diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 9081142..d7f46a4 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -22,9 +22,13 @@ jobs:
       - name: Sync environment
         run: uv sync --extra dev
       - name: Format check
-        run: uv run ruff format --preview --check src tests
+        run: >-
+          uv run ruff format --preview --check src tests
+          examples/serosurvey_study.py examples/serosurvey_study.ipynb
       - name: Lint
-        run: uv run ruff check --preview src tests
+        run: >-
+          uv run ruff check --preview src tests
+          examples/serosurvey_study.py examples/serosurvey_study.ipynb
       - name: Type check (strict)
         run: uv run mypy --strict src
 
@@ -54,13 +58,18 @@ jobs:
           enable-cache: true
           cache-dependency-glob: "**/pyproject.toml"
       - name: Sync environment
-        run: uv sync --extra examples
+        run: uv sync --extra examples --extra notebook
       - name: Run CSTR example
         run: uv run python examples/cstr_study.py
       - name: Run sklearn example
         run: uv run python examples/sklearn_study.py
       - name: Run assay cost annotation example
         run: uv run python examples/assay_study.py
+      - name: Execute serosurvey demo notebook
+        run: >-
+          uv run --extra notebook jupyter nbconvert --execute --to notebook
+          --ExecutePreprocessor.timeout=60 --output serosurvey_executed.ipynb
+          --output-dir /tmp examples/serosurvey_study.ipynb
 
   ci:
     name: ci
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 91f60e3..4a69129 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,6 +6,7 @@ All notable changes to this project will be documented in this file.
 
 ### Added
 
+- An executed IVAC-oriented Jupyter notebook compares synthetic serosurvey designs across cost, overall accuracy and underserved-group accuracy, with live budget/preference changes, a 15-minute presenter route and documentation figures. Install notebook tools with the `notebook` extra.
 - Adaptive trial inspection, explicit failure reporting, and bounded retries with persistent failure reasons and retry lineage (#151).
 - Incremental grid checkpoints preserve completed design-point/replicate evaluations across interruptions, including parallel workers and incomplete `Study` phases. `run_grid(max_retries=...)` provides opt-in bounded retries (#151).
 - Grouped surrogate validation holds out whole designs or regimes alongside separate row-validation metrics. Prediction and recommendation expose observed-support diagnostics and warn on extrapolation for GP and RF (#152).
diff --git a/README.md b/README.md
index 71b9069..bf19be6 100644
--- a/README.md
+++ b/README.md
@@ -99,6 +99,12 @@ front = study.front("benchmark")  # non-dominated config indices
 study.compare_phases()  # per-phase hypervolume and IGD+ between successive fronts
 ```
 
+For a complete 15-minute demonstration with live code and saved figures, see
+the [serosurvey design notebook](examples/serosurvey_study.ipynb) and
+[presentation guide](https://jcm-sci.github.io/trade-study/guide/serosurvey/).
+From a repository checkout, launch it with
+`uv run --extra notebook jupyter lab examples/serosurvey_study.ipynb`.
+
 ### Protocols
 
 Users implement two protocols to plug in their domain:
@@ -147,6 +153,7 @@ pip install trade-study[design,pareto]
 | `surrogate` | scikit-learn | GP/RF score and regime surrogates |
 | `dataframe` | pandas | ResultsTable export for analysis and CSV |
 | `all`      | All of the above                                                                                               |                                             |
+| `notebook` | JupyterLab, nbconvert, matplotlib, pandas, pymoo | Execute and present example notebooks |
 
 **Core dependency**: numpy only.
 
diff --git a/docs/assets/serosurvey_costs.png b/docs/assets/serosurvey_costs.png
new file mode 100644
index 0000000..9461133
Binary files /dev/null and b/docs/assets/serosurvey_costs.png differ
diff --git a/docs/assets/serosurvey_population.png b/docs/assets/serosurvey_population.png
new file mode 100644
index 0000000..d03fd3f
Binary files /dev/null and b/docs/assets/serosurvey_population.png differ
diff --git a/docs/assets/serosurvey_priorities.png b/docs/assets/serosurvey_priorities.png
new file mode 100644
index 0000000..a325c75
Binary files /dev/null and b/docs/assets/serosurvey_priorities.png differ
diff --git a/docs/assets/serosurvey_tradeoffs.png b/docs/assets/serosurvey_tradeoffs.png
new file mode 100644
index 0000000..968e4a8
Binary files /dev/null and b/docs/assets/serosurvey_tradeoffs.png differ
diff --git a/docs/guide/serosurvey.md b/docs/guide/serosurvey.md
new file mode 100644
index 0000000..5676287
--- /dev/null
+++ b/docs/guide/serosurvey.md
@@ -0,0 +1,176 @@
+# How much survey is enough—and whose uncertainty matters?
+
+This notebook is a 15-minute demonstration for a mixed audience at the
+Johns Hopkins International Vaccine Access Center (IVAC). It compares
+serosurvey designs using financial cost, overall estimation accuracy and
+accuracy for an underserved group. All populations, prevalences, prices
+and preference weights are hypothetical.
+
+Open the executed
+[Jupyter notebook](https://github.com/jcm-sci/trade-study/blob/main/examples/serosurvey_study.ipynb)
+to read the narrative, code, tables and saved figures. The accompanying
+[Python script](https://github.com/jcm-sci/trade-study/blob/main/examples/serosurvey_study.py)
+contains the simulator and plotting helpers and regenerates these figures.
+Use both files from a checkout; the notebook imports the companion module.
+
+## Run or present the notebook
+
+From the repository root:
+
+```bash
+uv run --extra notebook jupyter lab examples/serosurvey_study.ipynb
+```
+
+Choose the environment's Python kernel, then restart it and run all cells.
+The `notebook` extra supplies JupyterLab, nbconvert, pandas, matplotlib
+and Pareto analysis without requiring the other optional modeling backends.
+Installation needs network access; the executed example uses only local
+code and synthetic data. Run from a checkout of `main`: the notebook uses
+the preference API added after the 0.3.0 release.
+
+Clean notebook executions took about six to eight seconds on the development
+machine, including kernel startup and rendering. The companion script took
+about two seconds for 19,800 evaluations and four figures. These measurements
+exclude dependency installation; check your presentation machine before the talk.
+
+Saved notebook outputs provide a fallback without running code. Export them
+to HTML for an additional presentation copy:
+
+```bash
+uv run --extra notebook jupyter nbconvert --to html examples/serosurvey_study.ipynb
+```
+
+To verify execution in a fresh kernel without modifying the saved notebook:
+
+```bash
+uv run --extra notebook jupyter nbconvert --execute --to notebook \
+  --ExecutePreprocessor.timeout=60 --output serosurvey_executed.ipynb \
+  --output-dir /tmp examples/serosurvey_study.ipynb
+```
+
+## The decision
+
+IVAC's [SISS project](https://publichealth.jhu.edu/ivac/our-work/strengthening-immunization-systems-through-serosurveillance-siss)
+examined the design and use of serological surveillance. The
+[serosurvey costing study by Carcelen, Patenaude, Moss and colleagues](https://pmc.ncbi.nlm.nih.gov/articles/PMC7561102/)
+provides a concrete link between epidemiology and economic evaluation.
+Its study-, cluster- and participant-level cost structure motivates this
+example; we do not reproduce its study or use its historical prices.
+
+The fictional population consists of an 80% group and a 20% underserved
+group, with antibody-status prevalences of 90% and 65% respectively.
+These are assumed model inputs, not measured data or protection thresholds.
+
+![Hypothetical population assumptions](../assets/serosurvey_population.png)
+
+Compare 18 designs:
+
+| Factor | Levels |
+|---|---|
+| Participants | 300, 600, 1,200 |
+| Communities | 10, 20, 40 |
+| Allocation | Proportional 80:20, or oversampling 50:50 |
+
+Allocation applies to both participants and communities. Within each group,
+participants are distributed as evenly as possible, keeping exact totals.
+Independent community probabilities follow a Beta distribution centered on
+the group mean, with illustrative within-community correlation 0.06;
+participant counts then follow a binomial model. The estimand is the fixed
+group mean, not the realized mean in the sampled communities.
+
+The overall prevalence estimator uses population weights of 80:20 for both
+allocation strategies. Oversampling does not change the population composition.
+Financial cost is fixed setup plus community visit costs plus participant costs.
+Average per-community and per-participant costs are not added together as if
+they were independent marginal costs.
+
+## Run, aggregate and refine
+
+The notebook visibly constructs a `Study` with two grid `Phase`s: 100 simulated
+surveys per design, followed by 1,000 per design with an independent phase seed.
+Both phases evaluate all 18 designs. Refinement increases simulation replication,
+not the number of participants per survey. The refined estimates replace the
+screening estimates; the two phases are not pooled.
+
+Each scorer call returns financial cost and absolute prevalence errors in
+percentage points. `aggregate_replicates()` averages those errors, producing
+mean absolute error (MAE), and retains their Monte Carlo variation. Averaging
+signed errors before taking their absolute value would measure something different.
+
+## Inspect feasible alternatives
+
+A `Constraint` imposes an illustrative $40,000 financial budget. The Pareto
+set minimizes cost, overall MAE and underserved-group MAE simultaneously.
+Subgroup error is a narrow measure of information equity, not a comprehensive
+measure of equity in health outcomes.
+
+![Cost, overall accuracy and subgroup accuracy](../assets/serosurvey_tradeoffs.png)
+
+Gray designs exceed the budget. Outlines show the feasible Pareto set computed
+using all three objectives; the two panels are projections, not independently
+computed two-objective fronts. Design labels identify the preference winners.
+Vertical bars are approximately two Monte Carlo standard errors of estimated
+MAE. They express simulation precision under this model, not uncertainty in a
+real survey's prevalence or the model assumptions. They are marginal bars,
+not simultaneous post-selection confidence guarantees.
+
+## Change priorities without rerunning simulations
+
+The notebook passes the raw results to `preference_sweep()` with three explicit
+preference vectors and `normalization="reference"`. Fixed reference ranges are
+$0–70,000, 0–4 percentage points overall MAE, and 0–10 percentage points subgroup
+MAE. These anchors scale preferences; they are not feasibility thresholds and
+do not clip values. Scenario weights are hypothetical, not elicited stakeholder values.
+
+![Ranks under three preference scenarios](../assets/serosurvey_priorities.png)
+
+Rank 1 wins within a scenario. The displayed rows are feasible Pareto designs,
+but ranks include all feasible designs. Choices depend on point estimates and
+can change with further simulation or different assumptions. Any reported
+selection fraction describes the supplied preference scenarios, not a probability
+that a design is best.
+
+For a short live interaction, change the budget to $30,000 in the decision
+cell and rerun that cell and the figures below it. Restore the budget and edit
+the subgroup-priority weights to compare another preference. No simulation
+rerun is needed. If the budget admits no alternative, the example reports no choice.
+
+The optional cost breakdown in the appendix supports an economics discussion:
+
+![Cost components for the selected designs](../assets/serosurvey_costs.png)
+
+## A 15-minute presentation
+
+| Minutes | Content |
+|---|---|
+| 0–2 | The decision and the two population groups |
+| 2–4 | Design factors and competing objectives |
+| 4–6 | One visible `Study` definition and a live run |
+| 6–10 | Budget and Pareto plots |
+| 10–12 | Priorities and an optional budget change |
+| 12–13 | Assumptions a real project would replace |
+| 13–15 | Discussion |
+
+The notebook includes presenter notes and slideshow cell metadata. Leave the
+model details and cost breakdown as appendices for the main talk. The saved
+notebook and HTML export make a Beamer build unnecessary for the current
+fast-running example.
+
+## What a real project would replace
+
+Replace the synthetic population with context-specific prevalence, clustering,
+nonresponse and sampling-frame assumptions; add validated assay characteristics
+and uncertainty; use local financial and economic costs; and define objectives
+and practical constraints with stakeholders. The example holds assay effects
+fixed and does not equate antibody-status prevalence with complete protection.
+Communities are sampled without selection bias by construction. Real selection
+and nonresponse can introduce bias that more simulation cannot remove.
+
+The source notebook links the IVAC projects and member profiles that informed
+its scope. Those connections do not imply endorsement of the example.
+
+To regenerate documentation figures:
+
+```bash
+uv run --extra notebook python examples/serosurvey_study.py
+```
diff --git a/docs/index.md b/docs/index.md
index 69fdffa..d1f5bf2 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -7,6 +7,9 @@ sensitivity analysis, and model stacking.
 For installation and quick-start examples, see the
 [README](https://github.com/jcm-sci/trade-study#readme).
 
+For a short presentation with live code, tables and saved figures, see the
+[serosurvey design notebook](guide/serosurvey.md), designed for a mixed IVAC audience.
+
 ## Overview
 
 `trade-study` provides a structured workflow for multi-objective
diff --git a/examples/serosurvey_study.ipynb b/examples/serosurvey_study.ipynb
new file mode 100644
index 0000000..ac7096a
--- /dev/null
+++ b/examples/serosurvey_study.ipynb
@@ -0,0 +1,1153 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c790ff9d",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# How much survey is enough—and whose uncertainty matters?\n",
+    "\n",
+    "### A 15-minute trade-study demonstration for IVAC\n",
+    "\n",
+    "**Decision:** choose a serosurvey design that balances financial cost,\n",
+    "overall estimation accuracy, and accuracy for an underserved group.\n",
+    "\n",
+    "Every population, prevalence, price and priority below is **hypothetical**.\n",
+    "We estimate antibody-status prevalence; this example does not equate it\n",
+    "with complete protection or prescribe a real survey."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69e09179",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "## Presenter route and setup\n",
+    "\n",
+    "| Minutes | Story |\n",
+    "|---|---|\n",
+    "| 0–2 | One decision, two population groups |\n",
+    "| 2–4 | What can we change, and what counts as success? |\n",
+    "| 4–6 | Run 18 designs with repeated simulated surveys |\n",
+    "| 6–10 | Inspect budget feasibility and Pareto alternatives |\n",
+    "| 10–12 | Change priorities without rerunning the model |\n",
+    "| 12–13 | What would a real project replace? |\n",
+    "| 13–15 | Discussion |\n",
+    "\n",
+    "From a checkout of the repository, start with:\n",
+    "\n",
+    "```bash\n",
+    "uv run --extra notebook jupyter lab examples/serosurvey_study.ipynb\n",
+    "```\n",
+    "\n",
+    "Choose the environment's Python kernel, then **Restart Kernel and Run All Cells**.\n",
+    "The saved outputs also support a presentation without execution. Model and\n",
+    "plotting helpers live beside this notebook in `serosurvey_study.py`; the\n",
+    "trade-study orchestration is visible here. Installation needs network access;\n",
+    "execution after installation uses local code and synthetic data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "45c7efdc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-03T09:22:19.701247Z",
+     "iopub.status.busy": "2026-10-03T09:22:19.700748Z",
+     "iopub.status.idle": "2026-10-03T09:22:20.187824Z",
+     "shell.execute_reply": "2026-10-03T09:22:20.185881Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import sys\n",
+    "from dataclasses import replace\n",
+    "from pathlib import Path\n",
+    "from time import perf_counter\n",
+    "\n",
+    "repo_root = next(\n",
+    "    (\n",
+    "        path\n",
+    "        for path in (Path.cwd(), *Path.cwd().parents)\n",
+    "        if (path / \"examples\" / \"serosurvey_study.py\").is_file()\n",
+    "    ),\n",
+    "    None,\n",
+    ")\n",
+    "if repo_root is None:\n",
+    "    message = \"Open this notebook from a trade-study repository checkout.\"\n",
+    "    raise RuntimeError(message)\n",
+    "sys.path.insert(0, str(repo_root))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2776e8c1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-03T09:22:20.193768Z",
+     "iopub.status.busy": "2026-10-03T09:22:20.193032Z",
+     "iopub.status.idle": "2026-10-03T09:22:20.476987Z",
+     "shell.execute_reply": "2026-10-03T09:22:20.475256Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from IPython.display import display as show\n",
+    "\n",
+    "from examples.serosurvey_study import (\n",
+    "    BUDGET,\n",
+    "    GROUP_NAMES,\n",
+    "    GROUP_WEIGHTS,\n",
+    "    SurveyCosts,\n",
+    "    SurveyScorer,\n",
+    "    SurveyWorld,\n",
+    "    plot_cost_components,\n",
+    "    plot_population,\n",
+    "    plot_priorities,\n",
+    "    plot_tradeoffs,\n",
+    "    survey_factors,\n",
+    "    survey_observables,\n",
+    ")\n",
+    "from trade_study import (\n",
+    "    Constraint,\n",
+    "    Phase,\n",
+    "    PreferencePolicy,\n",
+    "    Study,\n",
+    "    build_grid,\n",
+    "    preference_sweep,\n",
+    ")\n",
+    "\n",
+    "plt.rcParams.update({\"font.size\": 12, \"figure.dpi\": 110})\n",
+    "world = SurveyWorld(seed=2026)\n",
+    "scorer = SurveyScorer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7be85b27",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## 1. A familiar decision\n",
+    "\n",
+    "We need useful evidence about a population, but have a finite survey budget.\n",
+    "An overall estimate can be reasonably accurate while a smaller, underserved\n",
+    "group remains poorly measured.\n",
+    "\n",
+    "This problem connects to IVAC's [SISS work](https://publichealth.jhu.edu/ivac/our-work/strengthening-immunization-systems-through-serosurveillance-siss)\n",
+    "and the [serosurvey costing analysis by Carcelen, Patenaude, Moss and colleagues](https://pmc.ncbi.nlm.nih.gov/articles/PMC7561102/).\n",
+    "The paper motivates separating study, community and participant costs;\n",
+    "we use **invented coefficients**, not its historical prices.\n",
+    "\n",
+    "**Audience question:** Would you spend additional resources on more people,\n",
+    "more communities, or better representation of a smaller group?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f47b951a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-03T09:22:20.483635Z",
+     "iopub.status.busy": "2026-10-03T09:22:20.482846Z",
+     "iopub.status.idle": "2026-10-03T09:22:20.837872Z",
+     "shell.execute_reply": "2026-10-03T09:22:20.836251Z"
+    },
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1210x462 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plot_population(world)\n",
+    "show(fig)\n",
+    "plt.close(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4339b184",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## 2. Three choices, three objectives\n",
+    "\n",
+    "| Choice | Levels |\n",
+    "|---|---|\n",
+    "| Participants | 300, 600, 1,200 |\n",
+    "| Communities | 10, 20, 40 |\n",
+    "| Allocation | Proportional (80:20) or oversampling (50:50) |\n",
+    "\n",
+    "That gives **3 × 3 × 2 = 18 designs**. Allocation applies to both participants\n",
+    "and communities. Within each group, participants are spread as evenly as\n",
+    "possible across its communities, preserving the total exactly.\n",
+    "\n",
+    "We minimize **financial cost**, **overall mean absolute error**, and\n",
+    "**underserved-group mean absolute error**. Errors are in percentage points.\n",
+    "Subgroup error is a specific measure of *information equity*, rather than a\n",
+    "complete measure of equity in health outcomes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "935208cc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-03T09:22:20.843552Z",
+     "iopub.status.busy": "2026-10-03T09:22:20.843296Z",
+     "iopub.status.idle": "2026-10-03T09:22:20.915820Z",
+     "shell.execute_reply": "2026-10-03T09:22:20.914160Z"
+    },
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_64e32\">\n",
+       "  <caption>First 6 of 18 designs</caption>\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th id=\"T_64e32_level0_col0\" class=\"col_heading level0 col0\" >design</th>\n",
+       "      <th id=\"T_64e32_level0_col1\" class=\"col_heading level0 col1\" >participants</th>\n",
+       "      <th id=\"T_64e32_level0_col2\" class=\"col_heading level0 col2\" >communities</th>\n",
+       "      <th id=\"T_64e32_level0_col3\" class=\"col_heading level0 col3\" >allocation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td id=\"T_64e32_row0_col0\" class=\"data row0 col0\" >D01</td>\n",
+       "      <td id=\"T_64e32_row0_col1\" class=\"data row0 col1\" >300</td>\n",
+       "      <td id=\"T_64e32_row0_col2\" class=\"data row0 col2\" >10</td>\n",
+       "      <td id=\"T_64e32_row0_col3\" class=\"data row0 col3\" >proportional</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td id=\"T_64e32_row1_col0\" class=\"data row1 col0\" >D02</td>\n",
+       "      <td id=\"T_64e32_row1_col1\" class=\"data row1 col1\" >300</td>\n",
+       "      <td id=\"T_64e32_row1_col2\" class=\"data row1 col2\" >10</td>\n",
+       "      <td id=\"T_64e32_row1_col3\" class=\"data row1 col3\" >oversample</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td id=\"T_64e32_row2_col0\" class=\"data row2 col0\" >D03</td>\n",
+       "      <td id=\"T_64e32_row2_col1\" class=\"data row2 col1\" >300</td>\n",
+       "      <td id=\"T_64e32_row2_col2\" class=\"data row2 col2\" >20</td>\n",
+       "      <td id=\"T_64e32_row2_col3\" class=\"data row2 col3\" >proportional</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td id=\"T_64e32_row3_col0\" class=\"data row3 col0\" >D04</td>\n",
+       "      <td id=\"T_64e32_row3_col1\" class=\"data row3 col1\" >300</td>\n",
+       "      <td id=\"T_64e32_row3_col2\" class=\"data row3 col2\" >20</td>\n",
+       "      <td id=\"T_64e32_row3_col3\" class=\"data row3 col3\" >oversample</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td id=\"T_64e32_row4_col0\" class=\"data row4 col0\" >D05</td>\n",
+       "      <td id=\"T_64e32_row4_col1\" class=\"data row4 col1\" >300</td>\n",
+       "      <td id=\"T_64e32_row4_col2\" class=\"data row4 col2\" >40</td>\n",
+       "      <td id=\"T_64e32_row4_col3\" class=\"data row4 col3\" >proportional</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td id=\"T_64e32_row5_col0\" class=\"data row5 col0\" >D06</td>\n",
+       "      <td id=\"T_64e32_row5_col1\" class=\"data row5 col1\" >300</td>\n",
+       "      <td id=\"T_64e32_row5_col2\" class=\"data row5 col2\" >40</td>\n",
+       "      <td id=\"T_64e32_row5_col3\" class=\"data row5 col3\" >oversample</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7908471f4050>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "factors = survey_factors()\n",
+    "observables = survey_observables()\n",
+    "grid = build_grid(factors, method=\"full\")\n",
+    "\n",
+    "designs = pd.DataFrame(grid)\n",
+    "designs.insert(0, \"design\", [f\"D{i + 1:02d}\" for i in range(len(grid))])\n",
+    "show(designs.head(6).style.hide(axis=\"index\").set_caption(\"First 6 of 18 designs\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2926809",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## 3. The simulator and scorer do different jobs\n",
+    "\n",
+    "**Simulator:** draw community-level probabilities around the fixed group means,\n",
+    "then draw antibody-status counts within communities. The illustrative\n",
+    "within-community correlation is 0.06. Each design/replicate uses its own\n",
+    "reproducible random stream.\n",
+    "\n",
+    "**Scorer:** compute the absolute difference from known synthetic truth and attach\n",
+    "the financial cost. Average these absolute errors across repeated surveys to\n",
+    "estimate **mean absolute error (MAE)**.\n",
+    "\n",
+    "**Important:** use the population shares, 80:20, when combining group estimates.\n",
+    "Sampling 50:50 does not make the population 50:50."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3314ea20",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-03T09:22:20.920909Z",
+     "iopub.status.busy": "2026-10-03T09:22:20.920642Z",
+     "iopub.status.idle": "2026-10-03T09:22:20.934407Z",
+     "shell.execute_reply": "2026-10-03T09:22:20.932896Z"
+    },
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_6955f\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6955f_level0_col0\" class=\"col_heading level0 col0\" >group</th>\n",
+       "      <th id=\"T_6955f_level0_col1\" class=\"col_heading level0 col1\" >population_share</th>\n",
+       "      <th id=\"T_6955f_level0_col2\" class=\"col_heading level0 col2\" >participants</th>\n",
+       "      <th id=\"T_6955f_level0_col3\" class=\"col_heading level0 col3\" >communities</th>\n",
+       "      <th id=\"T_6955f_level0_col4\" class=\"col_heading level0 col4\" >true_prevalence</th>\n",
+       "      <th id=\"T_6955f_level0_col5\" class=\"col_heading level0 col5\" >one_survey_estimate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td id=\"T_6955f_row0_col0\" class=\"data row0 col0\" >Other communities</td>\n",
+       "      <td id=\"T_6955f_row0_col1\" class=\"data row0 col1\" >80%</td>\n",
+       "      <td id=\"T_6955f_row0_col2\" class=\"data row0 col2\" >150</td>\n",
+       "      <td id=\"T_6955f_row0_col3\" class=\"data row0 col3\" >5</td>\n",
+       "      <td id=\"T_6955f_row0_col4\" class=\"data row0 col4\" >90.0%</td>\n",
+       "      <td id=\"T_6955f_row0_col5\" class=\"data row0 col5\" >80.0%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td id=\"T_6955f_row1_col0\" class=\"data row1 col0\" >Underserved communities</td>\n",
+       "      <td id=\"T_6955f_row1_col1\" class=\"data row1 col1\" >20%</td>\n",
+       "      <td id=\"T_6955f_row1_col2\" class=\"data row1 col2\" >150</td>\n",
+       "      <td id=\"T_6955f_row1_col3\" class=\"data row1 col3\" >5</td>\n",
+       "      <td id=\"T_6955f_row1_col4\" class=\"data row1 col4\" >65.0%</td>\n",
+       "      <td id=\"T_6955f_row1_col5\" class=\"data row1 col5\" >72.0%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x79084950b890>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Financial cost: $18,250\n",
+      "Overall absolute error: 6.6 percentage points\n",
+      "Subgroup absolute error: 7.0 percentage points\n"
+     ]
+    }
+   ],
+   "source": [
+    "example_config = {\"participants\": 300, \"communities\": 10, \"allocation\": \"oversample\"}\n",
+    "truth, outcome = world.generate(example_config, rep=0)\n",
+    "show(\n",
+    "    pd\n",
+    "    .DataFrame({\n",
+    "        \"group\": GROUP_NAMES,\n",
+    "        \"population_share\": GROUP_WEIGHTS,\n",
+    "        \"participants\": outcome.participants,\n",
+    "        \"communities\": outcome.communities,\n",
+    "        \"true_prevalence\": truth,\n",
+    "        \"one_survey_estimate\": outcome.prevalence,\n",
+    "    })\n",
+    "    .style.hide(axis=\"index\")\n",
+    "    .format({\n",
+    "        \"population_share\": \"{:.0%}\",\n",
+    "        \"true_prevalence\": \"{:.1%}\",\n",
+    "        \"one_survey_estimate\": \"{:.1%}\",\n",
+    "    })\n",
+    ")\n",
+    "scores = scorer.score(truth, outcome, example_config)\n",
+    "print(f\"Financial cost: ${scores['cost_usd']:,.0f}\")\n",
+    "print(f\"Overall absolute error: {scores['overall_error_pp']:.1f} percentage points\")\n",
+    "print(\n",
+    "    f\"Subgroup absolute error: {scores['underserved_error_pp']:.1f} percentage points\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7526d6fa",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## 4. Trade-study orchestrates the comparison\n",
+    "\n",
+    "First run 100 simulated surveys per design, then run 1,000 per design with an\n",
+    "**independent phase seed**. Both phases evaluate the same 18 designs. We keep\n",
+    "all of them because this model is cheap; premature elimination is unnecessary.\n",
+    "\n",
+    "Refinement increases the number of *simulated surveys*, not the participant\n",
+    "count in a survey. Its estimates replace the screening estimates; the two\n",
+    "phases are not pooled."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "134b7db6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-03T09:22:20.938174Z",
+     "iopub.status.busy": "2026-10-03T09:22:20.937971Z",
+     "iopub.status.idle": "2026-10-03T09:22:22.497878Z",
+     "shell.execute_reply": "2026-10-03T09:22:22.496474Z"
+    },
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "18 designs; 18,000 refinement evaluations\n",
+      "Both phases completed in 1.55 seconds on this machine.\n"
+     ]
+    }
+   ],
+   "source": [
+    "SCREEN_REPS = 100\n",
+    "REFINE_REPS = 1000\n",
+    "\n",
+    "study = Study(\n",
+    "    world=world,\n",
+    "    scorer=scorer,\n",
+    "    observables=observables,\n",
+    "    factors=factors,\n",
+    "    phases=[\n",
+    "        Phase(\"screen\", grid=grid, n_reps=SCREEN_REPS),\n",
+    "        Phase(\"refine\", grid=grid, n_reps=REFINE_REPS, world=replace(world, seed=2027)),\n",
+    "    ],\n",
+    ")\n",
+    "started = perf_counter()\n",
+    "study.run()\n",
+    "study_seconds = perf_counter() - started\n",
+    "\n",
+    "raw = study.results(\"refine\")\n",
+    "means = raw.aggregate_replicates()\n",
+    "print(f\"{len(grid)} designs; {len(raw.configs):,} refinement evaluations\")\n",
+    "print(f\"Both phases completed in {study_seconds:.2f} seconds on this machine.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28c5798d",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## 5. Separate eligibility from preference\n",
+    "\n",
+    "A financial ceiling is a **hard constraint**. Within that ceiling, some designs\n",
+    "have lower cost, some have lower overall error, and some have lower subgroup error.\n",
+    "\n",
+    "A design is **Pareto dominated** if another eligible design is no worse on\n",
+    "all three objectives and strictly better on at least one. The feasible Pareto\n",
+    "set supplies alternatives; preferences select among them.\n",
+    "\n",
+    "The following three priorities are hypothetical scenarios, not estimates of\n",
+    "stakeholder values. Fixed reference ranges make dollars and percentage points\n",
+    "comparable. They are scaling anchors, not feasibility thresholds, and values\n",
+    "beyond them are not clipped."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d8dc9279",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-03T09:22:22.502822Z",
+     "iopub.status.busy": "2026-10-03T09:22:22.502635Z",
+     "iopub.status.idle": "2026-10-03T09:22:22.530462Z",
+     "shell.execute_reply": "2026-10-03T09:22:22.528998Z"
+    },
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Budget: $40,000 (illustrative USD)\n",
+      "Feasible designs: 11; Pareto alternatives: 9\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_97ed2\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_97ed2_level0_col0\" class=\"col_heading level0 col0\" >cost_usd</th>\n",
+       "      <th id=\"T_97ed2_level0_col1\" class=\"col_heading level0 col1\" >overall_error_pp</th>\n",
+       "      <th id=\"T_97ed2_level0_col2\" class=\"col_heading level0 col2\" >underserved_error_pp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_97ed2_level0_row0\" class=\"row_heading level0 row0\" >Cost first</th>\n",
+       "      <td id=\"T_97ed2_row0_col0\" class=\"data row0 col0\" >0.950</td>\n",
+       "      <td id=\"T_97ed2_row0_col1\" class=\"data row0 col1\" >0.025</td>\n",
+       "      <td id=\"T_97ed2_row0_col2\" class=\"data row0 col2\" >0.025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_97ed2_level0_row1\" class=\"row_heading level0 row1\" >Overall accuracy</th>\n",
+       "      <td id=\"T_97ed2_row1_col0\" class=\"data row1 col0\" >0.100</td>\n",
+       "      <td id=\"T_97ed2_row1_col1\" class=\"data row1 col1\" >0.800</td>\n",
+       "      <td id=\"T_97ed2_row1_col2\" class=\"data row1 col2\" >0.100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_97ed2_level0_row2\" class=\"row_heading level0 row2\" >Subgroup accuracy</th>\n",
+       "      <td id=\"T_97ed2_row2_col0\" class=\"data row2 col0\" >0.100</td>\n",
+       "      <td id=\"T_97ed2_row2_col1\" class=\"data row2 col1\" >0.100</td>\n",
+       "      <td id=\"T_97ed2_row2_col2\" class=\"data row2 col2\" >0.800</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x790849343750>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Live-demo control: change the budget, then rerun this cell and the figures below.\n",
+    "budget = BUDGET\n",
+    "priorities = {\n",
+    "    \"Cost first\": {\n",
+    "        \"cost_usd\": 0.95,\n",
+    "        \"overall_error_pp\": 0.025,\n",
+    "        \"underserved_error_pp\": 0.025,\n",
+    "    },\n",
+    "    \"Overall accuracy\": {\n",
+    "        \"cost_usd\": 0.1,\n",
+    "        \"overall_error_pp\": 0.8,\n",
+    "        \"underserved_error_pp\": 0.1,\n",
+    "    },\n",
+    "    \"Subgroup accuracy\": {\n",
+    "        \"cost_usd\": 0.1,\n",
+    "        \"overall_error_pp\": 0.1,\n",
+    "        \"underserved_error_pp\": 0.8,\n",
+    "    },\n",
+    "}\n",
+    "constraints = [Constraint(\"financial_budget\", \"cost_usd\", \"<=\", budget)]\n",
+    "policy = PreferencePolicy(\n",
+    "    weights=list(priorities.values()),\n",
+    "    normalization=\"reference\",\n",
+    "    reference_bounds={\n",
+    "        \"cost_usd\": (0, 70_000),\n",
+    "        \"overall_error_pp\": (0, 4),\n",
+    "        \"underserved_error_pp\": (0, 10),\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "report = preference_sweep(raw, observables, policy=policy, constraints=constraints)\n",
+    "print(f\"Budget: ${budget:,.0f} (illustrative USD)\")\n",
+    "print(\n",
+    "    f\"Feasible designs: {report.feasible.sum()}; \"\n",
+    "    f\"Pareto alternatives: {report.pareto.sum()}\"\n",
+    ")\n",
+    "show(pd.DataFrame(priorities).T.style.format(\"{:.3f}\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cfadcaba",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-03T09:22:22.533942Z",
+     "iopub.status.busy": "2026-10-03T09:22:22.533698Z",
+     "iopub.status.idle": "2026-10-03T09:22:22.901958Z",
+     "shell.execute_reply": "2026-10-03T09:22:22.900593Z"
+    },
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1320x572 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plot_tradeoffs(report, budget)\n",
+    "show(fig)\n",
+    "plt.close(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "192cd7ba",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "### Read the trade-offs\n",
+    "\n",
+    "- Blue circles use proportional allocation; orange diamonds oversample.\n",
+    "- Gray points exceed the financial ceiling; the dashed line shows that ceiling.\n",
+    "- Black outlines mark the Pareto set computed using **all three objectives**.\n",
+    "  These panels are projections, not separate two-objective fronts.\n",
+    "- Labels identify the preference winners; they can change when inputs change.\n",
+    "- Vertical bars are approximately ±2 **Monte Carlo standard errors of estimated\n",
+    "  MAE**. They describe simulation precision under this model, not uncertainty\n",
+    "  in a real survey's prevalence estimate or in the model assumptions. They are\n",
+    "  marginal bars, not simultaneous confidence guarantees after selection.\n",
+    "\n",
+    "**Audience question:** Which alternative would you defend, and what information\n",
+    "would you need before committing resources?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "547ead3b",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## 6. Same evidence, different priorities\n",
+    "\n",
+    "We can change the budget or preference weights using the already computed\n",
+    "results. This step calls `preference_sweep`, **not the simulator**.\n",
+    "\n",
+    "The full feasible Pareto set is shown below, sorted by cost. Rank 1 is best\n",
+    "within a priority scenario; ranks are calculated among all feasible designs.\n",
+    "Nearby point estimates can change order with additional simulation or different\n",
+    "assumptions, so a rank is not a statistical guarantee of superiority."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b3025dab",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-03T09:22:22.908045Z",
+     "iopub.status.busy": "2026-10-03T09:22:22.907813Z",
+     "iopub.status.idle": "2026-10-03T09:22:23.162750Z",
+     "shell.execute_reply": "2026-10-03T09:22:23.161308Z"
+    },
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x569.8 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_46921\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th id=\"T_46921_level0_col0\" class=\"col_heading level0 col0\" >priority</th>\n",
+       "      <th id=\"T_46921_level0_col1\" class=\"col_heading level0 col1\" >design</th>\n",
+       "      <th id=\"T_46921_level0_col2\" class=\"col_heading level0 col2\" >participants</th>\n",
+       "      <th id=\"T_46921_level0_col3\" class=\"col_heading level0 col3\" >communities</th>\n",
+       "      <th id=\"T_46921_level0_col4\" class=\"col_heading level0 col4\" >allocation</th>\n",
+       "      <th id=\"T_46921_level0_col5\" class=\"col_heading level0 col5\" >cost_usd</th>\n",
+       "      <th id=\"T_46921_level0_col6\" class=\"col_heading level0 col6\" >overall_mae_pp</th>\n",
+       "      <th id=\"T_46921_level0_col7\" class=\"col_heading level0 col7\" >subgroup_mae_pp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td id=\"T_46921_row0_col0\" class=\"data row0 col0\" >Cost first</td>\n",
+       "      <td id=\"T_46921_row0_col1\" class=\"data row0 col1\" >D01</td>\n",
+       "      <td id=\"T_46921_row0_col2\" class=\"data row0 col2\" >300</td>\n",
+       "      <td id=\"T_46921_row0_col3\" class=\"data row0 col3\" >10</td>\n",
+       "      <td id=\"T_46921_row0_col4\" class=\"data row0 col4\" >proportional</td>\n",
+       "      <td id=\"T_46921_row0_col5\" class=\"data row0 col5\" >$16,000</td>\n",
+       "      <td id=\"T_46921_row0_col6\" class=\"data row0 col6\" >2.59</td>\n",
+       "      <td id=\"T_46921_row0_col7\" class=\"data row0 col7\" >8.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td id=\"T_46921_row1_col0\" class=\"data row1 col0\" >Overall accuracy</td>\n",
+       "      <td id=\"T_46921_row1_col1\" class=\"data row1 col1\" >D11</td>\n",
+       "      <td id=\"T_46921_row1_col2\" class=\"data row1 col2\" >600</td>\n",
+       "      <td id=\"T_46921_row1_col3\" class=\"data row1 col3\" >40</td>\n",
+       "      <td id=\"T_46921_row1_col4\" class=\"data row1 col4\" >proportional</td>\n",
+       "      <td id=\"T_46921_row1_col5\" class=\"data row1 col5\" >$35,800</td>\n",
+       "      <td id=\"T_46921_row1_col6\" class=\"data row1 col6\" >1.51</td>\n",
+       "      <td id=\"T_46921_row1_col7\" class=\"data row1 col7\" >4.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td id=\"T_46921_row2_col0\" class=\"data row2 col0\" >Subgroup accuracy</td>\n",
+       "      <td id=\"T_46921_row2_col1\" class=\"data row2 col1\" >D10</td>\n",
+       "      <td id=\"T_46921_row2_col2\" class=\"data row2 col2\" >600</td>\n",
+       "      <td id=\"T_46921_row2_col3\" class=\"data row2 col3\" >20</td>\n",
+       "      <td id=\"T_46921_row2_col4\" class=\"data row2 col4\" >oversample</td>\n",
+       "      <td id=\"T_46921_row2_col5\" class=\"data row2 col5\" >$33,500</td>\n",
+       "      <td id=\"T_46921_row2_col6\" class=\"data row2 col6\" >1.97</td>\n",
+       "      <td id=\"T_46921_row2_col7\" class=\"data row2 col7\" >3.56</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x79084546c050>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plot_priorities(report)\n",
+    "show(fig)\n",
+    "plt.close(fig)\n",
+    "\n",
+    "winner_rows = []\n",
+    "for priority, ranks in zip(priorities, report.ranks, strict=True):\n",
+    "    for i in np.flatnonzero(ranks == 1):\n",
+    "        cfg = report.summary.configs[i]\n",
+    "        cost, overall, subgroup = report.summary.scores[i]\n",
+    "        winner_rows.append({\n",
+    "            \"priority\": priority,\n",
+    "            \"design\": f\"D{i + 1:02d}\",\n",
+    "            **cfg,\n",
+    "            \"cost_usd\": cost,\n",
+    "            \"overall_mae_pp\": overall,\n",
+    "            \"subgroup_mae_pp\": subgroup,\n",
+    "        })\n",
+    "if winner_rows:\n",
+    "    show(\n",
+    "        pd\n",
+    "        .DataFrame(winner_rows)\n",
+    "        .style.hide(axis=\"index\")\n",
+    "        .format({\n",
+    "            \"cost_usd\": \"${:,.0f}\",\n",
+    "            \"overall_mae_pp\": \"{:.2f}\",\n",
+    "            \"subgroup_mae_pp\": \"{:.2f}\",\n",
+    "        })\n",
+    "    )\n",
+    "else:\n",
+    "    print(\"No eligible choice: increase the budget or revisit the available designs.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a44a734",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "### Two safe live changes\n",
+    "\n",
+    "1. Set `budget = 30_000` in the decision cell. Rerun that cell and the figures:\n",
+    "   which previously attractive designs are now excluded?\n",
+    "2. Restore the budget and edit the weights in `priorities[\"Subgroup accuracy\"]`\n",
+    "   in the decision cell. Rerun that cell and the figures below it.\n",
+    "\n",
+    "The results table remains unchanged. These scenarios expose value judgments;\n",
+    "they do not establish that one set of stakeholder priorities is correct."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53f33ff0",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## 7. What would a real project replace?\n",
+    "\n",
+    "- **Population model:** locally relevant prevalence, clustering, nonresponse,\n",
+    "  sampling frames, and uncertainty in these assumptions.\n",
+    "- **Measurement model:** validated assay characteristics and their uncertainty.\n",
+    "- **Cost model:** context-specific financial and economic costs, including\n",
+    "  participant and staff burden where appropriate.\n",
+    "- **Decision rules:** stakeholder-defined objectives, acceptable error, budget,\n",
+    "  and field-capacity constraints.\n",
+    "\n",
+    "Our target is the model's fixed group mean, not the realized mean of sampled\n",
+    "communities. Sampling communities is unbiased by construction; real selection\n",
+    "and nonresponse can introduce bias that more replication cannot remove.\n",
+    "\n",
+    "**Takeaway:** trade-study makes alternatives, evidence and priorities inspectable.\n",
+    "The domain model and the decision assumptions still need substantive expertise."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b23c87a",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Discussion\n",
+    "\n",
+    "What is the closest decision in your current work?\n",
+    "\n",
+    "- Comparing designs for surveillance or program evaluation?\n",
+    "- Comparing delivery strategies under cost and capacity constraints?\n",
+    "- Explaining a recommendation when several objectives matter?\n",
+    "\n",
+    "Which objective or constraint is missing from this demonstration?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6cf8e4ea",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Appendix: what drives financial cost?\n",
+    "\n",
+    "The illustrative coefficients are $3,000 setup, $250/$700 per community visit,\n",
+    "and $30/$40 per participant, for other/underserved communities respectively.\n",
+    "They represent a hypothetical access-cost difference, not observed local prices.\n",
+    "\n",
+    "Fixed, community and participant costs are additive. Average cost per participant\n",
+    "and average cost per community are **not** added together as if they were\n",
+    "independent marginal costs. Participant burden is not monetized in this version."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e2b2552e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-03T09:22:23.169890Z",
+     "iopub.status.busy": "2026-10-03T09:22:23.169299Z",
+     "iopub.status.idle": "2026-10-03T09:22:23.332303Z",
+     "shell.execute_reply": "2026-10-03T09:22:23.330632Z"
+    },
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RhmHY1ElqfXVJKlCggF555RWb8oTnOxPWL01Qu3Zt1ahRQ0ePHrU+/57g22+/lST169cv1ce2evVqSdKwYcNsYixYsKAGDhyY4n7Dhw+Xh4dHom2enp7WZ+QS6jmTve/ZL7/8Iun+Mf47Vun+s4I5c+bUxo0bFRsbm6oYypUrp/j4eG3atCnJ7Q/OPu3s/h1tb+nSpZKk0aNHJzuJlb2zZjsai6PnXUocbdPRY7DnHEhOwvPJH3zwgc0zrvnz59drr72W4v72/i44I2bJ/nPJkeuFs2JNjbQ4Hx1l9nrh7e2tQYMG2ZSvX79e165dU5UqVdS1a1eb7V5eXnr55ZclSWvXrk0ytqTmPfD29ladOnUk2Z5vZnl5eenMmTMaPXq02rVrp1q1aqly5coKDAzU+PHjJUnHjh1Lct/XX3/d5hypX7++9fNNaomxhg0bSpLOnDljLVu/fr2io6NVpkwZ9e7d22afF198UeXLl9e9e/ecugyds9/rcuXKSbr/XP7t27dttnt4eNhcgyRp1apV2rhxoypVqqR58+bZzLcAZBVMJgdkUk2aNJH0fzO4e3l5acOGDQoICFDJkiWt9Ro3bqwpU6bon3/+UdWqVa2JdML+SUn4x/PfEiZ1iYqKstk2cOBAvfrqq5o2bZoaN24s6f4kcitXrpSPj49dy/2EhIRIkqpWrZrk9n8vN5fg9OnTkpRoEpwHJZQntO9M9r5nR48elSS99957Gj58uPUPKQ/+NzY2VjExMbpy5UqqJsH77LPP1KZNG7Vv316VK1dWw4YNVatWLTVr1sw6UVFa9e9oeydOnJAkNWjQ4KHHl1qOxuLoeZcSR9t09BjsOQeSk/B7VLly5SS3J1eewN7fBWfELNl/LjlyvXBWrKmRFuejo8xeL0qXLp3kZGAJ7Z49e9Z6PA+2aRiGbt68Ken+vydJceTfKzN+/vlnvfjiiym2GxkZmWR52bJlbcrc3Nzk7e2tmzdvJrk94Y9OD/aXcG5Uq1YtyUTWYrGoWrVqOnr0qFP/vXP2e92sWTM988wzWrp0qYoUKaKmTZvqiSeeUN26ddWoUaNkE/Djx49LkurUqSM3N1IZZF2c3UAmVbFiRRUqVEgXL17U1q1bVaxYMYWFhenFF19MVK9Ro0aaMmWK1q9fr0qVKmnr1q2SUl4//WH/8CV8kXpQ9+7d9c477+iXX37RlStX5Ofnp++//15xcXHq0aOHcuXKlarjSvjCJynZmdKTayth5tfktieUJ9RzJnvfs4QvNIcPH35o26n98tOoUSMdOXJE3333ndasWaMffvhBU6ZMkXR/NPO7776zLmPj7P4dbS/hs3DmSKQjsZg575Jjpk1H3097zgFnx5zA3t8FszEnsPdccuR64axYHyYtzkczzF4vfH19k6x79+5dSdKNGzd048YNu9uVHPv3ylE3btzQK6+8oqioKLVv3149evRQ8eLFlTt3brm4uGjPnj3q3bt3sn0+LNaUtj/Y5sPO3Qe3xcTEpNinPdLivf7111+1ZMkSLVq0SFu3btVvv/0m6f4fKEaMGKEhQ4bY7JNw/KldQQbIrEjUgUyscePG+umnn7R+/XrraE7CaPaDdaT7t8rVqVNHt2/fVt68eZMdpXFUzpw51bt3b02cOFGzZs3SW2+9pe+//16Sfbe9WywWFShQQBcvXtSpU6eSXPInYQmXf0vY7+TJk/L39092v4IFC6Y6nrRSqFAhhYeHa+nSpSpVqlSKdR977LFUt1usWDF9/PHH+vjjjxUbG6u9e/dqypQpmjlzpjp37qydO3emSf+Otle4cGGdP39eR48eddrn4kgsZs675Jhp08znk9pz4GExh4SEJDlqmxZ3pJiJOYG955Kj1wtnxPowaXE+mpFW16uEkffmzZsnu7xngjx58qS63bSyYcMGRURE6Mknn9Rvv/1mM5q9a9euRxJHwjmZ0jmQsK1QoULWsoREO+EPJP+W3F0LaclisahTp07q1KmTJOny5cv6448/9P777+vNN99UiRIlbJbNe/nll9WmTZtkH3EBsgqeUQcysQefU//38+kJChcurLJly2rjxo3666+/rHWSez7djAEDBki6/1z6ihUrFBoaqtq1a9t9i2bdunUlyZroPyg+Pj7J8gf3e3Bt5wdNmzYtUb2HSbjtLrkvNWYkfHY7d+5UYGBgij+Ojhq4ublZ1y/29fXVrl27dOnSpTTp39H2EvabOHFiqo4pNZ+Jo7E4et6lxNE2nfX5pHQOJCfhedOZM2fabDMMI8lyZ3IkZsn+c8kZ14uUYjV7/UiL89FRaXW9atKkiSwWi3bv3q0CBQqk2G5Sf0xxhJnPJeE2/FKlStkk6Y/idyNBwrmxdetWHTx40Gb7oUOHtHnz5kR1pf+7Rf3QoUM2+/zzzz9O/yOcI+91gQIF1KNHD/3nP/+RJC1fvtymTv78+RUYGJjkWvBAVkKiDmRiDz6nvnbtWhUuXDjJZ8gaN26sa9euWb94pvR8uhkVK1ZUkyZNdOzYMevEQfaMpidI2Ofbb7/V9OnTFRcXJ0m6ffu23njjDR04cCDJ/RL+UDBv3jyNHz/eentcTEyMPvvsMy1evFiSUj0JU8Joz6lTp5w+IdE777wjNzc3jR07VsOHD7dJRC5cuKBvvvlGn376aarau3v3rnr16qWVK1cm+lIUFxen2bNn6+bNm3JxcbF+cXJ2/46299Zbb8nV1VWLFy/WgAEDEu0XGRmpH374QXPnzrWWpeYzcTQWR8+7lDjapiPHYO85kJw+ffpIkr755hvNmjVL8fHxkqQ7d+7orbfe0p49e+x8F5LnrJgl+88le68X9sZq9vqRFuejo5x9vUhQqlQpde3aVTdu3FCLFi20evVqm1u8t2zZotdffz3ZCdrslfC5rF+/3u5btROeIV+2bFmiSdouX76s3r1720ymmlYqVaqkBg0aKD4+Xt27d0/0SMLhw4fVrVs3xcXFqU6dOonmYEhI2keOHGmdo0GS/v77b3Xt2tX6u+4sD3uvv//+e40bN06nTp1KtD0sLEwLFy6UdH/yvn+bNGmSAgMD9e677zo1XiDDSbsJ5QE8CoULF7Yu7fL8888nWSdhqZaEn3379iVZL2EpncaNGye5/fjx48kuw5Zg4cKF1n58fHyM27dv231MhmEYvXv3traTJ08eo1y5ctZ1mFu1apXsMlkJS0tJMnLnzm2UK1fOulaykll6KCUJ6ynnyJHDKFOmjFGpUiWjbdu21u1m3rPvv//ecHNzs8bm5+dnlClTxrrOvZJZricpt27dSvQZFyxY0Chbtqx1DV5JRr9+/Uz3n9LST44ez6RJkwyLxWJIMiwWi1GiRAkjICDAWjZy5MhE9R/2mZiJxdHzLiWOtmnvMdh7DqT0Wb7wwgvWffLmzWuUK1cu0ZreCbE/yJHfBUfO25TYey7Zc71wJNbUnKspceTcSYvl2QzDsd+ph50ThnF/jfaE90m6v6Z2yZIljSJFihguLi7W8r179ybaL2HJsD///DPF927OnDmJyletWpXoGB5//HGjUqVKxpIlS5KNMUF8fLzx5JNPWvcvVKiQ4e/vb33drl07Q7q/pJ49sSYstZfUWu7J/Rty9OhRo0CBAta+ixUrZl2TPeHY/v05nzlzxnp+u7i4GMWKFTPy5MljSDJKlChhVKpUyZCSX57N2e/1e++9l+hzL126tOHv72/9fc2dO7dx6NAhm/4SlmJM7jsPkFWQqAOZ3PPPP2/9h27KlClJ1jlz5oy1Tr58+ZJcl9QwnJOox8TEWP94MHDgQLuPJ0FsbKwxatQo65rNCV+Kxo8fb6xbty7FhGnq1KlGmTJlEn2pLlu2rPHtt9/aHUdwcLDxxBNPJGqrdOnS1u1m37Pt27cbHTp0sCZBCT/+/v7GG2+8YezZsydVccbFxRlz5swxnnrqqURfnKX760R//fXXNmsxO9J/SkmAmeNZu3at0bhxY8PV1TVR4tSnTx/j+PHjieo+7DMxE4uZ8y45Ztq05xjsPQdS+izv3btnDBs2zPolXpJRuHBhIygoyPjtt98cSsqS+l1w9LxNiT3nkmGk/nrhSKypPVeT48i5k1aJumHY/zuVmkTdMO6vl/3ZZ5/ZfA45cuQwGjdubEyZMsVm/XJHk0fDMIxRo0YZPj4+ifpKaj3ypFy6dMl49tlnE51fxYsXN77++mvj77//fmSJumEYRkhIiPH8888n+jw8PT2Nrl27GidPnkyyr61btxpVq1a11ndzczM6duxonDt37qHrqDv7vT5x4oTxwQcfGGXLlrUm55IMLy8vo0ePHkZwcHCS/ZGoI7uwGIYTp8ME8MhdunTJehtiyZIlk50F9tChQ4qPj1fOnDmTnQzozp07OnXqlHLlypVoibcEMTExOnbsmDw8PJJcRka6fxtowgz0e/fuNb2EUGxsrM6ePSt3d3c99thjcnFxUWRkpEJCQpQ7d26VKFEi2X0vX76sa9euKV++fCpQoICpOG7cuKGLFy/q3r17iY7fGe+ZdP8Wz7CwMMXFxalo0aKmZ0G/ePGiIiMjU91WavtPOB4vLy+VLl3adHv/dufOHYWHhytXrlwPXRIuuc/EGbGYOe/Sok1HjuFh50BqPst79+4pNDQ0UcxffPGF3n33Xb399tv64osvrHWd8btg73mbEnvOJcn+64U9sab2XE2OPedOSp/r8ePHFR0drXLlytk8TnDt2jWFhYXJz8/voe9Xas/Hh50TSbl27ZquXLkiX19fFSxYMMnlx6T764rfunUr2X/3zp8/r+vXrysgICDJWefj4+MVGhqq27dvyzAM+fv72zVh3a1btxQWFiZfX1/r+3X37l2dPHnS5ngfFuvhw4cVFxenxx9/3Gb+mNT83kRHR+v8+fOS7k/ml9Ra9/928eJFRUREqEiRIsqdO7ek+xNFRkZGqlSpUolWG3gU73VUVJTOnz8vb29vFShQINnPXfq/7z158uRx2twFQEZEog7AqZYuXaqOHTuqdu3a2r59e3qHA8CJYmNjVb16dQUHB2vRokXq0qVLeocEAECWxGRyAJzm5s2bGjFihCTHJpEDkDH88MMPWrp0qaKjo61l58+fV7du3RQcHKwCBQqoffv26RghAABZG+uoAzBtypQp+vrrr3XmzBnrbXMvvvhieocFwEH//POPJkyYIBcXF/n7+8tisejs2bMyDEOurq6aNm1akrMxAwAA52BEHYBply5d0qFDhxQZGakKFSpo8eLFqXpGDkDG1KdPH3Xu3Fmenp46e/aszpw5I4vForp16+qPP/5Qp06d0jtEAACyNJ5RB2BawsQuefPm1WOPPZbe4QBwooRJpwoXLixvb+/0DgcAgGyBRB0AAAAAgAyEW98BAAAAAMhASNQfEBsbq3Pnzik2Nja9QwEAAAAAZFMk6g+4cOGCAgICdOHChfQOBQAAAACQTZGoAwAAAACQgZCoAwAAAACQgZCoAwAAAACQgZCoAwAAAACQgZCoAwAAAACQgZCoAwAAAACQgZCoAwAAAACQgZCoAwAAAACQgZCoAwAAAACQgZCoAwAAAACQgZCoAwAAAACQgZCoAwAAAACQgZCoAwAAAACQgZCoAwAAAACQgZCoAwAAAACQgZCoAwAAAACQgZCowy6xsbE6c+aMLl++bLqt27dvKzg4WDdv3nRCZAAAAACQNbildwBZUf/+/dM7hESmTZtmuo1du3bpo48+0po1axQXFydJ8vX1VadOnfTaa6/piSeesLvNzZs3q23btpo/f75eeOEF0zECAAAAQFZAoo6H2r17txo0aKDo6Gi5ubkpICBAcXFxCgsL08yZM3X9+nUtWbIkvcMEAAAAgCyBW9/xUBMnTlR0dLR69eql8PBwnT17VufPn9etW7c0a9Ys1a5dO8n9oqOjdfLkSZ07d85m25UrV3TmzBlJ0rlz5xQcHKzg4GCFhoZKkq5du6bg4GBFRUXZ7Hvy5EmdOHHC+vrWrVsKDg7WrVu3JEmRkZE6ceKE7ty5Y/rYAQAAAOBRY0QdDxUeHi5J+uKLL+Tn52ctz507t3r16mVT/9q1axo6dKgWLlxoTbRLliypoKAgdezYUZL03Xff6YMPPpAkvfvuu3r33XclSZ07d9bixYs1b948DRo0SHv37lW1atUStf/8888rKipKwcHBkqS1a9eqU6dOWrx4sbZu3arJkycrJiZGnp6eeu211/T555/L1dXVqe8JAADIesJnWtI7BAAOKvKSkd4hOBUj6niowMBASdL27dsfWjcyMlKNGzfWrFmzFBcXpxIlSqhQoUIKCQnRs88+q9WrV0uSChQooOLFi0uS/P39ValSJVWqVEnFihVzOM7x48crKChIuXPnVuHChRUVFaWgoCC99957DrcJAAAAAI9ahk/UDSPlv4zExsYqKioqyZ+ESc9gzgcffKDixYurQ4cOatiwof7zn/9o3rx5On36tE3dCRMmKDg4WAMGDNDFixcVEhKiCxcuaMOGDfL19dWwYcMkSX369NHUqVMlSf/73/+st74HBQU5HOfu3bv1yy+/6OrVqwoPD9fGjRuVL18+TZw4URcuXHC4XQAAAAB4lDJkon7mzBkNHz5clStXlpeXl7y9vVWjRg1NnTpVsbGxieq+//778vLySvLnm2++SacjyFoKFCigAwcOaPz48XJ3d9e3336rHj16qGTJkmrUqJEOHz5srfvrr78qb968eu2113T+/HlrAp4vXz516NBBe/bsSbPl2Pr166dOnTpZXzds2FAffPCB7t27p3Xr1qVJnwAAAADgbBnyGfX+/ftbb5F2cXFRdHS09u7dq4EDB2rDhg2aP3++zT4eHh42ZW5uGfLwMiVvb2+99dZbeuuttyRJp06d0q+//qoRI0aoTZs2OnDggHx8fBQSEqLr16+rSpUqybZ1+fJl+fr6Oj3GpCa1q1OnjiTp/PnzTu8PAAAAANJChsxky5Ytq7Zt26p169YqU6aM7t27p7Vr1+rll1/WggULNG7cOJtnmc+dO5doojOkrVKlSuntt9/WrVu3NHr0aK1evVpdu3aVm5ubcubMqZIlSya7b2omdkuo8+87KCTp5s2bSf5hJjIy0qbs9u3bkiR3d/eH9gkAAAAAGUGGTNQnTZqU6LWbm5ueeuopPf3005oxY0aSCZkkxcfHy8UlQ97Nn6nduXNHOXPmTHLbjRs3JElhYWGSpCpVqmjHjh3avHmz8uTJY1M/KipKnp6ekv4vGY+JibGpV7RoUUnSwYMHVatWLWv5wYMHderUKVWsWNFmn0WLFmnAgAGJyhYvXixJSdYHAAAAgIwoQybqCWJiYhQfH6+bN29qw4YNWrJkiQIDA1WuXDmbug0bNtSxY8fk5eWlunXr6sMPP1TTpk3TIeqsp2vXroqOjlbHjh1VpkwZFSxYUFevXtXvv/+uyZMnS5Jq1KghSXrttdf0559/qm7duhoyZIgCAwOVI0cOnTp1Sn/++adOnTqlv/76S5JUsGBBSdK8efNUrlw55c6dW76+vgoICFCVKlXk4uKiYcOGycPDQ6VLl9aBAwc0atSoZONcv369XnjhBfXt21fu7u766aefNGPGDBUpUoRzAQAAAECmYSpRP3funNavX6+DBw/q0qVLslgsKlCggAIDA9WkSRM99thjpoKrUqWKjh49Kun+6Gv37t2TXRP7yJEjslgsioyM1Jo1a7R27VrNnDkzyXW+E0RERCgiIsL6OmG9cCTm6empFStWaO3atUlu79Onjxo2bChJ6tixoz744AN9+umnGjhwoE3dBxPmwMBABQQEaPXq1dY5CRLWUS9ZsqS6deumuXPnqlu3btZ9OnTooAIFCig6Otqm7UGDBunrr7/WwoULrWVubm6aPn26cuTI4djBAwAAAMAjZneifu/ePS1YsEDffPON/v777xTr1qtXTwMGDNALL7zg0DPCHh4eypEjh2JiYhQXF6etW7fqn3/+UeHCha11/P39NX36dLVu3VpFixbV2bNnNXnyZAUFBWnw4MHq1KmTvL29k2w/KChIo0ePtjuu7GbRokXauHGjli5dqqNHjyo8PFy5cuVSxYoV1aVLF7Vu3TpR/U8++URt27bVzJkzdfDgQVksFpUsWVJt2rTR888/b63n6uqqVatWady4cTp48KCioqISzT0wffp0lS5dWn/88YdcXV3Vtm1b/ec//1HPnj2TTNSbNGmizp07KygoSGfPnlXp0qX11ltvqW7dumn35gAAAACAk1mMhy1U/oClS5fq3Xff1fHjx1WjRg21bdtWdevWVenSpZU/f34ZhqFr167pxIkT+vvvv7VixQrt27dP5cqV0+eff65nnnnGoSBv376tdevW6bXXXtOVK1d08OBBlS5dOsV9XnzxRc2dO1crVqxQ27Ztk6yT1Ih67dq1FRoaKn9/f4dixaO3ZMkSderUSb/++qs6duyY3uEAAIBMKnymJb1DAOCgIi+lOq3NFOwaUe/SpYt69+6tX3/9VZUqVUqyTsGCBVWhQgU99dRTGjt2rIKDg/Xll1+qS5cuunfvnkNB5s6dWx06dNCdO3fUrVs3LVu2zLpMWHJq166tuXPn6vLly8nW8fHxkY+Pj0MxAQAAAACQFuxK1I8cOfLQkex/CwwM1Pfff68PP/zQrv2SkrDUVnKzvj9o48aNkpToNnkAAAAAADI6u9YyszdJd2Tf8+fPq1mzZvrll1904sQJRURE6NSpU5o6dareeecdSbJOXHbx4kU1atRIixYt0vHjx3Xjxg3t3btXffr00c8//6x8+fKpfv36DseMzMHHx0eVKlXi7ggAAAAAWYKpWd8Nw9DRo0fl4uJiXTItIiJC48aN0+nTp/Xcc8859Fz6X3/9ZV3C69969+6txo0bW19v2rRJmzZtsqnn5uamadOmKVeuXHb3j8ylWbNmCg4OTu8wAAAAAMAp7BpR/7dFixapYsWKida27t69uz755BPNmzdPHTt21B9//GFXm4899pi2bt2ql156SRUqVJC3t7eKFi2qtm3bav78+Zo5c6a1bqFChbR161b16tVL5cuXl7e3t4oXL67u3btr+/bt6tKli5nDAwAAAADgkbNr1vd/a9iwoTZv3qytW7eqbt26CgkJUbVq1RQcHKzFixdr6NChatOmjVauXOnMmNPMuXPnFBAQwKzvAAAA2RCzvgOZV1ab9d3UiPqhQ4ck3Z8wTpLWrl2rVq1aKSAgQD179pQk7du3z1yEAAAAAABkI6YS9djY2ESvd+/ererVq0uS9dnwmzdvmukCAAAAAIBsxVSiXqZMGUnSn3/+qZiYGK1YsUKNGjWSJJ06dUqSVKpUKZMhAgAAAACQfZhK1AcOHCjp/gRypUuXlre3t+rWrSvp/m3wktSqVSuTIQIAAAAAkH2YWp6tb9++kqSlS5fKz89PI0eOlKurq6T7iXr58uWtyTwAAAAAAHg4U7O+ZzXM+g4AAJB9Mes7kHkx6zsAhxw7dkwDBgzQjh070juUJDkaX0Y/LgAAACCzsStRd3Nzc+gHWcfNmzf1008/6aOPPtKQIUP06aefav78+bpx40Z6h5bhhYWFadq0aTp27Ji17NChQxowYIB27dqVjpHdl1R8ju6XkY4LAAAAyGzsyqJfeOEFm7Lw8HCtW7dO+fLl0xNPPCGLxaIdO3bo2rVrat68uQoXLuy0YDMLy4x30juERIyXv3BKO5MmTdLw4cOTXHLP09NTH3/8sd59912n9JUVlStXTt98843q1KljLTt79qymTZumJk2aqFatWukYXdLxObpfRjouAAAAILOxK1H/8ccfE73ev3+/mjZtqubNm+vnn3+Wr6+vpPujrp07d9bevXu1fv16pwWL9DN69GiNGjVKktSoUSM1adJEfn5+unjxos6cOaMVK1Zo//796RtkBle0aFENGDAgvcNIlqPxZfTjQvrjmU8g88pqz3wCQGZh6r70N998U9evX9dXX31lTdIlydfXV19++aWqVKmiN99807pUGzKnAwcOaPTo0fL09NTPP/+sdu3a2dSJjo7W0aNHbcoPHTqkZcuWKSwsTH5+fmrXrp3NCOv+/fv1zTffaODAgfL399ecOXN09uxZValSRS+++KLc3NxkGIaWLVumzZs3K2fOnOrZs6fKlCmTJu2sX79eCxYs0EcffWQzqeCnn36q2NhYDR8+3KbPEiVK6Mcff9Tx48cVEBCg3r17y8/Pz7rvsWPHFBQUpFdeeUW1a9fW6tWrNWnSJEnS999/b/2jVo0aNVS3bl19/fXX6t69uxo1amTzvn733Xfau3evgoKC5OHhkeTnlhBbjx491LBhQ5vt33//vXbv3q2goCB5enraxJfg4sWLWrRokU6fPi1vb29Vq1ZNTz31lHWFB3uOq1+/fqlqEwAAAMjOTCXqCZNHlSxZ0mZbqVKlJEnbt2830wUygGnTpskwDI0cOTLJJF2SPDw8VKVKlURlY8aM0ciRIxUfH28tGzlypF5//XVNnjzZWhYSEqJp06apQoUK+uKLL3T+/HnrtkWLFmnZsmV6/vnn9fPPP1vLg4KCtG3bNlWqVMnp7QQHB2vatGkaMGCATaL+888/KyoqypqoJ/RZuXJlPfvsszp16pS17pdffqkdO3aoaNGikv7vWe4GDRqodu3a2rt3r1asWCFJWrNmjdasWSNJ6ty5s1588UUtXLhQISEhNon63bt39c4776h+/frJJunS/d/L2bNnKywszCZRj4mJ0XvvvafAwEB5enomGZ8krVu3Tu3atVN0dHSi/cuXL69Dhw7JxcXFruPq169fqtoEAAAAsjNTiXquXLl0584d7dmzxyYR2L17t7UOMrfNmzdLknr16pXqff78808NHz5cLi4u6tmzp6pWrarjx49rxowZ+vrrr1WjRg298sorifYZOXKkSpQoof79+8tisWjatGlasWKFnn32Wa1bt06DBw9WsWLFtGbNGq1atUpjxozR/Pnzbfp2Vjv2GDZsmIoUKaIRI0YoZ86c+umnn7Rnzx599tlnmjhxYpL7tGnTRmFhYZo0aZL69OljvdOgdOnSypkzp3r16qVJkybp9OnTKlGihHW/n376STdv3tTAgQNTjMnHx0cdO3bU4sWLdfnyZRUoUMC6bfny5bp69epDP9MRI0YoOjpazz33nGrXrq3o6Gjt2bNHS5YsUXx8fJJJdUrH5WibAAAAQHZiKlF/7rnn9PXXX6tfv35auHChdUT1n3/+Uf/+/a11kLlduHBBnp6e1pHh1Pj6668lSXPmzFH37t2t5c8995yaN2+uyZMn2yTqjz/+uDZt2mRdKaBt27aqVauWVqxYoZ07d6patWqSpKFDh6pq1ar666+/kuzbWe3Yo2zZstqyZYty5MghSXrjjTdUsmTJFOdoqFatmtq1a6dJkyapRYsWNpM1DhgwQBMnTtT333+v//73v9by6dOnq3jx4sne3fCg3r17a/78+Zo/f74GDx5sLZ89e7Zy5syprl27prj/lStX1KJFCy1cuDBR+bFjx5K9Tf1hx+VImwAAAEB2Ymro6tNPP1Xjxo115MgRVa1aVQULFlTBggVVtWpVHTlyRI0bN9Ynn3zirFiRTlxdXRUXF2fXPnv27NFjjz2WKEmXpGbNmumJJ57Q/v37FRsbm2jbyy+/nGg5vxo1asjd3V1PPPGENbmWJIvFotq1a+vSpUuKiYmx6dtZ7dijT58+1iRdun8nSfXq1XXp0iWH26xYsaKaNGmiGTNmWN//I0eOaMuWLXr11VdTNfLcsmVLFS1aVLNnz7aWXbt2TStWrFDHjh3l7e2d4v49evTQtm3bNHHiRB07dsz6GEO5cuVksTg2QVhatAkAAABkJaYSdW9vb61bt07z589Xt27dVLJkSZUqVUrdunXT/PnztW7duocmAsj4AgICdO/evSQni0vOrVu39NhjjyW5zd/fX/Hx8YqMjExUXrBgwUSvLRaLcuTIYVMu3V8OzjCMJBNsZ7WTFMNIevbbpJYh9PLysvljhL0GDhyo8+fPa+XKlZLuj6a7u7urT58+qdrfxcVFL774onbv3q1Dhw5JkubPn6+YmBj17t37ofsPHz5cM2fO1F9//aVGjRopb9686ty5s3V+CkekRZsAAABAVmL6YVAXFxe98MILmjdvnrZv365t27Zp3rx5euGFF3jWNIto3ry5JCWaAO5h8ufPr5MnTyaZqB49elQ5cuTIsH/EyZ07tyQpIiIiUblhGDp9+rRT+3rYCHKnTp1UqFAhTZ8+XTExMZo9e7Y6deqU5B8GkpOQkCeMqs+ePVtFixZVixYtUrV/586d9euvv+rChQsKDg7WY489pvr162vXrl3J7vOw43KkTQAAACC7cFomfe/ePd28eVM3btyw+UHmNnDgQHl5eWny5Mn69NNPkxx9Pn78uH7//Xfr6/r16+vq1asaM2ZMonpTp07VoUOHVK9evQz7h5xixYpJUqLZ4SXp888/17Vr15zaV8KM68ndIp8wer5ixQpNmTJFV65csXvN8scff1w1a9bU3LlzdfjwYe3YsUM9evRI1fs/c+ZM3b171/o6ICBAXbt2VWxsrJYvX+7QcTnaJgAAAJBdmJpMzjAMTZgwQdOmTdPRo0eTvS04uXJkDv7+/po+fbp69eqlDz/8UF9++aXq16+vfPny6dKlSzp9+rSCg4PVo0cPPfXUU5LuT9Q2f/58jR49WkuXLlXlypV14sQJ/f3335Kk999/Pz0PKUV169ZVoUKFNHHiRO3evVulS5fWgQMHdOTIERUqVMipfZUtW1aSNGrUKO3YsUO5c+dOtN64JPXr10+fffaZ/vOf/6hChQpq2rSp3f307t1bgwcP1quvviop9TP49+3bV0OHDlX16tVVrFgxXb9+XWvXrpWkRDPR23NcjrYJAAAAZBemEvUxY8ZoxIgRcnNzU6dOnVSiRAkmg8qievTooUKFCmno0KE6cOCAlixZkmj7448/rg4dOlhfV61aVQsXLlTfvn21b98+7du3T9L9SdaCgoLUunXrRxi9fby8vDRhwgT16tVLW7Zs0ZYtW+Tp6ak5c+bos88+U1RUlNP6Klq0qHr37q1Zs2Zp7ty5kv5vvfEECTO8//7773aPpifo1q2b3n77bW3ZskU1atRQYGBgqvZ75ZVXNHfuXK1bt85a5u7urgEDBqhnz54OHZejbQIAAADZhcUwMdzt7++v8+fPa+HChVliGbZz584pICBAoaGh8vf3T+9wMqwjR47owIEDunXrlgoXLqxSpUqpQoUKSdaNjIzUhg0bdOHCBeXPn986ediDTp8+rVWrVqlNmzY2I6rff/+9/P39bRL7zZs3Kzg4WH379rXO8O6sdhKcO3dOGzZskKurq5o2bapChQrpl19+UWxsrPV8T6nP1atX68KFC9ZnxMPCwrRs2TI1b97cOuKcYOfOnTp48KCioqJUunRptWzZMtH2N954Qz/88IPCwsKUJ08e2zc6FX755RddunRJ1apV05NPPmmzPbn4bt68qd27d+vs2bPy8/NTrVq1Ej0j78hxPaxNZC3hM/kDLpBZFXkpe90VyfUKyLyy2vXKVKLu6emp6Oho3b59W7ly5XJmXOmCRB0Z0blz51ShQgV16NBB8+bNS+9wALvxxRfIvLLaF9+H4XoFZF5Z7XplajavqlWrSpKuXr3qlGAA/J8lS5aob9++ql+/viIjIxPdDg8AAAAg6zKVqH/++efKkSOHJk6c6Kx4APx/27Zt0/fff6+zZ8/q+eefV5MmTdI7JAAAAACPgKnJ5Hbu3KmOHTtq/PjxCg4OVoMGDazLMj3onXfeMdMNkC116tRJpUqVUqlSpaxr2QMAAADI+kw9o57aGd4zy/JsPKMOAM7HM59A5pXVnvl8GK5XQOaV1a5XpkbUDxw44Kw4AAAAAACATCbqqV2LGQAAAAAApI6pyeQAAAAAAIBzmRpRT7B7926tX79eYWFhio6Ottk+efJkZ3QDAAAAAECWZypRj4qK0vPPP69ly5alWI9EHQAAAACA1DF16/vo0aO1bNkyNWrUSCEhIdby06dPq06dOurWrZsiIiJMBwkAAAAAQHZhKlFfsGCBJGn8+PEqUaKEtbx48eIaN26c5s+fr3HjxpkKEAAAAACA7MRUon7+/HlJUqVKle435nK/udjYWNWoUUOSNGfOHDNdADaioqIUGxv7yPcFAAAAgEfBVKJetGhRSdKNGzckSfny5ZMkXb9+Xa6urpKky5cvm+kCGYBhGIqKirL+JDVhoLMll1BfuXJFXl5e+uijj+xu08y+aY0/IAAAAABIYGoyuXr16unMmTPas2eP2rdvr1q1amnVqlVat26dcuXKJUkqWbKkUwLNTMJnWtI7hESKvGSY2n///v2qXr16ojI3NzdVqFBBffv21aBBg6x3UzjDuXPnFBAQoGHDhmnMmDGJtlksFnl4eMjd3d3uds3sm5ZSOl4AAAAA2Y+pRP2NN97Q/Pnz9cUXX6h9+/YaOnSoVq9erR49elgTt6FDhzolUKQ/V1dXubndP2Wio6MVHBysN998UyEhIfrqq68eSQz58+dXVFTUI98XAAAAAB4VU8Og9erV071797RmzRpJUsuWLbVixQp17txZzz77rH766Sf16dPHKYEi/fXt29d6+3tERISmTJkiV1dXTZ48WVevXrWpHx8fn2xbCbfTx8XFJaofExNjvbU+Li7O2l9MTIy1XmpuEzeMpO8i+Pe+ycWRGo4cX1JtPOx4/90uAAAAgKzN9P3Kbm5u1ufRJalNmzZauHChFixYoK5du5ptHhmUt7e3Bg4cqGeeeUZxcXE6ePCgJGnTpk167rnn9Nhjj8nNzU1FihTRkCFDdPv27UT7nzx5Ul5eXvr000/1xx9/qGbNmvLw8FD79u1VpkwZSdJnn30mLy8veXl5qUqVKpJSfs78hx9+0BNPPKEcOXIoV65cql+/vn7//Xfr9qT2fTCO33//XYGBgXJ3d1ehQoU0bNgwmz8IOHJ869evV40aNeTu7q78+fPr/ffftybtK1asSPF4Jenbb79VlSpV5OXlpdy5c6tWrVqaMWNGoj8CAAAAAMg6TN36bhiGjh49KhcXF5UrV06SFBERoXHjxun06dN67rnn9MwzzzglUGRM9+7dk3T/+W9Jatq0qTWBtFgsunDhgiZOnKhDhw7pzz//tNl/x44dGjVqlOLi4pQjRw65ubnJw8ND0dHRiW619/DwSDGOAQMGaNq0adbXsbGx2rp1q55++ulUjUJv377dGofFYtGlS5f0ySef6MKFC/r++++t9ew9vt27d+u///2vYmJiZLFYdO3aNY0bN04+Pj768MMP5erqmuLxzpw5U/3797e25+Liot27d+uVV15R9erVVa1atYceGwAAAIDMxdSI+qJFi1SxYkWNGjXKWta9e3d98sknmjdvnjp27Kg//vjDbIzIIB68NfvcuXP63//+p99//13u7u6qXLmypPuPP6xatUpXrlzRvXv3FBwcrDZt2mjNmjXauHGjTZu//fab+vXrp3Pnzik6OlorV67UiRMnJEnvv/++tb/9+/cnG9fatWs1bdo0FShQQLNnz9aVK1d0584dbd26VU899VSqju3333/Xq6++qrCwMN25c0fLly9X4cKF9cMPP2jv3r3WevYe35IlS/TGG28oLCxMMTExWrFihTw9PfXtt99Kktq2bZvi8Sa8v0uWLFFUVJTu3Lmjffv2qW/fvtakHgAAAEDWYipRnzRpkiRp0KBBkqSQkBBt2rRJZ8+eVVBQkCTpyy+/NBkiMorvvvvOemt2QECA/vOf/8gwDL377rvKkyePJGnlypVq3bq18ufPL8MwVLp0aetI94YNG2zarFmzpr7++ms99thjDse1YMECSfdvfe/Zs6fy588vT09P1a1bV7/99luq2qhRo4amTJmiIkWKyNPTU+3atbNOkLds2TJrPXuPr2nTpho/fryKFCkiNzc3tW3bVl26dNGZM2dsbpdPSqFChZQnTx61bNlSHh4e8vDwUNWqVTV9+nQFBgam6tgAAAAAZC6mEvVDhw5JkjVhWLt2rVq1aqWAgAD17NlTkrRv3z5zESLDSLhN29PTU/ny5VP9+vU1Y8YMjR071lpn9erVatGihXx9feXu7i4vLy8VL15cknThwgWbNhs3bmy9bd5RJ06ckLu7u1q3bu1wG02aNLGJo1mzZpKk06dPW8vsPb769evblBUrVkySFBkZ+dC4PvroI5UoUUKlSpVS7969NWnSJOt8AAAAAACyJlP3zv57oq3du3db19tOWEf95s2bDrW9ZcsWLVmyRMeOHVOOHDlUtmxZ9enTR6VLl7apGxcXp5kzZ2rVqlW6ffu2KlasqIEDB6ps2bIO9Y2k9e3bV1OnTk12+5o1a9S2bVvrM+H/Xs4tYXbzB+XNm9cpsRmGYWpG9KRmZH/wWXTJseNL6tn6hPZSE2+RIkW0fft27d27Vxs3btTOnTv18ccfq3Llylq0aJHy58+fyiMEAAAAkFmYGlFPmK36zz//tD5/26hRI0nSqVOnJEmlSpWyu92ePXuqQYMG+uKLL7Rs2TItXrxYn376qcqXL68pU6YkqhsTE6O2bduqb9++Wrx4sVatWqUvv/xSVatWTfJWZKSd2bNnS7o/d8GNGzcUGxurqKgohYSE2NWOi8v90/JhS7AlqFChgmJjYxPdom6vNWvW2Myivnr1akn/dw476/j+7WHHa7FYVKNGDb355puaPXu2Dh8+rI0bN2rMmDGm+gUAAACQMZlK1AcOHCjp/gRypUuXlre3t+rWrSvp/m3wktSqVSu727169aratGmjL7/8UsuXL9fPP/+s119/XYZh6K233tL169etdSdOnKg///xTJUuW1IwZM7RixQr17dtXd+/eVa9evZIc5UTacHV1lcViUWxsrOLi4nT16lWtW7dOzz77rF3t5MmTRxaLRZs2bdL58+dTXFdcknr06CFJ6tevn6ZMmaJTp07p0qVLWrVqlRo3bpyqPoODg9WrVy8dPnxYly5d0vz58zV06FBZLBZ16tTJqcdnz/E+88wz+vTTT7V7925dv35d4eHhmjt3ruLi4hQaGmqqXwAAAAAZk6lb3/v27StJWrp0qfz8/DRy5Ejrmupr165V+fLlrcm8PebOnWtzS/Szzz6rGzduaO7cuQoNDbVunzp1qtzc3LRq1SrrEnFt27ZVZGSk5s+frxUrVlgTLaStrl27aubMmerWrVui8t69e2vbtm2pbidnzpx64okntHXrVvn7+0uSypcvryNHjiRZv0GDBho6dKiCgoL0+uuvOxR7586dtXjxYs2bNy9R+ZAhQ1SpUiVJzju+f0vpeM+ePatly5bpww8/tNmve/fuDvcJAAAAIOMyNaIu3U/Wf/vtN82YMUMlSpSwli9dulRHjhxx6DnxpJ5bvnz5svbt26e8efNab7kPDw/XyZMn1bhxY2uSnqBfv36SlOSSWbCPi4uLPDw85O7unmK9du3aad68eapRo4by5MmjMmXKaMSIEfruu+9s9k9oM7klxmbOnGmdXd3T09P6rLfFYkkylvHjx2v+/Plq3Lix8uXLp6JFi6p9+/batGmTtU5y+0pStWrVtGrVKtWtW1e+vr4qV66cPv/8c+vqBc48Pnd3d3l4eCSavC654122bJmGDRummjVrKm/evCpWrJiefvpprVmzxvRIPgAAAICMyWKYmYErjfXq1Utnz57VzZs3dejQIeXPn1+zZs1Sy5YtJd2fcK5BgwZ68803bZaBu3r1qvz8/PT0008n++xyRESEIiIirK/Dw8NVu3ZthYaGWkc2kbWdOHFCZcuW1X//+1999NFH6R0OkCWFzzS3sgOA9FPkpQz7NTFNcL0CMq+sdr0ydeu7p6dnqupFRUU51P6OHTt09OhRSfefD+7QoYOqVKli3X7r1i1JSY/AJ6zrnVAnKUFBQRo9erRDsQEAAAAAkBZMJeodO3a0Kbt796527dqlsLAwNW/eXH5+fg63P3v2bEVGRlon7Zo+fbpWrVql4OBg5c6d23qrcVITjd27d0+SUrxde+jQodbn7KX/G1EHAAAAACC9mErUFyxYkGT53bt31aVLF+3bty/RM8L2ejBp7tKli0qUKKH33ntPCxYsUN++fVWgQAFJSnL264SyggULJtu+j4+PfHx8HI4Pmd/DnpUHAAAAgEctTbITLy8vjRs3TpUrV9agQYO0fPlyp7SbMDHd+fPnJd2fGdvDw0N//fWX4uLirDPOS/fXdpeU6FZ54N9KlSrl8KMZAAAAAJAWTM/6npxSpUpJkjZs2GDXfleuXNHnn3+eaJI3STp16pTGjBkjSdYZ3j08PNSmTRuFhobq448/TlR37NixslgsSd6eDwAAAABARpVmifqePXskybrMVGpFRUXpvffeU/78+VWmTBnVq1dP5cqVU5kyZbRnzx4FBgYmWpZq5MiRypEjhz7++GOVLFlSderUUcWKFRUWFqaXX37ZZtk2AAAAAAAyMlO3vi9evNimzDAMhYSEaMKECZJk91rPBQoUsK5NffLkSZ08eVLS/Rnmn3/+ef3vf/9LlPxXr15dS5cuVb9+/XT69GmdPn1arq6ueuWVVzRlyhQTRwcAAAAAwKNnah11iyX5tSbd3d310ksvKSgoSLlz57a7bcMwdPbsWZ0/f17e3t4qV65ciqPz8fHxOnTokG7fvq2yZcsqf/78dvd57tw5BQQEsI46ADgR6xIDmVdWW5f4YbheAZlXVrtemRpRX7lypU2Zi4uLChQooFKlSsnX19fhti0Wi4oXL67ixYunqr6Li4sCAwMd7g8AAAAAgIzAVKLepk0bZ8UBAAAAAADkxOXZbt68qTNnzkiSihcvbmo0HQAAAACA7Mr0rO/BwcFq0aKF8uXLp6pVq6pq1arKly+fWrZsqeDgYGfECAAAAABAtmFqRP3w4cOqX7++IiIiVL58ebVo0UKStHbtWq1Zs0b169fX9u3bVaFCBacECwAAAABAVmcqUR8xYoQiIiLUtWtXzZ07V+7u7pKk2NhYde/eXYsWLdLw4cO1aNEipwQLAAAAAEBWZ+rW93Xr1kmSxo4da03SJcnNzU1jx46VJK1fv95MFwAAAAAAZCumEvVbt25JkooUKWKzrXDhwpKkiIgIM10AAAAAAJCtmErUS5QoIUnaunWrzbYtW7YkqgMAAAAAAB7OVKLeo0cPSVL//v21adMmGYYhwzC0ceNGDRgwQJLUvXt381ECAAAAAJBNmJpM7v3339e2bdu0atUqNWrUSDlz5pQk3blzR5LUpk0bvf/+++ajBAAAAAAgmzCVqHt4eGj58uVavHixlixZolOnTslisahkyZLq1KmTunTpIovF4qxYAQAAAADI8kwl6pLk4uKi5557Ts8995wz4gEAAAAAIFsznagnuHfvnu7cuSPDMGy25cmTx1ndAAAAAACQpZlK1A3D0IQJEzRt2jQdPXo0ySQ9oR4AAAAAAHg4U4n6mDFjNGLECLm5ualTp04qUaIEz6QDAAAAAGCCqUR92rRpkqS5c+fyjDoAAAAAAE5gah31K1euSJLat2/vlGAAAAAAAMjuTCXqVatWlSRdvXrVKcEAAAAAAJDdmUrUP//8c+XIkUMTJ050VjwAAAAAAGRrdj2j/sUXX9iUdezYUePHj1dwcLAaNGggT09PmzrvvPOO4xECAAAAAJCNWAw71k5zdEb3zLI827lz5xQQEKDQ0FD5+/undzgAkCWEz2Q1ECCzKvJS5vgO5yxcr4DMK6tdr+waUT9w4EBaxQEAAAAAAGRnoh4YGJhWcQAAAAAAAJmcTK5atWqqVq2aw9sBAAAAAEBido2o/9v+/ftNbQcAAAAAAImZGlFPybVr1yRJuXLlSqsuAAAAAADIcuweUR8zZsxDy+Li4rR582ZJUs2aNR0MDQAAAACA7MfuRH348OGpKpOkSpUq6auvvrI7KAAAAAAAsiu7E/Xjx49b/79s2bI2ZZLk4uKivHnzKm/evCbDAwAAAAAge7E7US9Tpoz1/xctWmRTBgAAAAAAHGdq1vcuXbo4Kw4AAAAAAKA0nPUdAAAAAADYj0QdAAAAAIAMhEQdAAAAAIAMhEQdAAAAAIAMhEQdAAAAAIAMhEQdAAAAAIAMxOFE/ebNm/r000/VsGFDFSpUSF5eXipcuLAaNWqkzz//XLdu3XJmnAAAAAAAZAsOraO+f/9+tWvXTmFhYZIkDw8P+fr66saNG9q0aZM2bdqkyZMna9WqVXr88cedGjAAAAAAAFmZ3SPqd+7c0bPPPquwsDDVr19fW7Zs0Z07d3Tx4kVFRkZq06ZNevLJJxUaGqpOnTopKioqLeIGAAAAACBLsjtRnz9/vk6dOqVKlSpp7dq1qlevnlxc7jfj6uqqBg0aaN26dapYsaKOHTumn376yelBAwAAAACQVdmdqP/222+SpHfeeUceHh5J1vHy8tI777wjSVqyZInj0QEAAAAAkM3Y/Yz6P//8I0lq1KhRivUaN24s6f7z7I64cuWK/v77b4WEhMjPz09VqlRRYGCgTb2//vpL27dvT7KNVq1aqUaNGg71DwAAAABAerA7Ub906ZIkqWjRoinWS9h++fJlu9q/e/euunfvruXLl+vevXuJtj311FOaMWOG/Pz8rGXLly/X+PHjk2wrd+7cJOoAAAAAgEzF7kQ9MjJSkuTp6ZliPS8vL0nS7du37W5/yZIlKlCggOrUqaOSJUvq0qVLWrp0qX7//Xf17t1by5cvt9lv8ODB1j4T1KxZ066+AQAAAABIbw4tz2YPwzDsqp8zZ079+uuvat++vdzd3a3lhw8fVu3atbVy5UrduXNHOXPmTLTf8OHDE420AwAAAACQGTmcqLdo0cKZcVjlzJlTHTt2tCmvWLGiateurfXr18tisdhs37hxo86ePavcuXOrbt26qlSpUprEBwAAAABAWnI4UV+7dq0z43iou3fv6uDBg2rdurXNLe6S1Llz50Svn3nmGc2ePVs+Pj6PKkQAAAAAAEyzO1G/detWWsSRIsMw1L9/f929e1cTJkyw2e7n56dGjRrJ399fp0+f1sqVK7V06VL16tUrxeXhIiIiFBERYX0dHh6eFuEDAAAAAJBqdifquXPnTos4khUXF6f+/fvr119/1fLly1W2bNlE27t166YxY8Ykmtzu5MmTatCggZYuXaqjR4+qfPnySbYdFBSk0aNHp2n8AAAAAADYw8WZjV27dk1ffvmlBg0apAkTJiQarXZEZGSkOnbsqF9++UVr1qxJcu32mjVr2sxAX7p0ab322muSpL179ybb/tChQxUaGmr92bFjh6l4AQAAAAAwy+4R9ZCQED399NMqXbq0li5dmqi8QYMGCgsLs5ZNmTJF27dvV548eewO7NKlS3rqqad0+vRprVu3TtWqVbNrf1dXV0kpzzrv4+PDM+wAAAAAgAzF7hH1GTNm6ODBg6pdu3ai8g8++EDXr1/XxIkT9eOPP6pw4cI6duyYgoKC7A7q+PHjqlu3rs6fP68NGzYkm6TfuXNHe/bssSkPDw/Xt99+K0kKDAy0u38AAAAAANKL3SPqK1eulCS1a9fOWhYdHa2lS5dq4MCBGjRokKT7I9rdunXT77//ro8//jjV7V+8eFH16tXTlStX9Oqrr2rp0qWJRu4lqW/fvvLz81NERIRq1qypevXqqVq1aipQoIBCQkL0yy+/6Pbt22revLkqV65s7yECAAAAAJBu7E7UT5w4IUmJJnXbv3+/oqKi1LZtW2tZs2bNEtVPrcuXL+vKlSuSpOnTpydZ56mnnpKfn59y5cql2rVra+vWrdq6dWuiOk2bNtWCBQvs6hsAAAAAgPRmd6Ke8Mx3bGystWz//v2SpKpVq1rLfH19Jd0fbbdHgQIF9N577z20jiR5e3tr+/bt2rt3r7Zv366wsDDly5dP9evX1xNPPGFXvwAAAAAAZAR2J+olSpTQ/v37tXPnTrVs2VKStGHDBhUrVkwFCxa01jt//rwkyd/f3672CxUqpM8++8yufapXr67q1avbtQ8AAAAAABmR3ZPJde7cWZI0ZMgQ/fXXX1q4cKEWL15sLU9w9OhRSVKlSpWcECYAAAAAANmD3SPqgwcP1pw5c3T48GHrc+iPPfaY/vOf/ySqt2jRIkmySeABAAAAAEDy7E7UfX19tWvXLn333Xc6duyYSpUqpb59+ypfvnyJ6hUtWlSvv/66OnXq5LRgAQAAAADI6uxO1CXJx8dHQ4cOTbHOmDFjHAoIAAAAAIDszO5n1AEAAAAAQNqxe0S9Vq1ayW5zcXGRr6+vKlWqpBdeeEFPPvmkqeAAAAAAAMhu7E7U9+3bl+y2+Ph4GYahNWvWaMKECRo0aJAmTpxoJj4AAAAAALIVu299j42NTfYnLi5OFy9e1A8//CBfX19NmjRJs2bNSou4AQAAAADIkpz6jLrFYlHBggX18ssv64svvpAkTZ8+3ZldAAAAAACQpaXZZHJt27aVJB04cCCtugAAAAAAIMtJs0Td19dXkhQVFZVWXQAAAAAAkOWkWaJ++PBhSVLRokXTqgsAAAAAALKcNEnUo6Oj9cEHH0j6v1vgAQAAAADAw9m9PNv777+f7Lb4+HhdvHhRa9eu1fnz51WwYEF9+OGHpgIEAAAAACA7sTtRHzdu3MMbdXNTx44dFRQUJH9/f4cCAwAAAAAgO7I7Uf/rr7+S3ebi4iIfHx+VK1dOOXPmNBUYAAAAAADZkd2JepMmTdIgDAAAAAAAIKXhrO8AAAAAAMB+JOoAAAAAAGQgJOoAAAAAAGQgJOoAAAAAAGQgJOoAAAAAAGQgJOoAAAAAAGQgdi/PlhTDMHT48GGdO3dOMTExeuqpp5zRLAAAAAAA2Y7pEfXff/9dZcqUUaVKldS6dWs9/fTTkqQWLVrIz89PwcHBpoMEAAAAACC7MJWo//XXX+rYsaOuXbumoKCgRNteeOEFXb16Vd98842pAAEAAAAAyE5MJeqjRo1SXFycvv32W7311luJtjVo0ECStHLlSjNdAAAAAACQrZhK1Hft2iVJatWqlc22EiVKSJLOnTtnpgsAAAAAALIVp8z67uJi28ytW7ckSbly5XJGFwAAAAAAZAumEvXAwEBJ0s6dO2227dixQ5JUvXp1M10AAAAAAJCtmErUBwwYIEl6++23E93ifuXKFQ0fPlyS9Prrr5vpAgAAAACAbMXUOuovv/yydu3apSlTpqh48eLW8qJFi+revXt666231LlzZ9NBAgAAAACQXZhK1CXp66+/VufOnTV37lwdPnxYhmGoXLly6tmzp1q0aOGMGAEAAAAAyDZMJ+qS1KxZMzVr1swZTQEAAAAAkK2Zekb97NmzzooDAAAAAADIZKJeokQJNWvWTLNmzdLt27edFRMAAAAAANmWqUS9f//+2rdvn1566SUVLlxYvXr10po1axQfH++s+AAAAAAAyFZMJerffPONLly4oMWLF6t58+ZasGCBWrZsqeLFi+uDDz7Q4cOHnRUnAAAAAADZgqlEXZJy5Mihzp07a+nSpQoLC9PEiRNVuHBhffbZZ3r88cdVu3ZtZ8QJAAAAAEC2YDpRf5Cfn58GDRqknTt3at68efLx8dHOnTud2QUAAAAAAFmaU5ZnSxAeHq65c+dq9uzZOnDggCSpfv36zuwCAAAAAIAszXSifufOHS1ZskSzZ8/WmjVrFBcXp5IlS2rEiBHq1auXSpcu7Yw4AQAAAADIFkwl6q+88ooWL16sW7duydvbW7169VLv3r3VqFEjWSwW08FFR0frzJkz8vPzU758+R5a//z587p9+7aKFSsmLy8v0/0DAAAAAPComXpGfdasWXryySf1448/6sKFC/rhhx/UuHFjU0l6bGyspk2bprp16ypnzpwqX7688ufPr4CAAH399dcyDMNmn61btyowMFD+/v6qUKGC/Pz89O677youLs7M4QEAAAAA8MiZGlE/e/asHnvsMWfFIkm6ceOGBgwYIEny8PBQsWLFdOXKFZ07d05vvPGGQkND9dlnn1nrHzlyRK1bt9bt27eVP39+5c+fX8ePH9cXX3yh2NhYffnll06NDwAAAACAtGRqRN3ZSbokubu7q3///tq2bZvu3LmjY8eO6cqVK5oyZYosFosmTZqk2NhYa/1Ro0bp9u3bevPNN3Xx4kUdPXpU+/fvl5+fnyZNmqTTp087PUYAAAAAANKKxUjqXvJkjBo1Ksn/pnY/s+rXr6+tW7fqxo0b8vX1VUxMjPLmzStfX1+dOXNG7u7u1rpfffWV3nrrLY0fP15Dhw5NVfvnzp1TQECAQkND5e/v75SYASC7C59pfs4SAOmjyEup/pqYJXC9AjKvrHa9sitRT3j2PGGX1D6LbkcXKapUqZIMw9ChQ4ckSQcPHlRgYKB69OihH3/8MVHdI0eOqGLFiurZs6dmz56dqvZJ1AHA+fjiC2ReWe2L78NwvQIyr6x2vbLrGfXQ0NAUX6elqVOn6vDhw/rtt9+sZRcuXJAkFS9e3KZ+QtnFixeTbTMiIkIRERHW1+Hh4c4KFwAAAAAAh9iVqP97lPlRjTovWLBAb7zxhj755BO1b9/eWn7v3j1J9yed+7ccOXJIur/EW3KCgoI0evRoJ0cLAAAAAIDjTE0mV61aNVWrVs3h7akxYcIE9ejRQ59//rnef//9RNty5colSbp586bNfgkj5blz50627aFDhyo0NNT6s2PHDlOxAgAAAABglqnl2fbv329qe0oMw9A777yjL7/8Ut9884369+9vU6dkyZKS7j+r/m/BwcGSpFKlSiXbh4+Pj3x8fByOEQAAAAAAZzOVqKfk2rVrkv5v1Nse0dHR6tmzp3755RfNmjVLPXv2TLKev7+/ihUrpr/++ktnzpxJ9Kz6zJkzJd2fKR4AAAAAgMzC7kR9zJgxDy2Li4vT5s2bJUk1a9a0q/3IyEi1a9dOGzdu1PDhw1W5cmXt27cvUZ0KFSrI09NTktSnTx+NHDlS7dq109ixY1WoUCH98ssv+uGHH1SoUCE9/fTTdvUPAAAAAEB6smt5Nin1S7JJ95dTmzNnjqpXr57qfYKDg1W5cuUU6xw4cECBgYGSpDt37qhp06Y2z5e7ubnp119/1VNPPZXqvlmeDQCcj+WOgMwrqy139DBcr4DMK6tdr+weUT9+/Lj1/8uWLWtTJkkuLi7Kmzev8ubNa3dAXl5eqlq16kPrJMiZM6c2bNigSZMmafXq1bp9+7YqVqyowYMH2/UHAgAAAAAAMgK7R9QftHjxYklSly5dnBZQemJEHQCcjxEqIPPKaiNUD8P1Csi8str1ytRkclklQQcAAAAAIKNwyqzvu3fv1vr16xUWFqbo6Gib7ZMnT3ZGNwAAAAAAZHmmEvWoqCg9//zzWrZsWYr1SNQBAAAAAEgdFzM7jx49WsuWLVOjRo0UEhJiLT99+rTq1Kmjbt26KSIiwnSQAAAAAABkF6YS9QULFkiSxo8frxIlSljLixcvrnHjxmn+/PkaN26cqQABAAAAAMhOTCXq58+fl3R/vXTp/rJskhQbG6saNWpIkubMmWOmCwAAAAAAshVTiXrRokUlSTdu3JAk5cuXT5J0/fp1ubq6SpIuX75spgsAAAAAALIVU4l6vXr1JEl79uyRJNWqVUuStG7dOq1bt06SVLJkSTNdAAAAAACQrZhK1N944w1J0hdffCFJGjp0qCwWi3r06KFnn33WWgYAAAAAAFLH1PJs9erV071792SxWCRJLVu21IoVKzRjxgxZLBZ17txZXbt2dUqgAAAAAABkB6YS9TfeeEPx8fGaMmWKtaxNmzZq06aN6cAAAAAAAMiOTN36/v333+ubb76RYRjOigcAAAAAgGzNVKJeuXJlSdLp06edEQsAAAAAANmeqUT9v//9r1xdXfX55587Kx4AAAAAALI1U8+oHzhwQB06dNDUqVO1d+9eNWvWzLqW+oPeeecdM90AAAAAAJBtWAwTD5gnzPb+MJnlGfZz584pICBAoaGh8vf3T+9wACBLCJ+Zun8rAGQ8RV7KHN/hnIXrFZB5ZbXrlekRdQAAAAAA4DymEvXAwEBnxQEAAAAAAGRyMrlq1aqpWrVqDm8HAAAAAACJmRpR379/v6ntAAAAAAAgMVMj6im5du2aJClXrlxp1QUAAAAAAFmO3SPqY8aMeWhZXFycNm/eLEmqWbOmg6EBAAAAAJD92J2oDx8+PFVlklSpUiV99dVXdgcFAAAAAEB2ZXeifvz4cev/ly1b1qZMklxcXJQ3b17lzZvXZHgAAAAAAGQvdifqZcqUsf7/okWLbMoAAAAAAIDjTE0m17lzZwUGBurYsWPWsoiICA0bNkw9evTQ0qVLTQcIAAAAAEB2YipRX7RokSpWrKhRo0ZZy7p3765PPvlE8+bNU8eOHfXHH3+YjREAAAAAgGzDVKI+adIkSdKgQYMkSSEhIdq0aZPOnj2roKAgSdKXX35pMkQAAAAAALIPU4n6oUOHJEmBgYGSpLVr16pVq1YKCAhQz549JUn79u0zFyEAAAAAANmIqUQ9NjY20evdu3erevXqkqRcuXJJkm7evGmmCwAAAAAAshVTiXrCbO9//vmnYmJitGLFCjVq1EiSdOrUKUlSqVKlTIYIAAAAAED2YffybA8aOHCgXn31VXXv3l0FChSQr6+v6tatK+n+bfCS1KpVK/NRAgAAAGmsqPF2eocAwEFGegfgZKYS9b59+0qSli5dKj8/P40cOVKurq6S7ifq5cuX18CBA81HCQAAAABANmEqUZfuJ+sJCfuDWEMdAAAAAAD7mXpGHQAAAAAAOJepEXVPT89U1YuKijLTDQAAAAAA2YapRL1jx442ZXfv3tWuXbsUFham5s2by8/Pz0wXAAAAAABkK6YS9QULFiRZfvfuXXXp0kX79u3Tpk2bzHQBAAAAAEC2YnoyuaR4eXlp3Lhxqly5sgYNGqTly5enRTfZWvhMS3qHAMBBRV7KaguIAAAAwJnSbDK5UqVKSZI2bNiQVl0AAAAAAJDlpFmivmfPHkmSh4dHWnUBAAAAAECWY+rW98WLF9uUGYahkJAQTZgwQZL07LPPmukCAAAAAIBsxVSi3rVr12S3ubu769VXX1VQUJCZLgAAAAAAyFZMJeorV660KXNxcVGBAgVUqlQp+fr6OtSuYRjatm2bfv31V/3222+6fv26Pv/8c/Xq1cum7scff6wpU6Yk2c6nn36ql19+2aEYAAAAAABID6YS9TZt2jgrjkTGjh2r4cOHJyq7c+dOknUjIiJ08eLFJLdFRkY6PTYAAAAAANJSmizPZlZ8fLyefPJJderUSXFxcfrwww8fus/BgweVL1++RGU+Pj5pFSIAAAAAAGnCrkR91KhRDnVi737Dhg3TiBEjJEkLFixI1T4FCxaUn5+fvaEBAAAAAJCh2JWojx492qFO7E3UXV1d7e6je/fuOnbsmHLnzq26detq6NChqlixot3tAAAAAACQnuxK1ENDQ9MqDtP+/PNP6/8fPHhQP/74o5YsWaLWrVsnu09ERIQiIiKsr8PDw9M0RgAAAAAAHsauRN3f3z+t4nCYt7e3hg0bptatW8vf31+nT5/W5MmT9csvv+ill15SSEiIPD09k9w3KCjI4bsEAAAAAABICxlyMjl7jBgxQhaLxfq6ZMmSatq0qZ566iktX75cW7ZsUfPmzZPcd+jQoerbt6/1dXh4uGrXrp3mMQMAAAAAkJwMOZmcPR5M0h/UqlUrLV++XOfPn092Xx8fH2aGBwAAAABkKBlyMjln2L9/vyTZLNkGAAAAAEBGlqknk7t8+bLefvttDRo0SFWqVJGHh4euX7+uyZMna8aMGcqZM6caNGiQ3mECAAAAAJBqGXIyua1bt+rZZ5+VJEVFRUmS3nvvPevI/MyZM9WmTRvFxcVpzpw5mjNnjlxcXOTt7a2bN29a2xk3bpzy5MnzSGIGAAAAAMAZXNI7gKTExMTo4sWLunjxojXxjoiIsJYlJO8FCxbUvHnz1KhRI3l4eOjmzZtyc3NTvXr1tHTpUr3xxhvpeRgAAAAAANjNocnk/v3f1O6XWvXq1UtxTfO8efNKklxcXNStWzd169ZNhmHo+vXr8vb2lru7u139AQAAAACQUVgMwzBSXfn/z7CesEtyM67/mx1dpKtz584pICBAoaGhGXLN+AeFz0zdew8g4ynyUua4JjoL1ysg88pu1yvLjHfSOwQADjJe/iK9Q3AqU5PJZbTJ5QAAAAAAyOxMTSaX0UedAQAAAADIbDLkZHIAAAAAAGRXdiXqI0aM0I0bN+zu5MaNGxoxYoTd+wEAAAAAkN3YlajPnDlTJUuW1FtvvaW9e/c+tP6ePXv01ltvqWTJkpo5c6ajMQIAAAAAkG3Y9Yz60aNH9b///U/jx4/XV199pcKFC6t27doqXbq08uXLZ10i7cSJE9qxY4cuXryoPHny6O2339bbb7+dVscAAAAAAECWYVei7uXlpREjRujtt9/Wjz/+qEWLFmnNmjVatmxZonq5c+dWgwYN1LVrV3Xr1k1eXl5ODRoAAAAAgKzKrkQ9Qa5cudS/f3/1799f9+7d0+nTp3X58mVZLBYVKFBAJUqUkJubQ00DAAAAAJCtmc6m3d3dVbZsWZUtW9YZ8QAAAAAAkK2xPBsAAAAAABkIiToAAAAAABkIiToAAAAAABkIiToAAAAAABkIiToAAAAAABkIiToAAAAAABmIqUS9WrVqqlatmsPbAQAAAABAYqbWUd+/f7+p7QAAAAAAILE0u/X92rVrkqRcuXKlVRcAAAAAAGQ5do+ojxkz5qFlcXFx2rx5sySpZs2aDoYGAAAAAED2Y3eiPnz48FSVSVKlSpX01Vdf2R0UAAAAAADZld2J+vHjx63/X7ZsWZsySXJxcVHevHmVN29ek+EBADK7osbb6R0CAAcZ6R0AAGRTdifqZcqUsf7/okWLbMoAAAAAAIDjTE0m17lzZwUGBurYsWPWsoiICA0bNkw9evTQ0qVLTQcIAAAAAEB2YipRX7RokSpWrKhRo0ZZy7p3765PPvlE8+bNU8eOHfXHH3+YjREAAAAAgGzDVKI+adIkSdKgQYMkSSEhIdq0aZPOnj2roKAgSdKXX35pMkQAAAAAALIPU4n6oUOHJEmBgYGSpLVr16pVq1YKCAhQz549JUn79u0zFyEAAAAAANmIqUQ9NjY20evdu3erevXqkqRcuXJJkm7evGmmCwAAAAAAshVTiXrCbO9//vmnYmJitGLFCjVq1EiSdOrUKUlSqVKlTIYIAAAAAED2YSpRHzhwoKT7E8iVLl1a3t7eqlu3rqT7t8FLUqtWrUyGCAAAAABA9mH3OuoP6tu3ryRp6dKl8vPz08iRI+Xq6irpfqJevnx5azIPAAAAAAAezlSiLt1P1hMS9gexhjoAAAAAAPYznagnuHnzps6cOSNJKl68uHx9fZ3VNAAAAAAA2YapZ9QlKTg4WC1atFC+fPlUtWpVVa1aVfny5VPLli0VHBzsjBgBAAAAAMg2TI2oHz58WPXr11dERITKly+vFi1aSLr/fPqaNWtUv359bd++XRUqVHBKsAAAAAAAZHWmEvURI0YoIiJCXbt21dy5c+Xu7i7p/vrq3bt316JFizR8+HAtWrTIKcECAAAAAJDVmbr1fd26dZKksWPHWpN0SXJzc9PYsWMlSevXrzfTBQAAAAAA2YqpRP3WrVuSpCJFithsK1y4sCQpIiLCTBcAAAAAAGQrphL1EiVKSJK2bt1qs23Lli2J6gAAAAAAgIczlaj36NFDktS/f39t2rRJhmHIMAxt3LhRAwYMkCR1797dfJQAAAAAAGQTpiaTe//997Vt2zatWrVKjRo1Us6cOSVJd+7ckSS1adNG77//vvkoAQAAAADIJkwl6h4eHlq+fLkWL16sJUuW6NSpU7JYLCpZsqQ6deqkLl26yGKxOCtWAAAAAACyPFOJuiS5uLjoueee03PPPeeMeAAAAAAAyNZMJ+ppJTY2Vhs3btRvv/2m69evq3fv3mratGmy9deuXatVq1bp9u3bqlixol588UXly5fvEUYMAAAAAIB5piaTW79+vTw9PdWuXTubbe3atZOnp6c2bNhgd7vz5s1ToUKF1Lx5c3311VeaNWuWjh49mmRdwzD0yiuvqEWLFvriiy80depUDRkyRI8//riCg4Pt7hsAAAAAgPRkKlEfO3asoqOj9fbbb9tse/vttxUdHa1PPvnE7naPHTumiIgINW3aVM8//3yKdX/44QfNmDFDefPm1ciRIzVlyhS1bNlSFy9e1AsvvKC4uDi7+wcAAAAAIL2YStR37dolSapVq5bNtoSy3bt3291u9+7ddeHCBa1bt04dO3ZMse6ECRNksVi0cuVKjRo1SgMHDtTq1avVqlUrHTx4UGvWrLG7fwAAAAAA0oupRP3evXuSpLt379psi4yMlCRFRUXZ3W65cuWUP3/+h9a7cuWKDhw4oLp166pOnTrWcovFojfffFPS/WfXAQAAAADILEwl6pUrV5Yk/fHHHzbbVq9eLUmqVKmSmS5SdPz4cUlSzZo1bbbVqFEjUZ2kRERE6Ny5c9af8PDwtAkUAAAAAIBUMpWov/rqq5Kkd999V6tWrVJ8fLzi4+O1cuVKvffee5Kk/v37m48yGREREZKU5OzuCSPyN2/eTHb/oKAgBQQEWH9q166dNoECAAAAAJBKppZne+WVV7R161Z9//33atu2rby8vGQYhvV29/79++uVV15xSqBJcXG5/3eG+Ph4m20JZa6ursnuP3ToUPXt29f6Ojw8nGQdAAAAAJCuTK+j/t1336lLly6aP3++jh07Jun+M+Y9evRQq1atTAeYkoRR86RuWQ8LC0tUJyk+Pj7y8fFJm+AAAAAAAHCA6URdktq0aaM2bdo4oym7lC9fXq6urtq4caPNtoSytHxGHgAAAAAAZzP1jHp6y5Url5o0aaJjx45p+vTp1vLr169b129/6qmn0is8AAAAAADs5pQRdWc7cuSIPvvsM0lSSEiIJGnWrFnatm2bJGnw4MHWWd2HDRumdevWqV+/fpo7d64KFSqkv/76S5cvX9ZTTz2l6tWrp89BAAAAAADggAyZqF+4cEGzZs1KVLZt2zZrot6xY0drot60aVNNnz5dgwcP1oYNG6z1mzVrptmzZz+6oAEAAAAAcIIMmahXqFBBM2bMSHZ7QpKeoE+fPurSpYs2bdqk27dvq2LFiqpatWpahwkAAAAAgNNlyES9cOHCeumll+zax9fXl+fRAQAAAACZXqaeTA4AAAAAgKyGRB0AAAAAgAzErlvfR40a5VAnju4HAAAAAEB2Y1eiPnr0aIc6IVEHAAAAACB17ErUQ0ND0yoOAAAAAAAgOxN1f3//tIoDAAAAAACIyeQAAAAAAMhQnLKO+u7du7V+/XqFhYUpOjraZvvkyZOd0Q0AAAAAAFmeqUQ9KipKzz//vJYtW5ZiPRJ1AAAAAABSx9St76NHj9ayZcvUqFEjhYSEWMtPnz6tOnXqqFu3boqIiDAdJAAAAAAA2YWpRH3BggWSpPHjx6tEiRLW8uLFi2vcuHGaP3++xo0bZypAAAAAAACyE1OJ+vnz5yVJlSpVut+Yy/3mYmNjVaNGDUnSnDlzzHQBAAAAAEC2YipRL1q0qCTpxo0bkqR8+fJJkq5fvy5XV1dJ0uXLl810AQAAAABAtmIqUa9Xr54kac+ePZKkWrVqSZLWrVundevWSZJKlixppgsAAAAAALIVU4n6G2+8IUn64osvJElDhw6VxWJRjx499Oyzz1rLAAAAAABA6phanq1evXq6d++eLBaLJKlly5ZasWKFZsyYIYvFos6dO6tr165OCRQAAAAAgOzAVKIuSW5uiZto06aN2rRpY7ZZAAAAAACyJVO3vgMAAAAAAOeya0R91KhRSf43tfsBAAAAAICUWQzDMFJd+f8/i56wS8Lrh7Gji3R17tw5BQQEKDQ0VP7+/ukdTorCZ6buvQeQ8RR5KXNcE53FMuOd9A4BgIOMl79I7xAeKa5XQOaV1a5Xdo2oh4aGpvgaAAAAAACYY1ei/u9R5ow+6gwAAAAAQGbDZHIAAAAAAGQgphL19evXy9PTU+3atbPZ1q5dO3l6emrDhg1mugAAAAAAIFsxlaiPHTtW0dHRevvtt222vf3224qOjtYnn3xipgsAAAAAALIVU4n6rl27JEm1atWy2ZZQtnv3bjNdAAAAAACQrZhK1O/duydJunv3rs22yMhISVJUVJSZLgAAAAAAyFZMJeqVK1eWJP3xxx8221avXi1JqlSpkpkuAAAAAADIVkwl6q+++qok6d1339WqVasUHx+v+Ph4rVy5Uu+9954kqX///uajBAAAAAAgm7BrHfV/e+WVV7R161Z9//33atu2rby8vGQYhvV29/79++uVV15xSqAAAAAAAGQHphJ1Sfruu+/UpUsXzZ8/X8eOHZMklStXTj169FCrVq1MBwgAAAAAQHZiOlGXpDZt2qhNmzbOaAoAAAAAgGzNKYm6dH8G+Dt37sgwDJttefLkcVY3AAAAAABkaaYSdcMwNGHCBE2bNk1Hjx5NMklPqAfnKmq8nd4hAHAQV0QAAACkxFSiPmbMGI0YMUJubm7q1KmTSpQoIYvF4qzYAAAAAADIdkwl6tOmTZMkzZ07V88995xTAgIAAAAAIDsztY76lStXJEnt27d3SjAAAAAAAGR3phL1qlWrSpKuXr3qlGAAAAAAAMjuTCXqn3/+uXLkyKGJEyc6Kx4AAAAAALI1U8+o79y5Ux07dtT48eMVHBysBg0ayNPT06beO++8Y6YbAAAAAACyDVOJ+rvvvmv9/9WrV2v16tVJ1iNRBwAAAAAgdUwl6gcOHHBWHAAAAAAAQCYT9cDAQGfF4bB9+/bpyJEjSW6rWbOmypYt+4gjAgAAAADAcaYS9Yzgxx9/1Pjx45PcNmnSJBJ1AAAAAECm4pREfffu3Vq/fr3CwsIUHR1ts33y5MnO6CZFnTp1Uo4cORKVlStXLs37BQAAAADAmUwl6lFRUXr++ee1bNmyFOs9ikT922+/lZ+fX5r3AwAAAABAWjK1jvro0aO1bNkyNWrUSCEhIdby06dPq06dOurWrZsiIiJMB5kaISEhWrJkidasWaPr168/kj4BAAAAAHA2UyPqCxYskCSNHz9eJUqUsJYXL15c48aNU5MmTVSqVCmNGTPGVJCpUbt2bev/58iRQwMHDtTnn39uczs8AAAAAAAZmakR9fPnz0uSKlWqdL8xl/vNxcbGqkaNGpKkOXPmmOkiVdzd3VWjRg116NBBVapUUUxMjCZMmKA33ngjxf0iIiJ07tw56094eHiaxwoAAAAAQEpMJepFixaVJN24cUOSlC9fPknS9evX5erqKkm6fPmymS4eqk2bNjpz5ox2796tpUuXav/+/dq4caO8vb313Xff6cyZM8nuGxQUpICAAOvPg6PyAAAAAACkB1OJer169SRJe/bskSTVqlVLkrRu3TqtW7dOklSyZEkzXTxUixYtVKRIkURlDRs21KBBg2QYhnbs2JHsvkOHDlVoaKj1J6W6AAAAAAA8CqYS9YRby7/44gtJ9xNfi8WiHj166Nlnn7WWpYe8efNKkmJiYpKt4+PjI39/f+vPvxN+AAAAAAAeNVOTydWrV0/37t2TxWKRJLVs2VIrVqzQjBkzZLFY1LlzZ3Xt2tUpgSYlOjpa165ds0mwIyMjNXv2bEmspQ4AAAAAyFxMJeqS5OaWuIk2bdqoTZs2ZptNlevXr6tkyZLq3LmzqlWrpgIFCigkJEQzZ87U2bNnVb16devt+AAAAAAAZAamE/UE9+7d0507d2QYhs22PHnyOKubRDw9PeXn56d58+Zp3rx5ibaVL19eixcvto72AwAAAACQGZhK1A3D0IQJEzRt2jQdPXo0ySQ9oV5ayJMnj06fPq3ffvtN27dvV1hYmPLly6f69evrmWeeYQ11AAAAAECmYypRHzNmjEaMGCE3Nzd16tRJJUqUeOQj2Al9d+rU6ZH2CwAAAABAWjCVqE+bNk2SNHfuXD333HNOCQgAAAAAgOzM1PJsV65ckSS1b9/eKcEAAAAAAJDdmUrUq1atKkm6evWqU4IBAAAAACC7M5Wof/7558qRI4cmTpzorHgAAAAAAMjWTD2jvnPnTnXs2FHjx49XcHCwGjRoIE9PT5t677zzjpluAAAAAADINkwl6u+++671/1evXq3Vq1cnWY9EHQAAAACA1DGVqB84cMBZcQAAAAAAAJlM1AMDA50VBwAAAAAAkMnJ5AAAAAAAgHPZNaI+atSoJP+b2v0AAAAAAEDK7ErUR48eLen/Eu+E1w9Dog4AAAAAQOrYlaiHhoam+BoAAAAAAJhjV6Lu7+9vnUAuODhY/v7+kqQ1a9ZIklq0aOHk8AAAAAAAyF7snvX94MGDNmUtW7aUJBmGYT4iAAAAAACyMWZ9BwAAAAAgAzG1jjrST79tt9I7BACOejm9A3i0uF4BmRjXKwCZRRa7XjGiDgAAAABABuLwiPqNGzdSVSZJefLkcbQbAAAAAACyFYcT9bx586aqTGKSOQAAAAAAUsvuRL1169ZpEQcAAAAAAJADifqqVavSIg4AAAAAACAmkwMAAAAAIEMhUQcAAAAAIAMhUQcAAAAAIAMhUQcAAAAAIAMhUQcAAAAAIAMhUQcAAAAAIAMhUQcAAAAAIAMhUQcAAAAAIAMhUQcAAAAAIAMhUQcAAAAAIAMhUQcAAAAAIAMhUQcAAAAAIAMhUQcAAAAAIAMhUQcAAAAAIAMhUQcAAAAAIAMhUQcAAAAAIAMhUQcAAAAAIAMhUQcAAAAAIAMhUQcAAAAAIAMhUQcAAAAAIAMhUQcAAAAAIAPJUom6YRiKjo5O7zAAAAAAAHBYlkjUjx8/rqefflqenp7y9PRUqVKl9PXXX6d3WAAAAAAA2M0tvQMw6/z582rYsKEuXrwoi8UiDw8PhYSE6I033tCtW7f0/vvvp3eIAAAAAACkWqYfUR89erQuXryorl276sqVK7p7965+//135cyZU6NHj9aFCxfSO0QAAAAAAFItUyfqcXFx+umnn5Q3b17NmDFD+fLlk8ViUfv27fX+++8rKipKv/76a3qHCQAAAABAqmXqRP3EiRO6efOmWrRooVy5ciXa9swzz0iSdu3alR6hAQAAAADgkEz9jHpYWJgkqXTp0jbbEsoS6iQlIiJCERER1tehoaGSpPDwcGeGmSZu376d3iEAcNC5c+fSO4RHiusVkHlxvQKQWWSm61XhwoXl5pZyKp6pE/WoqChJkpeXl822hLK7d+8mu39QUJBGjx5tU167dm0nRQgAtubNm5feIQBAqnC9ApBZZKbrVWhoqPz9/VOsk6kT9Zw5c0pK+q+fCWX/viX+QUOHDlXfvn2tr6OiohQaGqqSJUs+9C8cQFoJDw9X7dq1tWPHDhUpUiS9wwGAZHG9ApBZcL1CRlK4cOGH1snU2WhAQIAk6ejRozbbEsoS6iTFx8dHPj4+icrKlCnjxAgBxxUpUuShf2kDgIyA6xWAzILrFTKLTD2ZXMmSJVWwYEGtXbtWV69eTbRt4cKFkqQ6deqkR2gAAAAAADgkUyfqFotFL774oiIjI/Xss89q3759Cg8P19dff60JEybIx8dHHTt2TO8wAQAAAABItUx967skDRs2TEuXLtXGjRtVvXr1RNuCgoKUN2/edIoMcIyPj49Gjhxp81gGAGQ0XK8AZBZcr5DZWAzDMNI7CLMuX76sUaNGafXq1bp9+7YqVqyooUOH6umnn07v0AAAAAAAsEuWSNQBAAAAAMgqMvUz6gAAAAAAZDUk6gAAAAAAZCAk6gAAAAAAZCCZftZ3IKO5e/euZs2aZX3t4uIib29vFSxYUFWrVpWfn99D29i/f7/++ecfeXh4qGHDhipSpEiydQ8fPqzNmzcrLi5Or776qlxdXZ1yHACyvkuXLumXX36xvnZ1dZW3t7eKFCmiatWqydfX96Ft7N69W7t27ZKXl5d69eqVbD3DMLR9+3YdPXpU3t7eatq0KSuzAEi1U6dO6Y8//rC+dnNzk4+Pj/z9/VW1alXlypUrxf3j4+O1detWHTx4UAUKFNCzzz7rlLpAmjEAOFV4eLghKdmfBg0aGH/88UeS+96+fdto3759ovpubm7Gp59+alN37NixRoUKFRLVvXv3blofHoAs5O+//072WuXi4mK0adPG2LVrV5L7DhkyxChWrJi1fqFChZLt58KFC0adOnUStZ8zZ05j1qxZaXVoALKYRYsWJXu9cnd3N7p06WIcPXrUZr/bt28b/fr1MwoVKmStX7NmzST7sKcukNaY9R1wsgsXLqhIkSIqUaKEWrduLUm6c+eOzp8/r507d+rWrVuSpK+++kpDhgxJtG+vXr00Z84c5c+fX23atFFERIRWrVqle/fu6aefflLXrl2tdUuUKKEzZ86oQoUKioiIUFhYmO7evStPT89Hd7AAMrVt27apbt26qly5surVqydJioyM1OnTp7Vr1y5FRUXJzc1NCxcutBlRslgskqQaNWro5MmT8vT01IULF5Lsp3Hjxtq4caP8/f3VvHlzhYWFac2aNbJYLNqyZYuefPLJtD1QAJne4sWL1bVrVz355JOqWrWqDMPQ7du3dfLkSe3evVuxsbHKmTOnVq9erQYNGlj3O3funAICAuTi4qK6detq+/btqlq1qnbt2mXThz11gTSXzn8oALKchBH1Z555xmZbZGSk8fHHHxsWi8VwcXExDh8+bN0WEhJiWCwWo2jRokZ4eLi1/M8//zQsFosRGBiYqK1p06ZZ92/dujUj6gDsljCiPmTIEJtt169fN1577TVDkuHt7W1cuXIl0favvvrKOHPmjGEYhlG+fPlkR9TXr19vSDIqV65s3Lp1y1r+3XffGZKMp556ynkHBCDLShhR/9///mezLTw83OjatashyfD39zeio6Ot265fv25MnTrVuHDhgmEYhpErV65kR8ntqQukNSaTAx6hnDlzavjw4RoyZIji4+P1zTffWLetXLlShmFo8ODBKly4sLW8RYsWat68uYKDg3XmzBlreb9+/VShQoVHGj+A7CNPnjz6+uuv1bFjR926dUuzZ89OtH3IkCEqVqzYQ9tZsWKFJGnYsGHKnTu3tbxPnz4qW7as/vjjD8XExDg3eADZSuHChTV//nzVqVNH586d09KlS63b8uTJo/79+6tQoUIPbceeukBaI1EH0sHrr78uSdqyZYu17NChQ5Kkhg0b2tRPuIXr4MGDjyA6APg/SV2v7JHSta1+/fqKiYnRiRMnHA8QAHR/MswBAwZIcvx6BWQkJOpAOihTpozc3d0TPc957do1SUpyhveEsoQ6APCoVKxYUZKSff78YRKuWw/eKZSAaxsAZzJ7vQIyEhJ1IJ24uLgoLi7O+jo+Pl7S/03Q9O+6D9YBgEclYcnHB69X9oiPj5fFYuHaBiDNmb1eARkJiTqQDs6fP6/o6GgVKFDAWpawXvHVq1dt6l+5ciVRHQB4VE6ePClJia5X9vD19ZVhGEmOmnNtA+BMZq9XQEZCog6kg3nz5kmSateubS0rV66cJCW5BMju3bsT1QGARyWp65U9HnZts1gsKlOmjOMBAsD/Z/Z6BWQkJOrAI/b7779r5MiRkqSXX37ZWt6sWTNJ0uTJkxUdHW0tP3bsmJYtW6bHHnuMWd4BPFLTp0/X1KlT5e7urhdffNGhNhKubUFBQTIMw1q+YcMG7dq1S3Xr1lWuXLmcEi+A7Ck+Pl7//e9/tWzZMuXJk0edOnVK75AA09zSOwAgqzp9+rSmTp0qSYqKitK5c+e0fv166+j4wIEDVb9+fWv9atWqqXHjxtqwYYPq1aunbt266datW5o6dapiYmI0ZMiQRM94/vXXXzp69KgkKTQ0VJL03Xffyc3NTR4eHon+CAAAKTlw4ID1enXnzh2dPn1af/75p44cOSJJ+vTTT1WiRIlE+yxbtkxhYWGSpBs3buju3bvWNvz8/NSlSxdJUvv27VWmTBn98ccfat68uTp06KCwsDBr3TfffPMRHCGArGLbtm2aOnWqDMNQZGSkTp48qZUrV1qXsJ08ebLN4zTz58/XzZs3JUmxsbG6fPmy9RpUvHhxtW3b1qG6QJpK32XcgawnPDzckJTsT/78+Y3x48cbcXFxNvueOXPGKF26tM0+Xbp0MWJjYxPV7d27d7J9+Pr6PqKjBZCZ/f333yler/z9/Y1Zs2YluW/jxo2T3a9q1aqJ6u7Zs8coWLCgTb0333zzERwlgKxg0aJFKV6vypYta/z+++9J7pvUd6uEn/bt2ztcF0hLjKgDTpYzZ07179/f+trFxUW5cuVSoUKFVK1aNTVq1Eg5cuRIct9ixYrpn3/+0U8//aR//vlHHh4eatKkiVq3bm1Tt2nTpvL09Ew2BgB4mEKFCtlcr7y9vVW0aFHVqFFD9erVs86i/G8dOnRI9nGcgICARK+rV6+uI0eOaP78+Tp27Ji8vb3Vrl071a1b13kHAyBLK126dKLrlZubm3Lnzq1ixYrpiSee0BNPPJHsvt27d9elS5eS3FalShWH6wJpyWIYDzwwBgAAAAAA0hWTyf2/9utYAAAAAGCQv/U0dpRFAAAAMCLqAAAAMCLqAAAAMCLqAAAAMCLqAAAAMCLqAAAAMCLqAAAAMCLqAAAAMCLqAAAAMCLqAAAAMCLqAAAAMCLqAAAAMCLqAAAAMCLqAAAAMBJl9Kmm2hl2rAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 990x495 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plot_cost_components(report, SurveyCosts())\n",
+    "show(fig)\n",
+    "plt.close(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6812b59",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Appendix: inspect weighting and Monte Carlo precision\n",
+    "\n",
+    "For the same group estimates, a sample-weighted average changes when allocation\n",
+    "changes. The population-weighted estimate uses the fixed 80:20 composition.\n",
+    "The comparison below demonstrates the estimand, not an accuracy claim from one draw."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "80988a02",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-03T09:22:23.337273Z",
+     "iopub.status.busy": "2026-10-03T09:22:23.336997Z",
+     "iopub.status.idle": "2026-10-03T09:22:23.359237Z",
+     "shell.execute_reply": "2026-10-03T09:22:23.357199Z"
+    },
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Population-weighted estimate: 0.784\n",
+      "Sample-weighted average:     0.760\n",
+      "Population target:           0.850\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_d132e\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d132e_level0_col0\" class=\"col_heading level0 col0\" >phase</th>\n",
+       "      <th id=\"T_d132e_level0_col1\" class=\"col_heading level0 col1\" >surveys_per_design</th>\n",
+       "      <th id=\"T_d132e_level0_col2\" class=\"col_heading level0 col2\" >largest_subgroup_mae_mcse_pp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td id=\"T_d132e_row0_col0\" class=\"data row0 col0\" >screen</td>\n",
+       "      <td id=\"T_d132e_row0_col1\" class=\"data row0 col1\" >100</td>\n",
+       "      <td id=\"T_d132e_row0_col2\" class=\"data row0 col2\" >0.676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td id=\"T_d132e_row1_col0\" class=\"data row1 col0\" >refine</td>\n",
+       "      <td id=\"T_d132e_row1_col1\" class=\"data row1 col1\" >1000</td>\n",
+       "      <td id=\"T_d132e_row1_col2\" class=\"data row1 col2\" >0.200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x790845413ed0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sample_weights = np.array(outcome.participants) / sum(outcome.participants)\n",
+    "print(f\"Population-weighted estimate: {np.dot(GROUP_WEIGHTS, outcome.prevalence):.3f}\")\n",
+    "print(f\"Sample-weighted average:     {np.dot(sample_weights, outcome.prevalence):.3f}\")\n",
+    "print(f\"Population target:           {np.dot(GROUP_WEIGHTS, truth):.3f}\")\n",
+    "\n",
+    "precision_rows = []\n",
+    "for phase in (\"screen\", \"refine\"):\n",
+    "    summary = study.results(phase).aggregate_replicates()\n",
+    "    errors = [\n",
+    "        row[\"score_std\"][\"underserved_error_pp\"] / np.sqrt(row[\"n_reps\"] - 1)\n",
+    "        for row in summary.metadata\n",
+    "    ]\n",
+    "    precision_rows.append({\n",
+    "        \"phase\": phase,\n",
+    "        \"surveys_per_design\": summary.metadata[0][\"n_reps\"],\n",
+    "        \"largest_subgroup_mae_mcse_pp\": max(errors),\n",
+    "    })\n",
+    "show(\n",
+    "    pd\n",
+    "    .DataFrame(precision_rows)\n",
+    "    .style.hide(axis=\"index\")\n",
+    "    .format({\"largest_subgroup_mae_mcse_pp\": \"{:.3f}\"})\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f4e667b",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Appendix: export and present without execution\n",
+    "\n",
+    "The notebook stores tables and figures. Generate a standalone HTML presentation\n",
+    "from those saved outputs:\n",
+    "\n",
+    "```bash\n",
+    "uv run --extra notebook jupyter nbconvert --to html examples/serosurvey_study.ipynb\n",
+    "```\n",
+    "\n",
+    "For a fresh headless run, with a 60-second limit per cell:\n",
+    "\n",
+    "```bash\n",
+    "uv run --extra notebook jupyter nbconvert --execute --to notebook \\\n",
+    "  --ExecutePreprocessor.timeout=60 --output serosurvey_executed.ipynb \\\n",
+    "  --output-dir /tmp examples/serosurvey_study.ipynb\n",
+    "```\n",
+    "\n",
+    "Export results from a code cell if needed:\n",
+    "\n",
+    "```python\n",
+    "report.summary.to_dataframe(include_metadata=True).to_csv(\"serosurvey_designs.csv\", index=False)\n",
+    "```\n",
+    "\n",
+    "The companion script regenerates the documentation's PNG figures:\n",
+    "\n",
+    "```bash\n",
+    "uv run --extra notebook python examples/serosurvey_study.py\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "531c85ec",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Sources and audience connections\n",
+    "\n",
+    "- [IVAC's portfolio](https://publichealth.jhu.edu/ivac/projects): coverage/equity,\n",
+    "  epidemiology, economics/finance, operations research and policy.\n",
+    "- [SISS](https://publichealth.jhu.edu/ivac/our-work/strengthening-immunization-systems-through-serosurveillance-siss)\n",
+    "  and [Serosurvey Tools](https://serosurveytools.org/about/): design and use of serological surveillance.\n",
+    "- [Carcelen et al., 2020](https://pmc.ncbi.nlm.nih.gov/articles/PMC7561102/):\n",
+    "  *How much does it cost to measure immunity?* This is motivation, not a reproduction.\n",
+    "- [Andrea Carcelen](https://publichealth.jhu.edu/faculty/4158/andrea-carcelen):\n",
+    "  serosurveillance and reaching vulnerable populations.\n",
+    "- [Bryan Patenaude](https://publichealth.jhu.edu/faculty/3683/bryan-n-patenaude):\n",
+    "  economic evaluation, financing and equity measurement.\n",
+    "- [Shaun Truelove](https://publichealth.jhu.edu/faculty/3998/shaun-truelove):\n",
+    "  modeling to inform prevention and response.\n",
+    "- [Chizoba Wonodi](https://publichealth.jhu.edu/faculty/2206/chizoba-barbara-wonodi)\n",
+    "  and [Molly Sauer](https://publichealth.jhu.edu/faculty/3466/molly-sauer):\n",
+    "  immunization delivery, implementation and prioritization.\n",
+    "- [Svea Closser](https://publichealth.jhu.edu/faculty/3657/svea-closser):\n",
+    "  health systems and the experiences of frontline workers.\n",
+    "\n",
+    "These connections informed the example's scope; they do not imply endorsement."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/serosurvey_study.py b/examples/serosurvey_study.py
new file mode 100644
index 0000000..b0737cc
--- /dev/null
+++ b/examples/serosurvey_study.py
@@ -0,0 +1,578 @@
+"""Synthetic serosurvey design for a short IVAC trade-study demonstration.
+
+All populations, prevalences and prices are invented. The model compares
+estimation of antibody-status prevalence, not clinical protection.
+Run: uv run --extra notebook python examples/serosurvey_study.py
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field, replace
+from pathlib import Path
+from time import perf_counter
+from typing import TYPE_CHECKING, Any
+
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.lines import Line2D
+
+from trade_study import (
+    Constraint,
+    Direction,
+    Factor,
+    FactorType,
+    Observable,
+    Phase,
+    PreferencePolicy,
+    Study,
+    build_grid,
+    preference_sweep,
+)
+
+if TYPE_CHECKING:
+    from matplotlib.figure import Figure
+    from numpy.typing import NDArray
+
+    from trade_study import PreferenceSweep, ResultsTable
+
+GROUP_WEIGHTS = (0.8, 0.2)
+GROUP_NAMES = ("Other communities", "Underserved communities")
+COLORS = {"proportional": "#0072B2", "oversample": "#D55E00"}
+PRIORITIES = {
+    "Cost first": {
+        "cost_usd": 0.95,
+        "overall_error_pp": 0.025,
+        "underserved_error_pp": 0.025,
+    },
+    "Overall accuracy": {
+        "cost_usd": 0.1,
+        "overall_error_pp": 0.8,
+        "underserved_error_pp": 0.1,
+    },
+    "Subgroup accuracy": {
+        "cost_usd": 0.1,
+        "overall_error_pp": 0.1,
+        "underserved_error_pp": 0.8,
+    },
+}
+BUDGET = 40_000.0
+
+
+def allocation_counts(
+    config: dict[str, Any],
+) -> tuple[tuple[int, int], tuple[int, int]]:
+    """Allocate participants and communities to the two population groups.
+
+    Args:
+        config: Participant/community totals and allocation label. Totals
+            must be positive multiples of ten, with at least one person
+            in each community. Proportional allocation is 80:20; the
+            oversampling allocation is 50:50.
+
+    Returns:
+        Participant counts, then community counts, ordered by group.
+
+    Raises:
+        ValueError: If totals or allocation are unsupported.
+    """
+    n, k = config["participants"], config["communities"]
+    if any(
+        not isinstance(value, (int, np.integer)) or isinstance(value, bool)
+        for value in (n, k)
+    ):
+        msg = "Participant and community totals must be integers"
+        raise ValueError(msg)
+    if n < k or k < 10 or n % 10 or k % 10:
+        msg = "Use integer multiples of ten with participants >= communities >= 10"
+        raise ValueError(msg)
+    shares = {"proportional": GROUP_WEIGHTS, "oversample": (0.5, 0.5)}
+    if config["allocation"] not in shares:
+        msg = "Allocation must be 'proportional' or 'oversample'"
+        raise ValueError(msg)
+    fraction = shares[config["allocation"]][1]
+    second_n, second_k = round(n * fraction), round(k * fraction)
+    return (int(n - second_n), second_n), (int(k - second_k), second_k)
+
+
+@dataclass(frozen=True)
+class SurveyOutcome:
+    """Group estimates and exact field-work counts for one simulated survey.
+
+    Attributes:
+        prevalence: Estimated antibody-status prevalence in each group.
+        participants: Participant counts ordered by group.
+        communities: Community counts ordered by group.
+    """
+
+    prevalence: NDArray[np.float64]
+    participants: tuple[int, int]
+    communities: tuple[int, int]
+
+
+@dataclass(frozen=True)
+class SurveyWorld:
+    """Independent clustered surveys with known hypothetical group means.
+
+    Attributes:
+        prevalence: Target means for other/underserved communities.
+        correlation: Within-community intraclass correlation. Communities
+            are independent, with Beta-distributed probabilities.
+        seed: Phase-specific seed; configuration and replicate identify
+            independent, reproducible random streams.
+    """
+
+    prevalence: tuple[float, float] = (0.9, 0.65)
+    correlation: float = 0.06
+    seed: int = 2026
+
+    def __post_init__(self) -> None:
+        """Validate the illustrative population and random seed.
+
+        Raises:
+            ValueError: If probabilities, correlation or seed are invalid.
+        """
+        if len(self.prevalence) != 2 or not all(
+            np.isfinite(p) and 0 <= p <= 1 for p in self.prevalence
+        ):
+            msg = "Require two finite probabilities between zero and one"
+            raise ValueError(msg)
+        if not np.isfinite(self.correlation) or not 0 <= self.correlation < 1:
+            msg = "Require finite 0 <= correlation < 1"
+            raise ValueError(msg)
+        if (
+            not isinstance(self.seed, int)
+            or isinstance(self.seed, bool)
+            or self.seed < 0
+        ):
+            msg = "Require a nonnegative integer seed"
+            raise ValueError(msg)
+
+    def generate(
+        self, config: dict[str, Any], *, rep: int = 0
+    ) -> tuple[NDArray[np.float64], SurveyOutcome]:
+        """Simulate clustered counts, preserving exact participant totals.
+
+        Args:
+            config: Survey participant/community totals and allocation.
+            rep: Nonnegative replicate id. Configurations do not share
+                random streams; this example makes no paired-CRN claim.
+
+        Returns:
+            Fixed target group means and one survey's group estimates.
+
+        Raises:
+            ValueError: If the replicate id or configuration is invalid.
+        """
+        if not isinstance(rep, int) or isinstance(rep, bool) or rep < 0:
+            msg = "Replicate id must be a nonnegative integer"
+            raise ValueError(msg)
+        people, communities = allocation_counts(config)
+        allocation_id = int(config["allocation"] == "oversample")
+        rng = np.random.default_rng(
+            np.random.SeedSequence([
+                self.seed,
+                rep,
+                int(config["participants"]),
+                int(config["communities"]),
+                allocation_id,
+            ])
+        )
+        estimates = []
+        for p, n, k in zip(self.prevalence, people, communities, strict=True):
+            sizes = np.full(k, n // k, dtype=int)
+            sizes[: n % k] += 1
+            if self.correlation == 0 or p in {0, 1}:
+                probabilities = np.full(k, p)
+            else:
+                concentration = 1 / self.correlation - 1
+                probabilities = rng.beta(p * concentration, (1 - p) * concentration, k)
+            positives = rng.binomial(sizes, probabilities)
+            estimates.append(float(positives.sum() / n))
+        return np.array(self.prevalence), SurveyOutcome(
+            np.array(estimates), people, communities
+        )
+
+
+@dataclass(frozen=True)
+class SurveyCosts:
+    """Invented financial costs in illustrative USD, without price calibration.
+
+    Attributes:
+        setup: Fixed survey setup cost.
+        per_community: Community visit costs ordered by group.
+        per_participant: Participant costs ordered by group.
+    """
+
+    setup: float = 3000.0
+    per_community: tuple[float, float] = (250.0, 700.0)
+    per_participant: tuple[float, float] = (30.0, 40.0)
+
+    def __post_init__(self) -> None:
+        """Validate the nonnegative financial cost assumptions.
+
+        Raises:
+            ValueError: If cost coefficients are invalid.
+        """
+        if len(self.per_community) != 2 or len(self.per_participant) != 2:
+            msg = "Supply one cost per population group"
+            raise ValueError(msg)
+        values = (self.setup, *self.per_community, *self.per_participant)
+        if not all(np.isfinite(value) and value >= 0 for value in values):
+            msg = "Costs must be finite and nonnegative"
+            raise ValueError(msg)
+
+    def components(self, config: dict[str, Any]) -> dict[str, float]:
+        """Compute additive fixed, community and participant costs.
+
+        Args:
+            config: Survey participant/community totals and allocation.
+
+        Returns:
+            Three separate financial cost components in illustrative USD.
+        """
+        people, communities = allocation_counts(config)
+        return {
+            "Setup": self.setup,
+            "Community visits": float(np.dot(communities, self.per_community)),
+            "Participants": float(np.dot(people, self.per_participant)),
+        }
+
+
+@dataclass(frozen=True)
+class SurveyScorer:
+    """Financial cost and absolute prevalence error, with population weighting.
+
+    Attributes:
+        costs: Illustrative financial cost coefficients.
+    """
+
+    costs: SurveyCosts = field(default_factory=SurveyCosts)
+
+    def score(
+        self,
+        truth: NDArray[np.float64],
+        observations: SurveyOutcome,
+        config: dict[str, Any],
+    ) -> dict[str, float]:
+        """Score one survey; averaging errors gives mean absolute error.
+
+        Args:
+            truth: Target antibody-status prevalences, ordered by group.
+            observations: Simulated group estimates and field-work counts.
+            config: Survey configuration used for financial cost.
+
+        Returns:
+            Cost and overall/subgroup absolute errors in percentage points.
+        """
+        overall = float(np.dot(observations.prevalence - truth, GROUP_WEIGHTS))
+        return {
+            "cost_usd": sum(self.costs.components(config).values()),
+            "overall_error_pp": 100 * abs(overall),
+            "underserved_error_pp": 100 * abs(observations.prevalence[1] - truth[1]),
+        }
+
+
+def survey_factors() -> list[Factor]:
+    """Return the factors for the 18-design demonstration.
+
+    Returns:
+        Participant count, community count and allocation factors.
+    """
+    return [
+        Factor("participants", FactorType.DISCRETE, levels=[300, 600, 1200]),
+        Factor("communities", FactorType.DISCRETE, levels=[10, 20, 40]),
+        Factor(
+            "allocation", FactorType.CATEGORICAL, levels=["proportional", "oversample"]
+        ),
+    ]
+
+
+def survey_observables() -> list[Observable]:
+    """Return the three objectives, all minimized.
+
+    Returns:
+        Financial cost, overall MAE and underserved-group MAE observables.
+    """
+    return [Observable(name, Direction.MINIMIZE) for name in PRIORITIES["Cost first"]]
+
+
+def preference_policy() -> PreferencePolicy:
+    """Return three hypothetical priorities with fixed reference anchors.
+
+    Returns:
+        An explicit policy; anchors are scaling choices, not eligibility limits.
+    """
+    return PreferencePolicy(
+        weights=list(PRIORITIES.values()),
+        normalization="reference",
+        reference_bounds={
+            "cost_usd": (0, 70_000),
+            "overall_error_pp": (0, 4),
+            "underserved_error_pp": (0, 10),
+        },
+    )
+
+
+def plot_population(world: SurveyWorld) -> Figure:
+    """Draw the population assumptions, rather than estimated outcomes.
+
+    Args:
+        world: Hypothetical group prevalences.
+
+    Returns:
+        A two-panel population-share and antibody-status prevalence figure.
+    """
+    fig, axes = plt.subplots(1, 2, figsize=(11, 4.2), layout="constrained")
+    for ax, values, title in zip(
+        axes,
+        (GROUP_WEIGHTS, world.prevalence),
+        ("Population composition", "Assumed antibody-status prevalence"),
+        strict=True,
+    ):
+        bars = ax.bar(GROUP_NAMES, 100 * np.array(values), color=list(COLORS.values()))
+        ax.bar_label(bars, fmt="%.0f%%", padding=4, fontsize=13)
+        ax.set(ylim=(0, 110), ylabel="Percent", title=title)
+        ax.spines[["top", "right"]].set_visible(False)
+    fig.suptitle("A fictional population: assumptions, not measured data", fontsize=16)
+    return fig
+
+
+def _selected_indices(report: PreferenceSweep) -> NDArray[np.intp]:
+    return np.flatnonzero(np.any(report.ranks == 1, axis=0))
+
+
+def _mc_errors(results: ResultsTable, name: str) -> NDArray[np.float64]:
+    return np.array([
+        meta["score_std"][name] / np.sqrt(meta["n_reps"] - 1)
+        for meta in results.metadata
+    ])
+
+
+def plot_tradeoffs(report: PreferenceSweep, budget: float) -> Figure:
+    """Project the three-objective feasible Pareto set into two panels.
+
+    Args:
+        report: Preference report, including budget feasibility and Pareto flags.
+        budget: Financial ceiling in illustrative USD.
+
+    Returns:
+        Cost versus overall/subgroup MAE, with approximate Monte Carlo bars.
+    """
+    fig, axes = plt.subplots(1, 2, figsize=(12, 5.2), layout="constrained")
+    table = report.summary
+    costs = table.scores[:, 0] / 1000
+    selected = _selected_indices(report)
+    for ax, name, title in zip(
+        axes,
+        ("overall_error_pp", "underserved_error_pp"),
+        ("Overall population", "Underserved group"),
+        strict=True,
+    ):
+        errors = table.scores[:, table.observable_names.index(name)]
+        ax.errorbar(
+            costs,
+            errors,
+            yerr=2 * _mc_errors(table, name),
+            fmt="none",
+            ecolor="#bbbbbb",
+            capsize=2,
+            zorder=1,
+        )
+        for i, config in enumerate(table.configs):
+            ax.scatter(
+                costs[i],
+                errors[i],
+                s=75,
+                marker="o" if config["allocation"] == "proportional" else "D",
+                color=COLORS[config["allocation"]] if report.feasible[i] else "#cccccc",
+                edgecolors="black" if report.pareto[i] else "none",
+                linewidths=1.4,
+                zorder=3,
+            )
+        for offset, i in enumerate(selected):
+            ax.annotate(
+                f"D{i + 1:02d}",
+                (costs[i], errors[i]),
+                xytext=(5, 10 + 10 * (offset % 2)),
+                textcoords="offset points",
+                fontsize=10,
+                fontweight="bold",
+            )
+        ax.axvline(budget / 1000, color="#555555", linestyle="--")
+        ax.set(
+            xlabel="Financial cost (illustrative USD thousands)",
+            ylabel="Mean absolute error (percentage points)",
+            title=title,
+        )
+        ax.spines[["top", "right"]].set_visible(False)
+    handles = [
+        Line2D(
+            [],
+            [],
+            marker="o",
+            linestyle="",
+            color=COLORS["proportional"],
+            label="Proportional allocation",
+        ),
+        Line2D(
+            [],
+            [],
+            marker="D",
+            linestyle="",
+            color=COLORS["oversample"],
+            label="Oversample underserved group",
+        ),
+        Line2D(
+            [],
+            [],
+            marker="o",
+            linestyle="",
+            color="white",
+            markeredgecolor="black",
+            label="Feasible Pareto design (all 3 objectives)",
+        ),
+        Line2D([], [], marker="o", linestyle="", color="#cccccc", label="Over budget"),
+    ]
+    fig.legend(handles=handles, loc="outside lower center", ncol=2, fontsize=10)
+    fig.suptitle("Cost, overall accuracy and subgroup accuracy compete", fontsize=16)
+    return fig
+
+
+def plot_priorities(report: PreferenceSweep) -> Figure:
+    """Show ranks among feasible Pareto candidates under three priorities.
+
+    Args:
+        report: Results of the three named preference scenarios.
+
+    Returns:
+        A rank heatmap; highlighted cells identify winners, including ties.
+    """
+    indices = np.flatnonzero(report.pareto)
+    indices = indices[np.argsort(report.summary.scores[indices, 0])]
+    ranks = report.ranks[:, indices].T
+    fig, ax = plt.subplots(
+        figsize=(10, max(3.5, 0.42 * len(indices) + 1.4)), layout="constrained"
+    )
+    if not len(indices):
+        ax.text(0.5, 0.5, "No design meets the budget", ha="center", va="center")
+        ax.axis("off")
+        return fig
+    ax.imshow(
+        ranks, cmap="Blues_r", vmin=1, vmax=max(2, float(ranks.max())), aspect="auto"
+    )
+    labels = []
+    for i in indices:
+        cfg = report.summary.configs[i]
+        strategy = "P" if cfg["allocation"] == "proportional" else "O"
+        labels.append(
+            f"D{i + 1:02d} · {cfg['participants']} people / "
+            f"{cfg['communities']} communities / {strategy}"
+        )
+    ax.set_yticks(np.arange(len(indices)), labels, fontsize=10)
+    ax.set_xticks(np.arange(len(PRIORITIES)), list(PRIORITIES), fontsize=11)
+    for row, column in np.ndindex(ranks.shape):
+        rank = ranks[row, column]
+        ax.text(
+            column,
+            row,
+            f"{rank:.0f}",
+            ha="center",
+            va="center",
+            color="white" if rank < (ranks.max() + 1) / 2 else "black",
+            fontweight="bold" if rank == 1 else "normal",
+        )
+    ax.set_title(
+        "Same evidence, different priorities\n"
+        "Rank 1 wins; ranks include all feasible designs",
+        fontsize=15,
+        pad=16,
+    )
+    ax.set_xlabel("Allocation: P = proportional; O = oversample", labelpad=12)
+    return fig
+
+
+def plot_cost_components(report: PreferenceSweep, costs: SurveyCosts) -> Figure:
+    """Show financial cost components for the preference winners.
+
+    Args:
+        report: Preference report identifying selected candidates.
+        costs: Illustrative financial cost model.
+
+    Returns:
+        A stacked cost figure, or a message if no design is feasible.
+    """
+    indices = _selected_indices(report)
+    fig, ax = plt.subplots(figsize=(9, 4.5), layout="constrained")
+    if not len(indices):
+        ax.text(0.5, 0.5, "No design meets the budget", ha="center", va="center")
+        ax.axis("off")
+        return fig
+    components = [costs.components(report.summary.configs[i]) for i in indices]
+    bottom = np.zeros(len(indices))
+    for name, color in zip(
+        components[0], ("#666666", "#009E73", "#E69F00"), strict=True
+    ):
+        values = np.array([row[name] for row in components]) / 1000
+        ax.bar(
+            [f"D{i + 1:02d}" for i in indices],
+            values,
+            bottom=bottom,
+            label=name,
+            color=color,
+        )
+        bottom += values
+    ax.set(
+        ylabel="Financial cost (illustrative USD thousands)",
+        title="Why do the selected designs cost different amounts?",
+    )
+    ax.legend(loc="upper left", frameon=False)
+    ax.spines[["top", "right"]].set_visible(False)
+    return fig
+
+
+def main() -> None:
+    """Run the notebook's study and export documentation figures.
+
+    All 18 designs receive both screening and independent refinement.
+    """
+    started = perf_counter()
+    factors = survey_factors()
+    observables = survey_observables()
+    grid = build_grid(factors, method="full")
+    world = SurveyWorld(seed=2026)
+    study = Study(
+        world=world,
+        scorer=SurveyScorer(),
+        observables=observables,
+        factors=factors,
+        phases=[
+            Phase("screen", grid=grid, n_reps=100),
+            Phase("refine", grid=grid, n_reps=1000, world=replace(world, seed=2027)),
+        ],
+    )
+    study.run()
+    report = preference_sweep(
+        study.results("refine"),
+        observables,
+        policy=preference_policy(),
+        constraints=[Constraint("financial_budget", "cost_usd", "<=", BUDGET)],
+    )
+    asset_dir = Path(__file__).resolve().parents[1] / "docs" / "assets"
+    for name, figure in {
+        "population": plot_population(world),
+        "tradeoffs": plot_tradeoffs(report, BUDGET),
+        "priorities": plot_priorities(report),
+        "costs": plot_cost_components(report, SurveyCosts()),
+    }.items():
+        figure.savefig(asset_dir / f"serosurvey_{name}.png", dpi=160)
+        plt.close(figure)
+    print(
+        f"18 designs, 19,800 evaluations, completed in {perf_counter() - started:.2f}s"
+    )
+    for name, ranks in zip(PRIORITIES, report.ranks, strict=True):
+        selected = np.flatnonzero(ranks == 1)
+        print(f"{name}: " + ", ".join(f"D{i + 1:02d}" for i in selected))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/mkdocs.yml b/mkdocs.yml
index c8ebb02..b1a47c7 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -65,6 +65,7 @@ nav:
       - Reactor Design (CSTR): guide/cstr.md
       - Hyperparameter Sweep (sklearn): guide/sklearn.md
       - Assay Costs and Annotations: guide/assay.md
+      - Serosurvey Design (IVAC Notebook): guide/serosurvey.md
       - Monitoring Station Design: guide/monitoring.md
       - Bayesian Model Criticism: guide/bayesian.md
   - API Reference:
diff --git a/pyproject.toml b/pyproject.toml
index 36ebe0e..b5ebefe 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -86,6 +86,12 @@ examples = [
   "scikit-learn>=1.3",
   "trade-study[all]",
 ]
+notebook = [
+  "jupyterlab>=4.0",
+  "nbconvert>=7.0",
+  "matplotlib>=3.7",
+  "trade-study[pareto,viz,dataframe]",
+]
 test = [
   "pytest>=8.1",
   "pytest-cov>=6.0",
diff --git a/tests/test_serosurvey_example.py b/tests/test_serosurvey_example.py
new file mode 100644
index 0000000..89fb921
--- /dev/null
+++ b/tests/test_serosurvey_example.py
@@ -0,0 +1,142 @@
+"""Statistical and decision invariants for the synthetic serosurvey demo."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pytest
+
+from examples.serosurvey_study import (
+    GROUP_WEIGHTS,
+    SurveyCosts,
+    SurveyOutcome,
+    SurveyScorer,
+    SurveyWorld,
+    allocation_counts,
+    plot_priorities,
+    preference_policy,
+    survey_factors,
+    survey_observables,
+)
+from trade_study import Constraint, build_grid, preference_sweep, run_grid
+
+
+def test_allocations_preserve_exact_totals() -> None:
+    grid = build_grid(survey_factors(), method="full")
+    assert len(grid) == 18
+    for config in grid:
+        people, communities = allocation_counts(config)
+        assert sum(people) == config["participants"]
+        assert sum(communities) == config["communities"]
+        expected_share = 0.2 if config["allocation"] == "proportional" else 0.5
+        assert people[1] / sum(people) == pytest.approx(expected_share)
+        assert communities[1] / sum(communities) == pytest.approx(expected_share)
+
+
+def test_score_uses_population_weights_when_oversampling() -> None:
+    config = {"participants": 300, "communities": 10, "allocation": "oversample"}
+    truth = np.array([0.9, 0.65])
+    outcome = SurveyOutcome(np.array([0.9, 0.55]), (150, 150), (5, 5))
+    scores = SurveyScorer().score(truth, outcome, config)
+    assert scores["overall_error_pp"] == pytest.approx(2.0)
+    assert scores["underserved_error_pp"] == pytest.approx(10.0)
+    assert scores["cost_usd"] == 18_250
+    assert np.dot(GROUP_WEIGHTS, truth) == pytest.approx(0.85)
+
+
+@pytest.mark.parametrize("correlation", [0.0, 0.3])
+def test_deterministic_population_has_zero_error(correlation: float) -> None:
+    world = SurveyWorld(prevalence=(1.0, 0.0), correlation=correlation)
+    config = {"participants": 300, "communities": 40, "allocation": "proportional"}
+    truth, outcome = world.generate(config, rep=7)
+    np.testing.assert_array_equal(outcome.prevalence, truth)
+    scores = SurveyScorer().score(truth, outcome, config)
+    assert scores["overall_error_pp"] == 0
+    assert scores["underserved_error_pp"] == 0
+
+
+def test_clustered_estimator_matches_known_mean_and_variance() -> None:
+    world = SurveyWorld(prevalence=(0.9, 0.65), correlation=0.08)
+    config = {"participants": 300, "communities": 40, "allocation": "proportional"}
+    estimates = np.array([
+        world.generate(config, rep=rep)[1].prevalence for rep in range(5000)
+    ])
+    # Unequal cluster sizes are intentional: 60 people / 8 communities
+    # requires four clusters of size 8 and four of size 7.
+    n, sum_squares, p, rho = 60, 4 * 8**2 + 4 * 7**2, 0.65, 0.08
+    variance = p * (1 - p) * ((1 - rho) / n + rho * sum_squares / n**2)
+    assert abs(estimates[:, 1].mean() - p) < 4 * np.sqrt(variance / len(estimates))
+    assert estimates[:, 1].var(ddof=1) == pytest.approx(variance, rel=0.12)
+
+
+def test_streams_replay_and_phase_seeds_are_distinct() -> None:
+    config = {"participants": 300, "communities": 10, "allocation": "proportional"}
+    first = SurveyWorld(seed=2026).generate(config, rep=3)[1].prevalence
+    replay = SurveyWorld(seed=2026).generate(config, rep=3)[1].prevalence
+    new_phase = SurveyWorld(seed=2027).generate(config, rep=3)[1].prevalence
+    new_replicate = SurveyWorld(seed=2026).generate(config, rep=4)[1].prevalence
+    np.testing.assert_array_equal(first, replay)
+    assert not np.array_equal(first, new_phase)
+    assert not np.array_equal(first, new_replicate)
+
+
+@pytest.mark.parametrize(
+    "settings",
+    [
+        {"prevalence": (0.9, np.nan)},
+        {"prevalence": (0.9, 1.2)},
+        {"correlation": -0.1},
+        {"correlation": 1.0},
+        {"seed": -1},
+    ],
+)
+def test_invalid_model_assumptions_are_rejected(settings: dict[str, Any]) -> None:
+    with pytest.raises(ValueError, match="Require"):
+        SurveyWorld(**settings)
+
+
+@pytest.mark.parametrize(
+    "config",
+    [
+        {"participants": 300.0, "communities": 10, "allocation": "proportional"},
+        {"participants": 300, "communities": 40, "allocation": "unknown"},
+        {"participants": 30, "communities": 40, "allocation": "proportional"},
+        {"participants": 300, "communities": 11, "allocation": "proportional"},
+    ],
+)
+def test_invalid_designs_are_rejected(config: dict[str, Any]) -> None:
+    with pytest.raises(ValueError, match=r"integers|Allocation|Use integer"):
+        SurveyWorld().generate(config)
+
+
+def test_budget_change_uses_existing_results_and_reports_no_choice() -> None:
+    grid = build_grid(survey_factors(), method="full")[:2]
+    observables = survey_observables()
+    raw = run_grid(SurveyWorld(), SurveyScorer(), grid, observables, n_reps=8)
+    original_scores = raw.scores.copy()
+    for budget, feasible in [(16_000, [True, False]), (1, [False, False])]:
+        report = preference_sweep(
+            raw,
+            observables,
+            policy=preference_policy(),
+            constraints=[Constraint("budget", "cost_usd", "<=", budget)],
+        )
+        assert report.feasible.tolist() == feasible
+        if budget == 1:
+            assert np.all(np.isnan(report.ranks))
+            assert not report.pareto.any()
+            figure = plot_priorities(report)
+            assert figure.axes[0].texts[0].get_text() == "No design meets the budget"
+            plt.close(figure)
+    np.testing.assert_array_equal(raw.scores, original_scores)
+
+
+def test_financial_costs_are_additive_and_nonnegative() -> None:
+    config = {"participants": 300, "communities": 10, "allocation": "proportional"}
+    components = SurveyCosts().components(config)
+    assert components == {"Setup": 3000, "Community visits": 3400, "Participants": 9600}
+    assert sum(components.values()) == 16_000
+    with pytest.raises(ValueError, match="finite and nonnegative"):
+        SurveyCosts(setup=-1)