMother()
+    mock_X = mock_ranker_uncertainty_inputs["mock_X"]
+
+    raw_scores = np.array(
+        [
+            [0.4, 0.1, 0.3],
+            [0.9, 0.7, 0.8],
+            [0.1, 0.3, 0.2],
+            [0.6, 0.9, 0.4],
+        ]
+    )
+
+    mock_helper_output = pd.DataFrame(
+        {
+            "mean_predictions": raw_scores.mean(axis=1),
+            "knowledge_uncertainty": [9.0, 9.0, 9.0, 9.0],
+            "data_uncertainty": [None, None, None, None],
+            "total_uncertainty": [None, None, None, None],
+        },
+        index=mock_X.index,
+    )
+
+    with patch(
+        "mother.ml.models.m_catboost.utils.get_virtual_prediction",
+        return_value=(mock_helper_output, raw_scores),
+    ):
+        with patch.object(model, "predict", return_value=np.array([2, 1, 4, 3])):
+            result = model.predict_uncertainty(mock_X, use_ranks=True)
+
+    rank_ensembles = np.array(
+        [
+            [3, 4, 3],
+            [1, 2, 1],
+            [4, 3, 4],
+            [2, 1, 2],
+        ],
+        dtype=float,
+    )
+    expected_mean_rank = rank_ensembles.mean(axis=1)
+    np.testing.assert_allclose(result["mean_predictions"].to_numpy(), expected_mean_rank)
+
+    expected_rank_uncertainty = rank_ensembles.std(axis=1, ddof=1)
+    np.testing.assert_allclose(result["knowledge_uncertainty"].to_numpy(), expected_rank_uncertainty)
+
+
+@pytest.mark.parametrize("use_ranks", [False, True], ids=["score_mode", "rank_mode"])
+def test_predict_uncertainty_combined_new_parameters_ranker_calls_mother_cv(use_ranks):
+    """Exercise combined ranker uncertainty kwargs through mother_cv forwarding."""
+
+    class DummyRankEstimator(skl_base.BaseEstimator, AbstractMotherPipeline):
+        def __init__(self):
+            self.kwarg_calls = []
+
+        def get_hyperparameter_space(self, X, y, trial, prefix: str = ""):
+            return {}
+
+        def fit(self, X, y):
+            return self
+
+        def predict_uncertainty(
+            self,
+            X,
+            n_ensembles: int = 10,
+            n_threads: int = 1,
+            use_ranks: bool = False,
+            uncertainty_for_opt: bool = False,
+            normalize_by_group_size: bool = False,
+            return_quantiles: bool = False,
+        ):
+            self.kwarg_calls.append(
+                {
+                    "n_ensembles": n_ensembles,
+                    "n_threads": n_threads,
+                    "use_ranks": use_ranks,
+                    "uncertainty_for_opt": uncertainty_for_opt,
+                    "normalize_by_group_size": normalize_by_group_size,
+                    "return_quantiles": return_quantiles,
+                }
+            )
+
+            n = len(X)
+            if uncertainty_for_opt:
+                return pd.DataFrame({"knowledge_uncertainty": np.zeros(n)}, index=X.index)
+
+            pred = np.arange(1, n + 1, dtype=float) if use_ranks else np.linspace(0.1, 0.9, num=n)
+            result = pd.DataFrame(
+                {
+                    "pred": pred,
+                    "mean_predictions": pred,
+                    "knowledge_uncertainty": np.zeros(n, dtype=float),
+                    "data_uncertainty": [None] * n,
+                    "total_uncertainty": [None] * n,
+                },
+                index=X.index,
+            )
+            if return_quantiles:
+                result["score_q25"] = pred
+                result["score_q50"] = pred
+                result["score_q75"] = pred
+            return result
+
+    X = pd.DataFrame({"x": np.arange(12, dtype=float)})
+    y = pd.DataFrame({"target": np.arange(12, dtype=float)})
+    cv = KFold(n_splits=2, shuffle=True, random_state=42)
+    estimator = DummyRankEstimator()
+
+    result = mother_cv(
+        estimator,
+        cv=cv,
+        X=X,
+        y=y,
+        n_ensembles=4,
+        n_threads=2,
+        use_ranks=use_ranks,
+        normalize_by_group_size=True,
+        return_quantiles=True,
+        uncertainty_for_opt=False,
+    )
+
+    assert len(estimator.kwarg_calls) == cv.get_n_splits()
+    for call in estimator.kwarg_calls:
+        assert call["n_ensembles"] == 4
+        assert call["n_threads"] == 2
+        assert call["use_ranks"] is use_ranks
+        assert call["normalize_by_group_size"] is True
+        assert call["return_quantiles"] is True
+        assert call["uncertainty_for_opt"] is False
+
+    expected_prefixed_cols = {
+        "pred_target",
+        "pred_mean_predictions",
+        "pred_knowledge_uncertainty",
+        "pred_score_q25",
+        "pred_score_q50",
+        "pred_score_q75",
+    }
+    assert expected_prefixed_cols.issubset(set(result.columns))
+    # mother_cv drops columns that are all NaN.
+    assert "pred_data_uncertainty" not in result.columns
+    assert "pred_total_uncertainty" not in result.columns
+    if use_ranks:
+        assert np.allclose(result["pred_target"].to_numpy(), result["pred_target"].to_numpy().astype(int))
+    else:
+        assert not np.allclose(result["pred_target"].to_numpy(), result["pred_target"].to_numpy().astype(int))
+
+
+def test_uncertainty_for_opt_returns_only_knowledge_uncertainty(mock_ranker_uncertainty_inputs):
+    model = CatboostRankerMother()
+    mock_X = mock_ranker_uncertainty_inputs["mock_X"]
+    mock_helper_output = mock_ranker_uncertainty_inputs["mock_helper_output"]
+
+    dummy_raw = np.zeros((4, 10))
+    with patch(
+        "mother.ml.models.m_catboost.utils.get_virtual_prediction",
+        return_value=(mock_helper_output, dummy_raw),
+    ):
+        with patch.object(model, "predict", return_value=np.array([0.2, 0.9, 0.1, 0.6])):
+            result = model.predict_uncertainty(mock_X, uncertainty_for_opt=True)
+
+    assert list(result.columns) == ["knowledge_uncertainty"]
+    np.testing.assert_allclose(result["knowledge_uncertainty"].to_numpy(), np.array([0.25, 0.5, 0.1, 0.75]))
+
+
+def test_invalid_n_ensembles_raises(mock_ranker_uncertainty_inputs):
+    model = CatboostRankerMother()
+    with pytest.raises(ValueError):
+        model.predict_uncertainty(mock_ranker_uncertainty_inputs["mock_X"], n_ensembles=0)
+
+
+def test_predict_uncertainty_rejects_unknown_kwargs(mock_ranker_uncertainty_inputs):
+    model = CatboostRankerMother()
+    with pytest.raises(TypeError):
+        model.predict_uncertainty(mock_ranker_uncertainty_inputs["mock_X"], foo="bar")
+
+
+def test_predict_uncertainty_use_ranks_groupwise_via_helper(mock_ranker_uncertainty_inputs):
+    """ranker_predict_uncertainty_for_groups calls predict_uncertainty per group."""
+    model = CatboostRankerMother()
+    mock_X = mock_ranker_uncertainty_inputs["mock_X"]
+    group_ids = np.array([0, 0, 1, 1])
+
+    group0_result = pd.DataFrame(
+        {
+            "pred": [1, 2],
+            "mean_predictions": [1.0, 2.0],
+            "knowledge_uncertainty": [0.1, 0.2],
+            "data_uncertainty": [None, None],
+            "total_uncertainty": [None, None],
+        },
+        index=["a", "b"],
+    )
+    group1_result = pd.DataFrame(
+        {
+            "pred": [2, 1],
+            "mean_predictions": [2.0, 1.0],
+            "knowledge_uncertainty": [0.3, 0.4],
+            "data_uncertainty": [None, None],
+            "total_uncertainty": [None, None],
+        },
+        index=["c", "d"],
+    )
+
+    call_results = [group0_result, group1_result]
+    with patch.object(model, "predict_uncertainty", side_effect=call_results) as mock_pu:
+        result = m_catboost.ranker_predict_uncertainty_for_groups(model, mock_X, group_ids, use_ranks=True)
+
+    assert mock_pu.call_count == 2
+    pd.testing.assert_index_equal(result.index, mock_X.index)
+    np.testing.assert_allclose(result["knowledge_uncertainty"].to_numpy(), [0.1, 0.2, 0.3, 0.4])
+
+
+def test_ranker_predict_uncertainty_for_groups_matches_per_group_manual(mock_ranker_uncertainty_inputs):
+    """ranker_predict_uncertainty_for_groups result equals manual per-group call."""
+    model = CatboostRankerMother()
+    mock_X = mock_ranker_uncertainty_inputs["mock_X"]
+    group_ids = np.array([0, 0, 1, 1])
+
+    mock_out = pd.DataFrame(
+        {
+            "pred": [1.0, 2.0],
+            "mean_predictions": [0.4, 0.9],
+            "knowledge_uncertainty": [0.25, 0.5],
+            "data_uncertainty": [None, None],
+            "total_uncertainty": [None, None],
+        },
+    )
+
+    def _per_group_side_effect(X_group, **kw):
+        out = mock_out.copy()
+        out.index = X_group.index
+        return out
+
+    with patch.object(model, "predict_uncertainty", side_effect=_per_group_side_effect):
+        from_helper = m_catboost.ranker_predict_uncertainty_for_groups(model, mock_X, group_ids)
+
+    expected = pd.concat(
+        [
+            _per_group_side_effect(mock_X.iloc[[0, 1]]),
+            _per_group_side_effect(mock_X.iloc[[2, 3]]),
+        ]
+    ).loc[mock_X.index]
+
+    pd.testing.assert_frame_equal(from_helper, expected)
+
+
+def test_ranker_predict_uncertainty_for_groups_preserves_duplicate_index_order(mock_ranker_uncertainty_inputs):
+    model = CatboostRankerMother()
+    mock_X = mock_ranker_uncertainty_inputs["mock_X"].copy()
+    mock_X.index = ["duplicate", "duplicate", "other", "other"]
+    group_ids = np.array([0, 0, 1, 1])
+
+    def _per_group_side_effect(X_group, **kw):
+        return pd.DataFrame({"value": X_group.iloc[:, 0].to_numpy()})
+
+    with patch.object(model, "predict_uncertainty", side_effect=_per_group_side_effect):
+        result = m_catboost.ranker_predict_uncertainty_for_groups(model, mock_X, group_ids)
+
+    expected = pd.DataFrame({"value": mock_X.iloc[:, 0].to_numpy()}, index=mock_X.index)
+    pd.testing.assert_frame_equal(result, expected)
+
+
+# ---------------------------------------------------------------------------
+# CatboostRankerMother.predict_uncertainty return_raw tests
+# ---------------------------------------------------------------------------
+# Test return_raw parameter
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.slow
+def test_return_raw_integration(fitted_ranker_data):
+    """Test return_raw parameter returns correct output for topk analysis."""
+    model = fitted_ranker_data["model"]
+    X_group = fitted_ranker_data["X_group"]
+
+    # Test default behavior
+    result_default = model.predict_uncertainty(X_group, n_ensembles=10)
+    assert isinstance(result_default, pd.DataFrame)
+
+    # Test return_raw=True
+    uncertainty_df, raw_scores = model.predict_uncertainty(X_group, n_ensembles=10, return_raw=True)
+
+    assert isinstance(uncertainty_df, pd.DataFrame)
+    assert isinstance(raw_scores, np.ndarray)
+    assert raw_scores.shape == (len(X_group), 10)
+
+    # Verify mean_predictions matches raw scores mean
+    np.testing.assert_allclose(
+        uncertainty_df["mean_predictions"].values,
+        raw_scores.mean(axis=1),
+        rtol=1e-5,
+    )
+
+
+# ---------------------------------------------------------------------------
+# CatboostRankerMother get_params / set_params / clone / pickle tests
+# ---------------------------------------------------------------------------
+
+
+def test_get_params_contains_all_custom_keys():
+    model = CatboostRankerMother()
+    params = model.get_params()
+    for key in (
+        "target_type",
+        "model_type",
+        "tune_pairwise_type",
+        "tune_boosting_type",
+        "tune_tree_structure_type",
+        "tune_loss_function",
+        "top",
+        "max_pairs",
+    ):
+        assert key in params, f"'{key}' missing from get_params()"
+
+
+def test_get_params_default_values():
+    model = CatboostRankerMother()
+    params = model.get_params()
+    assert params["model_type"] == "ranking"
+    assert params["target_type"] == "single_target"
+    assert not params["tune_pairwise_type"]
+    assert not params["tune_boosting_type"]
+    assert params["tune_tree_structure_type"]
+    assert params["tune_loss_function"]
+    assert params["top"] == 0
+    assert params["max_pairs"] is None
+
+
+def test_init_normalizes_pairlogit_parameter_separator_for_max_pairs():
+    model = CatboostRankerMother(loss_function="PairLogit;foo=1", max_pairs=125)
+
+    assert model.get_params()["loss_function"] == "PairLogit:foo=1;max_pairs=125"
+
+
+@pytest.mark.parametrize("max_pairs", [0, -1, 2.5, True])
+def test_init_does_not_append_invalid_max_pairs(max_pairs):
+    model = CatboostRankerMother(loss_function="PairLogit", max_pairs=max_pairs)
+
+    assert model.get_params()["loss_function"] == "PairLogit"
+
+
+def test_set_params_updates_attributes():
+    model = CatboostRankerMother()
+    model.set_params(
+        tune_boosting_type=True,
+        tune_loss_function=False,
+        tune_pairwise_type=False,
+        top=10,
+        max_pairs=100,
+    )
+    assert model.tune_boosting_type
+    assert not model.tune_loss_function
+    assert model.top == 10
+    assert model.max_pairs == 100
+
+
+def test_set_params_reflected_in_get_params():
+    model = CatboostRankerMother()
+    model.set_params(tune_loss_function=False, top=5)
+    params = model.get_params()
+    assert not params["tune_loss_function"]
+    assert params["top"] == 5
+
+
+def test_set_params_updates_supported_loss_parameters_only():
+    model = CatboostRankerMother(max_pairs=100)
+
+    model.set_params(loss_function="YetiRank:mode=Classic")
+    assert model.get_params()["loss_function"] == "YetiRank:mode=Classic"
+
+    model.set_params(loss_function="PairLogit")
+    assert model.get_params()["loss_function"] == "PairLogit"
+
+    model.set_params(loss_function="PairLogit:max_pairs=50")
+    assert model.get_params()["loss_function"] == "PairLogit:max_pairs=50"
+
+    model.set_params(max_pairs=200)
+    assert model.get_params()["loss_function"] == "PairLogit:max_pairs=200"
+
+    model.set_params(loss_function="QueryRMSE", max_pairs=300)
+    assert model.get_params()["loss_function"] == "QueryRMSE"
+
+    model.set_params(loss_function="PairLogit:max_pairs=75", max_pairs=None)
+    assert model.get_params()["loss_function"] == "PairLogit:max_pairs=75"
+
+    model.set_params(loss_function="PairLogit;foo=1", max_pairs=125)
+    assert model.get_params()["loss_function"] == "PairLogit:foo=1;max_pairs=125"
+
+    model = CatboostRankerMother(loss_function="PairLogit:max_pairs=75", max_pairs=75)
+    model.set_params(max_pairs=None)
+    assert model.get_params()["loss_function"] == "PairLogit"
+
+
+def test_set_params_top_zero_preserves_ndcg_mode_and_parameters():
+    model = CatboostRankerMother(loss_function="YetiRank:mode=NDCG;dcg_denominator=Position;dcg_type=Base", top=5)
+
+    model.set_params(top=0)
+
+    loss_function = model.get_params()["loss_function"]
+    assert loss_function == "YetiRank:mode=NDCG;dcg_denominator=Position;dcg_type=Base"
+
+
+def test_set_params_top_zero_preserves_classic_mode_without_ndcg_parameters():
+    model = CatboostRankerMother(loss_function="YetiRank:mode=Classic", top=5)
+
+    model.set_params(top=0)
+
+    assert model.get_params()["loss_function"] == "YetiRank:mode=Classic"
+
+
+def test_model_type_must_be_ranking():
+    with pytest.raises(ValueError, match="model_type for CatboostRankerMother must be 'ranking'"):
+        CatboostRankerMother(model_type="regression")
+
+    model = CatboostRankerMother(model_type="ranking")
+    with pytest.raises(ValueError, match="model_type for CatboostRankerMother must be 'ranking'"):
+        model.set_params(model_type="regression")
+
+
+def test_set_params_pairwise_guard_disables_incompatible_combination():
+    """set_params must re-apply pairwise incompatibility guard after updates."""
+    model = CatboostRankerMother(
+        tune_pairwise_type=True,
+        tune_tree_structure_type=False,
+        tune_boosting_type=False,
+    )
+    assert model.tune_pairwise_type
+    model.set_params(tune_tree_structure_type=True)
+    assert not model.tune_pairwise_type
+
+
+def test_set_params_rejects_invalid_pairwise_loss_configuration():
+    model = CatboostRankerMother()
+    with pytest.raises(ValueError, match="requires SymmetricTree grow_policy and Plain boosting_type"):
+        model.set_params(loss_function="YetiRankPairwise", grow_policy="Lossguide")
+
+
+def test_set_params_pairwise_loss_disables_incompatible_tuning_flags():
+    model = CatboostRankerMother(
+        tune_tree_structure_type=True,
+        tune_boosting_type=True,
+    )
+
+    model.set_params(loss_function="YetiRankPairwise")
+
+    assert not model.tune_tree_structure_type
+    assert not model.tune_boosting_type
+
+
+def test_suggested_params_loss_excludes_pairwise_for_fixed_ordered_boosting():
+    class RecordingTrial:
+        number = 0
+
+        def __init__(self):
+            self.choices = {}
+
+        def suggest_categorical(self, name, choices):
+            self.choices[name] = choices
+            return choices[0]
+
+        def suggest_float(self, name, low, high, log=False):
+            return low
+
+    model = CatboostRankerMother(
+        boosting_type="Ordered",
+        tune_pairwise_type=True,
+        tune_tree_structure_type=False,
+        tune_boosting_type=False,
+    )
+    trial = RecordingTrial()
+
+    model.suggested_params_loss(
+        trial,
+        {"grow_policy": "SymmetricTree"},
+        pd.Series([0.0, 1.0]),
+        prefix="",
+    )
+
+    assert trial.choices["base_loss"] == ["YetiRank", "PairLogit", "QuerySoftMax"]
+
+
+def test_sklearn_clone_preserves_params():
+    skl_set_config(enable_metadata_routing=True)
+    model = CatboostRankerMother(
+        tune_boosting_type=True,
+        tune_loss_function=False,
+        top=5,
+        max_pairs=50,
+    )
+    cloned = skl_base.clone(model)
+    assert cloned.tune_boosting_type == model.tune_boosting_type
+    assert cloned.tune_loss_function == model.tune_loss_function
+    assert cloned.top == model.top
+    assert cloned.max_pairs == model.max_pairs
+    assert cloned.model_type == "ranking"
+
+
+@pytest.mark.slow
+def test_pickle_roundtrip_preserves_params_and_predictions():
+    skl_set_config(enable_metadata_routing=True)
+    model, X, _, groups = _fit_ranker()
+    mask = groups == 0
+    X_group = X[mask]
+
+    pred_before = model.predict(X_group)
+
+    serialized = pickle.dumps(model)
+    restored = pickle.loads(serialized)
+
+    assert restored.model_type == model.model_type
+    assert restored.target_type == model.target_type
+    assert restored.tune_boosting_type == model.tune_boosting_type
+    assert restored.tune_loss_function == model.tune_loss_function
+    assert restored.tune_tree_structure_type == model.tune_tree_structure_type
+    assert restored.get_params()["posterior_sampling"] == model.get_params()["posterior_sampling"]
+
+    pred_after = restored.predict(X_group)
+    np.testing.assert_array_almost_equal(pred_before, pred_after)
diff --git a/test/unit/test_catboost_reg_uncertainty.py b/test/unit/test_catboost_reg_uncertainty.py
index 940e8d6..e32a8fe 100644
--- a/test/unit/test_catboost_reg_uncertainty.py
+++ b/test/unit/test_catboost_reg_uncertainty.py
@@ -296,21 +296,28 @@ def test_CatboostGaussianProcessRegressorMother_initialization(self) -> None:
         self.assertEqual(params["samples"], 10)
         self.assertEqual(params["prior_iterations"], 100)
         self.assertEqual(params["model_type"], "regression")
-        self.assertFalse(params["tune_boosting_type"])
-        self.assertTrue(params["tune_tree_structure_type"])
+        self.assertNotIn("tune_boosting_type", params)
+        self.assertNotIn("tune_tree_structure_type", params)
+        self.assertNotIn("tune_loss_function", params)
 
     def test_CatboostGaussianProcessRegressorMother_get_set_params(self) -> None:
         """Test parameter getting and setting."""
         model: CatboostGaussianProcessRegressorMother = CatboostGaussianProcessRegressorMother(
-            samples=15, learning_rate=0.2, prior_iterations=500, tune_boosting_type=True, tune_tree_structure_type=False
+            samples=15,
+            learning_rate=0.2,
+            prior_iterations=500,
         )
         # Test get_params
         params = model.get_params()
         self.assertEqual(params["samples"], 15)
         self.assertAlmostEqual(params["learning_rate"], 0.2)
         self.assertEqual(params["prior_iterations"], 500)
-        self.assertTrue(params["tune_boosting_type"])
-        self.assertFalse(params["tune_tree_structure_type"])
+        self.assertNotIn("tune_boosting_type", params)
+        self.assertNotIn("tune_tree_structure_type", params)
+        self.assertNotIn("tune_loss_function", params)
+        self.assertFalse(model.tune_boosting_type)
+        self.assertFalse(model.tune_tree_structure_type)
+        self.assertFalse(model.tune_loss_function)
 
         # Test set_params
         model.set_params(samples=20, learning_rate=0.3, prior_iterations=600)
@@ -479,9 +486,7 @@ def test_CatboostGaussianProcessRegressorMother_pickling(self) -> None:
 
     def test_CatboostGaussianProcessRegressorMother_cloning(self) -> None:
         """Test model cloning functionality."""
-        model = CatboostGaussianProcessRegressorMother(
-            samples=15, prior_iterations=100, learning_rate=0.2, tune_boosting_type=True
-        )
+        model = CatboostGaussianProcessRegressorMother(samples=15, prior_iterations=100, learning_rate=0.2)
         model.fit(self.X, self.y_regression)
 
         # Test cloning preserves parameters
@@ -502,9 +507,7 @@ def test_CatboostGaussianProcessRegressorMother_cloning(self) -> None:
 
     def test_CatboostGaussianProcessRegressorMother_state_persistence(self) -> None:
         """Test __getstate__ and __setstate__ methods."""
-        model = CatboostGaussianProcessRegressorMother(
-            samples=10, prior_iterations=50, tune_boosting_type=True, tune_tree_structure_type=False
-        )
+        model = CatboostGaussianProcessRegressorMother(samples=10, prior_iterations=50)
         model.fit(self.X, self.y_regression)
 
         # Test state saving and loading
diff --git a/test/unit/test_mother_cv.py b/test/unit/test_mother_cv.py
index eb723c3..b9d86e7 100644
--- a/test/unit/test_mother_cv.py
+++ b/test/unit/test_mother_cv.py
@@ -618,6 +618,24 @@ def test_mother_cv_raises_error_on_invalid_estimator_type(synthetic_data_regress
         )
 
 
+def test_mother_cv_rejects_tuple_uncertainty_output(synthetic_data_regression, cv):
+    class TupleUncertaintyEstimator(BaseEstimator, AbstractMotherPipeline):
+        def fit(self, X, y):
+            return self
+
+        def get_hyperparameter_space(self, X, y, trial, prefix=""):
+            return {}
+
+        def predict_uncertainty(self, X):
+            uncertainty = pd.DataFrame({"mean_predictions": np.zeros(len(X))}, index=X.index)
+            return uncertainty, np.zeros(len(X))
+
+    X, y, _ = synthetic_data_regression
+
+    with pytest.raises(TypeError, match="tuple-valued predict_uncertainty outputs"):
+        mother_cv(TupleUncertaintyEstimator(), cv=cv, X=X, y=y)
+
+
 def test_mother_cv_return_estimators_as_tuple(
     regression_pipeline,
     synthetic_data_regression,
diff --git a/test/unit/test_utils.py b/test/unit/test_utils.py
index 6551ad2..a2a5db8 100644
--- a/test/unit/test_utils.py
+++ b/test/unit/test_utils.py
@@ -27,6 +27,16 @@
 repo_dir: pl.Path = pl.Path(__file__).parent.parent.parent
 
 
+@pytest.mark.parametrize("virtual_ensembles_count", [0, -1, 1.5, True])
+def test_get_virtual_prediction_rejects_invalid_ensemble_count(virtual_ensembles_count):
+    with pytest.raises(ValueError, match="virtual_ensembles_count must be a positive integer"):
+        mother.ml.utils.get_virtual_prediction(
+            pd.DataFrame(),
+            model=None,
+            virtual_ensembles_count=virtual_ensembles_count,
+        )
+
+
 @pytest.mark.parametrize(
     "model_type, target_type, expected_loss",
     [
@@ -174,6 +184,100 @@ def test_convert_input_single_column_dataframe():
     np.testing.assert_array_equal(result, np.array([7, 8, 9]))
 
 
+def test_topk_rank_disagreement_reports_ensemble_disagreement_frequency():
+    rank_ensembles = np.array([[1, 1, 2], [2, 3, 1], [3, 2, 3]])
+
+    result = mother.ml.utils.topk_rank_disagreement(rank_ensembles, k=2)
+
+    np.testing.assert_allclose(result, [0.0, 4 / 9, 4 / 9])
+
+
+def test_topk_rank_disagreement_is_zero_for_unanimous_membership():
+    rank_ensembles = np.array([[1, 1], [2, 2], [3, 3]])
+
+    result = mother.ml.utils.topk_rank_disagreement(rank_ensembles, k=1)
+
+    np.testing.assert_allclose(result, [0.0, 0.0, 0.0])
+
+
+@pytest.mark.parametrize("k", [0, 4])
+def test_topk_rank_disagreement_rejects_invalid_k(k):
+    with pytest.raises(ValueError, match="k must be"):
+        mother.ml.utils.topk_rank_disagreement(np.ones((3, 2)), k=k)
+
+
+def test_topk_rank_disagreement_rejects_out_of_range_ranks():
+    with pytest.raises(ValueError, match="rank_ensembles must be 1-based"):
+        mother.ml.utils.topk_rank_disagreement(np.array([[0, 1], [2, 3], [1, 2]]), k=1)
+
+
+def test_get_virtual_prediction_accepts_numpy_integer_ensemble_count(monkeypatch):
+    class FakeRanker:
+        def virtual_ensembles_predict(self, *args, **kwargs):
+            assert kwargs["virtual_ensembles_count"] == 2
+            return np.zeros((2, 2))
+
+    monkeypatch.setattr(mother.ml.utils, "CatBoostRanker", FakeRanker)
+    result, raw_scores = mother.ml.utils.get_virtual_prediction(
+        pd.DataFrame(index=[0, 1]), model=FakeRanker(), virtual_ensembles_count=np.int64(2)
+    )
+
+    assert result.shape[0] == 2
+    assert raw_scores.shape == (2, 2)
+
+
+@pytest.mark.parametrize("model", [None, object()])
+def test_get_virtual_prediction_rejects_invalid_model(model):
+    with pytest.raises(ValueError, match="model must be an instance of"):
+        mother.ml.utils.get_virtual_prediction(pd.DataFrame(index=[0]), model=model)
+
+
+def test_topk_score_variance_limits_values_to_reference_topk():
+    score_ensembles = np.array([[4.0, 6.0], [3.0, 3.0], [1.0, 5.0]])
+
+    topk_mask, variances = mother.ml.utils.topk_score_variance(
+        score_ensembles, k=2, reference_ranks=np.array([1, 2, 3])
+    )
+
+    np.testing.assert_array_equal(topk_mask, [True, True, False])
+    np.testing.assert_allclose(variances, [2.0, 0.0, 0.0])
+
+
+@pytest.mark.parametrize("reference_ranks", [np.array([0, 1, 2]), np.array([1, 2, 4])])
+def test_topk_score_variance_rejects_out_of_range_reference_ranks(reference_ranks):
+    with pytest.raises(ValueError, match="reference_ranks must be 1-based"):
+        mother.ml.utils.topk_score_variance(np.ones((3, 2)), k=1, reference_ranks=reference_ranks)
+
+
+def test_topk_score_variance_is_zero_for_a_single_ensemble():
+    _, variances = mother.ml.utils.topk_score_variance(np.array([[4.0], [2.0]]), k=1)
+
+    np.testing.assert_array_equal(variances, [0.0, 0.0])
+
+
+def test_groupwise_topk_analysis_respects_group_boundaries():
+    uncertainty_df = pd.DataFrame({"mean_predictions": [1.0, 2.0, 1.0, 2.0], "knowledge_uncertainty": [0.0] * 4})
+    score_ensembles = np.array([[4.0, 5.0], [3.0, 2.0], [1.0, 4.0], [2.0, 3.0]])
+
+    result = mother.ml.utils.groupwise_topk_analysis(
+        uncertainty_df, score_ensembles, group_ids=np.array(["a", "a", "b", "b"]), k=1
+    )
+
+    np.testing.assert_allclose(result["topk_disagreement_prob"], [0.0, 0.0, 0.5, 0.5])
+    np.testing.assert_array_equal(result["topk_member"], [True, False, True, False])
+    np.testing.assert_allclose(result["topk_score_var"], [0.5, 0.0, 4.5, 0.0])
+
+
+def test_groupwise_topk_analysis_rejects_missing_group_ids():
+    uncertainty_df = pd.DataFrame({"mean_predictions": [1.0, 2.0], "knowledge_uncertainty": [0.0, 0.0]})
+    score_ensembles = np.array([[2.0, 3.0], [1.0, 0.0]])
+
+    with pytest.raises(ValueError, match="group_ids must not contain missing values"):
+        mother.ml.utils.groupwise_topk_analysis(
+            uncertainty_df, score_ensembles, np.array(["a", np.nan], dtype=object), k=1
+        )
+
+
 def test_numeric_columns_all_numeric():
     df = pd.DataFrame({"a": [1, 2, 3], "b": [4.0, 5.0, 6.0]})
     result = utils.get_numeric_columns(df)
@@ -990,3 +1094,78 @@ def test_add_prefix_to_dict_keys_prefix_special_chars():
     d = {"x": 10}
     result = add_prefix_to_dict_keys(d, "!@#")
     assert result == {"!@#x": 10}
+
+
+@pytest.fixture
+def fitted_ranker_with_stability_data():
+    """Fixture providing trained ranker with clear vs ambiguous ranking groups."""
+    from mother.ml.models.m_catboost import CatboostRankerMother
+
+    rng = np.random.default_rng(123)
+
+    # Group 0: Clearly separated (one clear winner)
+    X_group0 = rng.standard_normal((5, 8))
+    y_group0 = np.array([100, 50, 40, 30, 20])  # Clear hierarchy
+
+    # Group 1: Near-tie (all similar)
+    X_group1 = rng.standard_normal((5, 8))
+    y_group1 = rng.normal(50, 5, 5)  # High uncertainty
+
+    X = pd.DataFrame(np.vstack([X_group0, X_group1]), columns=[f"feat_{i}" for i in range(8)])
+    y = np.concatenate([y_group0, y_group1])
+    groups = np.array([0] * 5 + [1] * 5)
+
+    # Train ranker
+    model = CatboostRankerMother(
+        iterations=100,
+        max_depth=3,
+        learning_rate=0.05,
+        verbose=False,
+        random_seed=42,
+    )
+    model.fit(X=X, y=y, group_id=groups)
+
+    return {"model": model, "X": X, "groups": groups}
+
+
+@pytest.mark.slow
+class TestRankingUtilsIntegration:
+    """Integration tests combining ranking utility functions with CatBoost rankers."""
+
+    def test_return_raw_parameter_integration(self, fitted_ranker_with_stability_data):
+        """Test that return_raw=True provides scores for topk analysis."""
+        data = fitted_ranker_with_stability_data
+
+        # Get predictions with uncertainty using new API
+        unc_df, score_ensembles = data["model"].predict_uncertainty(data["X"], n_ensembles=10, return_raw=True)
+
+        # Validate return types
+        assert isinstance(unc_df, pd.DataFrame)
+        assert isinstance(score_ensembles, np.ndarray)
+        assert score_ensembles.shape == (len(data["X"]), 10)
+
+        # Analyze top-k stability
+        topk_analysis = mother.ml.utils.groupwise_topk_analysis(unc_df, score_ensembles, data["groups"], k=2)
+
+        # Validation checks
+        assert len(topk_analysis) == len(data["X"])
+        assert {"topk_disagreement_prob", "topk_score_var", "topk_member"}.issubset(topk_analysis.columns)
+
+        # Each group should have exactly k=2 members
+        for g in [0, 1]:
+            n_topk = topk_analysis[data["groups"] == g]["topk_member"].sum()
+            assert n_topk == 2, f"Group {g} should have exactly 2 top-k members"
+
+    def test_stability_differences_across_groups(self, fitted_ranker_with_stability_data):
+        """Test that clear vs ambiguous rankings are distinguished."""
+        data = fitted_ranker_with_stability_data
+
+        unc_df, score_ensembles = data["model"].predict_uncertainty(data["X"], n_ensembles=15, return_raw=True)
+
+        topk_analysis = mother.ml.utils.groupwise_topk_analysis(unc_df, score_ensembles, data["groups"], k=2)
+
+        # Group 0 (clear winner) should have lower disagreement
+        group0_topk = topk_analysis[data["groups"] == 0][topk_analysis["topk_member"]]
+        min_prob_g0 = group0_topk["topk_disagreement_prob"].min()
+
+        assert min_prob_g0 < 0.5, "Clear winner group should have low disagreement"