spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T04:42:13.258Z","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T04:42:13.264Z","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f"}4{"parentUuid":null,"isSidechain":false,"promptId":"61b6de4b-9d29-4b28-ad1f-ee1837fef690","type":"user","message":{"role":"user","content":"Begin"},"uuid":"1c15ff07-407e-4a38-b8d7-9d7667ba70c4","timestamp":"2026-10-30T04:42:13.268Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","shapeHash":"a1cd04e6fff80a2d5aff51f927b5817a142afba0e4f59b9832d822fdceaa6b91","timestamp":"2026-10-30T04:42:13.281Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RC0145\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f"}12{"parentUuid":"83447399-51bc-4f3b-9229-c7de308caa14","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoryPizuKdot4bpTvN9","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2811,"cache_read_input_tokens":1504,"output_tokens":134,"output_tokens_details":{"thinking_tokens":19},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2811},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":419,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXory7M8Jt8rxcXns4T5","requestRef":"18428010-10af-4c87-9b9c-5800a5d7cad9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"08743be5-34e0-4ce7-8b73-d47ee82657ee","timestamp":"2026-10-30T04:42:15.394Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"08743be5-34e0-4ce7-8b73-d47ee82657ee","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoryPizuKdot4bpTvN9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01ESEX5ZkWXqe3VwbDGDWiFP","name":"Bash","input":{"command":"cat TASK.md; 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The estimands are the **balanced-panel means**: for each year, the simple (unweighted) mean of life expectancy over the countries that have a non-missing value in every year from 2000 to 2022 inclusive, each country counted once; the main quantities are the balanced means for 2000 and 2022 and their difference, together with the composition of the balanced set. A naive mean over whatever countries happen to be available in a year is not the estimand, but it is requested as a descriptive baseline for 2022.\n\n## Inputs\n- `data/life_expectancy_panel.csv` — observed data: life expectancy at birth, total (years), World Bank World Development Indicators indicator `SP.DYN.LE00.IN`, 20 countries, years 2000–2022. Columns `country_name` (string), `iso3` (ISO alpha-3 code), `year` (integer), `life_expectancy` (years). Source URL, retrieval date, license and SHA-256 are in `data/README.md`. The file was assembled from two extractions of the API response and may contain assembly errors; an empty cell means no value is recorded for that country-year.\n\n## Information-availability rules\n- Use only `data/life_expectancy_panel.csv`. Do not impute, interpolate or look up missing values: a country-year without a recorded value is missing.\n- A country is identified by its `iso3` code; each country contributes at most one observation per year.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-02\" |\n| `n_countries_total` | integer | countries | distinct countries (iso3) present in the file |\n| `n_countries_balanced` | integer | countries | countries with a recorded value in every year 2000–2022 |\n| `n_countries_incomplete` | integer | countries | distinct countries not in the balanced set |\n| `balanced_countries` | array of strings | — | iso3 codes of the balanced set, sorted |\n| `n_analysed` | integer | country-years | rows of the balanced analytic dataset (countries × years) |\n| `mean_2000_balanced` | number | years | balanced-panel mean life expectancy in 2000 |\n| `mean_2022_balanced` | number | years | balanced-panel mean life expectancy in 2022 |\n| `change_2000_2022_balanced` | number | years | `mean_2022_balanced − mean_2000_balanced` |\n| `mean_2022_all_available` | number | years | baseline: mean over all distinct countries with a recorded 2022 value, each counted once |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected and how it was handled (empty list if none) |\n| `n_input_rows` | object | rows | {\"life_expectancy_panel.csv\": number of data rows read (excluding the header)} |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — the balanced analytic dataset: one row per country-year of the balanced set, columns `iso3`, `country_name`, `year`, `life_expectancy`; exactly `n_analysed` rows, one row per (iso3, year) pair.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the number of rows read to `n_analysed` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`).\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/analytic.csv` and `out/exclusions.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 15 minutes of work.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\nlife_expectancy_panel.csv\nREADME.md","is_error":true,"tool_use_id":"toolu_01ESEX5ZkWXqe3VwbDGDWiFP"}]},"uuid":"4b0e53e2-2e40-4b65-8ebd-3fc1791544c2","timestamp":"2026-10-30T04:42:16.107Z","toolUseResult":"Error: Exit code 1\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Scientific question\nMeasure the change in life expectancy at birth between 2000 and 2022 across a fixed set of countries. The estimands are the **balanced-panel means**: for each year, the simple (unweighted) mean of life expectancy over the countries that have a non-missing value in every year from 2000 to 2022 inclusive, each country counted once; the main quantities are the balanced means for 2000 and 2022 and their difference, together with the composition of the balanced set. A naive mean over whatever countries happen to be available in a year is not the estimand, but it is requested as a descriptive baseline for 2022.\n\n## Inputs\n- `data/life_expectancy_panel.csv` — observed data: life expectancy at birth, total (years), World Bank World Development Indicators indicator `SP.DYN.LE00.IN`, 20 countries, years 2000–2022. Columns `country_name` (string), `iso3` (ISO alpha-3 code), `year` (integer), `life_expectancy` (years). Source URL, retrieval date, license and SHA-256 are in `data/README.md`. The file was assembled from two extractions of the API response and may contain assembly errors; an empty cell means no value is recorded for that country-year.\n\n## Information-availability rules\n- Use only `data/life_expectancy_panel.csv`. Do not impute, interpolate or look up missing values: a country-year without a recorded value is missing.\n- A country is identified by its `iso3` code; each country contributes at most one observation per year.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-02\" |\n| `n_countries_total` | integer | countries | distinct countries (iso3) present in the file |\n| `n_countries_balanced` | integer | countries | countries with a recorded value in every year 2000–2022 |\n| `n_countries_incomplete` | integer | countries | distinct countries not in the balanced set |\n| `balanced_countries` | array of strings | — | iso3 codes of the balanced set, sorted |\n| `n_analysed` | integer | country-years | rows of the balanced analytic dataset (countries × years) |\n| `mean_2000_balanced` | number | years | balanced-panel mean life expectancy in 2000 |\n| `mean_2022_balanced` | number | years | balanced-panel mean life expectancy in 2022 |\n| `change_2000_2022_balanced` | number | years | `mean_2022_balanced − mean_2000_balanced` |\n| `mean_2022_all_available` | number | years | baseline: mean over all distinct countries with a recorded 2022 value, each counted once |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected and how it was handled (empty list if none) |\n| `n_input_rows` | object | rows | {\"life_expectancy_panel.csv\": number of data rows read (excluding the header)} |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — the balanced analytic dataset: one row per country-year of the balanced set, columns `iso3`, `country_name`, `year`, `life_expectancy`; exactly `n_analysed` rows, one row per (iso3, year) pair.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the number of rows read to `n_analysed` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`).\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/analytic.csv` and `out/exclusions.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 15 minutes of work.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\nlife_expectancy_panel.csv\nREADME.md","sandboxed":false,"sourceToolAssistantUUID":"bb2e4ec9-fb8f-418a-9a85-5a3380b85e3b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","hash":"fd15a702c1f3c6b7bddfd58916ebd9e8efa1e9c9862123466c819039807940e7","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01ESEX5ZkWXqe3VwbDGDWiFP","name":"Bash","input":{"command":"cat TASK.md; 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SHA-256 of the raw API response: `ead88ee94d5b6cc6f8a055c9f2ec79f232c37faf8baca0ea122dbfc0550ff27e`\n- License: World Bank open data, Creative Commons Attribution 4.0 (CC BY-4.0).\n- Columns: `country_name` (string), `iso3` (ISO-3166 alpha-3 code as returned by the API), `year` (integer), `life_expectancy` (years, 3 decimals). Values were rounded to 3 decimals; nothing else was changed in the values.\n- The panel was assembled by the task author from two extractions of the API response (one earlier, narrower extraction and one later, wider one) and may contain assembly errors. An empty `life_expectancy` cell means no value was recorded for that country-year in the file.\n{\n  \"task_id\": \"F1-conf-02\",\n  \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/analytic.csv\", \"out/exclusions.csv\"],\n  \"results_keys\": {\n    \"task_id\": {\"type\": \"string\", \"nullable\": false},\n    \"n_countries_total\": {\"type\": \"integer\", \"nullable\": false},\n    \"n_countries_balanced\": {\"type\": \"integer\", \"nullable\": false},\n    \"n_countries_incomplete\": {\"type\": \"integer\", \"nullable\": false},\n    \"balanced_countries\": {\"type\": \"array\", \"nullable\": false},\n    \"n_analysed\": {\"type\": \"integer\", \"nullable\": false},\n    \"mean_2000_balanced\": {\"type\": \"number\", \"nullable\": false},\n    \"mean_2022_balanced\": {\"type\": \"number\", \"nullable\": false},\n    \"change_2000_2022_balanced\": {\"type\": \"number\", \"nullable\": false},\n    \"mean_2022_all_available\": {\"type\": \"number\", \"nullable\": false},\n    \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n    \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n    \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n    \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n    \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n  },\n  \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n  \"family_checks\": [\"exclusion_ledger_sums\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\"],\n  \"ci_pairs\": [],\n  \"positive_keys\": [],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-6\n}\nb3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac  data/life_expectancy_panel.csv\ne9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0  data/README.md\n     440 data/life_expectancy_panel.csv\n(439, 4) ['country_name', 'iso3', 'year', 'life_expectancy']\n  country_name iso3  year life_expectancy\n0    Australia  AUS  2000          79.234\n1    Australia  AUS  2001          79.634\n2    Australia  AUS  2002          79.937\n3    Australia  AUS  2003          80.239\n4    Australia  AUS  2004          80.490\niso3\nTUR    46\nAUS    23\nAUT    23\nGBR    23\nCHE    23\nSWE    23\nESP    23\nNLD    23\nKOR    23\nJPN    23\nITA    23\nDEU    23\nFRA    23\nCAN    23\nBEL    23\nUSA    23\nIND    12\nNGA    12\nETH    12\nBGD    12\nName: count, dtype: int64\niso3\nAUS          [Australia]\nAUT            [Austria]\nBEL            [Belgium]\nBGD         [Bangladesh]\nCAN             [Canada]\nCHE        [Switzerland]\nDEU            [Germany]\nESP              [Spain]\nETH           [Ethiopia]\nFRA             [France]\nGBR     [United Kingdom]\nIND              [India]\nITA              [Italy]\nJPN              [Japan]\nKOR        [Korea, Rep.]\nNGA            [Nigeria]\nNLD        [Netherlands]\nSWE             [Sweden]\nTUR    [Turkey, Turkiye]\nUSA      [United States]\nName: country_name, dtype: object\nyear\n2000    17\n2001    17\n2002    17\n2003    17\n2004    17\n2005    17\n2006    17\n2007    17\n2008    17\n2009    17\n2010    17\n2011    21\n2012    21\n2013    21\n2014    21\n2015    21\n2016    21\n2017    21\n2018    21\n2019    21\n2020    21\n2021    21\n2022    21\nName: count, dtype: int64\n    country_name iso3  year life_expectancy\n347       Turkey  TUR  2000          71.931\n370      Turkiye  TUR  2000          71.931\n348       Turkey  TUR  2001          72.271\n371      Turkiye  TUR  2001          72.271\n349       Turkey  TUR  2002          72.605\n372      Turkiye  TUR  2002          72.605\n350       Turkey  TUR  2003          72.906\n373      Turkiye  TUR  2003          72.906\n351       Turkey  TUR  2004          73.223\n374      Turkiye  TUR  2004          73.223\n352       Turkey  TUR  2005          73.510\n375      Turkiye  TUR  2005          73.510\n353       Turkey  TUR  2006          73.804\n376      Turkiye  TUR  2006          73.804\n354       Turkey  TUR  2007          74.129\n377      Turkiye  TUR  2007          74.129\n355       Turkey  TUR  2008          74.380\n378      Turkiye  TUR  2008          74.380\n356       Turkey  TUR  2009          74.679\n379      Turkiye  TUR  2009          74.679\n357       Turkey  TUR  2010          74.964\n380      Turkiye  TUR  2010          74.964\n358       Turkey  TUR  2011          74.823\n381      Turkiye  TUR  2011          74.823\n359       Turkey  TUR  2012          75.552\n382      Turkiye  TUR  2012          75.552\n360       Turkey  TUR  2013          76.171\n383      Turkiye  TUR  2013          76.171\n361       Turkey  TUR  2014          76.452\n384      Turkiye  TUR  2014          76.452\n362       Turkey  TUR  2015          76.539\n385      Turkiye  TUR  2015          76.539\n363       Turkey  TUR  2016          76.595\n386      Turkiye  TUR  2016          76.595\n364       Turkey  TUR  2017          77.037\n387      Turkiye  TUR  2017          77.037\n365       Turkey  TUR  2018          77.454\n388      Turkiye  TUR  2018          77.454\n366       Turkey  TUR  2019          77.737\n389      Turkiye  TUR  2019          77.737\n367       Turkey  TUR  2020          76.525\n390      Turkiye  TUR  2020          76.525\n368       Turkey  TUR  2021          75.722\n391      Turkiye  TUR  2021          75.722\n369       Turkey  TUR  2022          77.591\n392      Turkiye  TUR  2022          77.591\n    country_name iso3  year life_expectancy\n331  Switzerland  CHE  2007                \ncount    438.000000\nmean      78.466521\nstd        6.073816\nmin       51.490000\n25%       77.738000\n50%       80.517500\n75%       81.803500\nmax       84.560000\nName: life_expectancy, dtype: float64\nEmpty DataFrame\nColumns: [country_name, iso3, year, life_expectancy]\nIndex: []\nEmpty DataFrame\nColumns: [country_name, iso3, year, life_expectancy]\nIndex: []","is_error":false}]},"uuid":"f7dabedd-159d-4ab1-8ea5-0642ea2c1fa1","timestamp":"2026-10-30T04:42:20.090Z","toolUseResult":{"stdout":"# Data provenance — F1-conf-02\n\n`life_expectancy_panel.csv` — observed data (not simulated): **Life expectancy at birth, total (years)**, indicator `SP.DYN.LE00.IN`, World Development Indicators (World Bank, source id 2, release last updated 2026-10-08), for 20 countries (Australia, Austria, Bangladesh, Belgium, Canada, Ethiopia, France, Germany, India, Italy, Japan, Korea Rep., Netherlands, Nigeria, Spain, Sweden, Switzerland, Türkiye, United Kingdom, United States), years 2000–2022.\n\n- Source URL (JSON API): `https://api.worldbank.org/v2/country/CAN;USA;GBR;FRA;DEU;ITA;ESP;JPN;KOR;AUS;SWE;NLD;CHE;BEL;AUT;TUR;IND;NGA;ETH;BGD/indicator/SP.DYN.LE00.IN?format=json&per_page=2000&date=2000:2022`\n- Retrieved: 2026-10-30T03:25Z. SHA-256 of the raw API response: `ead88ee94d5b6cc6f8a055c9f2ec79f232c37faf8baca0ea122dbfc0550ff27e`\n- License: World Bank open data, Creative Commons Attribution 4.0 (CC BY-4.0).\n- Columns: `country_name` (string), `iso3` (ISO-3166 alpha-3 code as returned by the API), `year` (integer), `life_expectancy` (years, 3 decimals). Values were rounded to 3 decimals; nothing else was changed in the values.\n- The panel was assembled by the task author from two extractions of the API response (one earlier, narrower extraction and one later, wider one) and may contain assembly errors. An empty `life_expectancy` cell means no value was recorded for that country-year in the file.\n{\n  \"task_id\": \"F1-conf-02\",\n  \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/analytic.csv\", \"out/exclusions.csv\"],\n  \"results_keys\": {\n    \"task_id\": {\"type\": \"string\", \"nullable\": false},\n    \"n_countries_total\": {\"type\": \"integer\", \"nullable\": false},\n    \"n_countries_balanced\": {\"type\": \"integer\", \"nullable\": false},\n    \"n_countries_incomplete\": {\"type\": \"integer\", \"nullable\": false},\n    \"balanced_countries\": {\"type\": \"array\", \"nullable\": false},\n    \"n_analysed\": {\"type\": \"integer\", \"nullable\": false},\n    \"mean_2000_balanced\": {\"type\": \"number\", \"nullable\": false},\n    \"mean_2022_balanced\": {\"type\": \"number\", \"nullable\": false},\n    \"change_2000_2022_balanced\": {\"type\": \"number\", \"nullable\": false},\n    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data/README.md\n     440 data/life_expectancy_panel.csv\n(439, 4) ['country_name', 'iso3', 'year', 'life_expectancy']\n  country_name iso3  year life_expectancy\n0    Australia  AUS  2000          79.234\n1    Australia  AUS  2001          79.634\n2    Australia  AUS  2002          79.937\n3    Australia  AUS  2003          80.239\n4    Australia  AUS  2004          80.490\niso3\nTUR    46\nAUS    23\nAUT    23\nGBR    23\nCHE    23\nSWE    23\nESP    23\nNLD    23\nKOR    23\nJPN    23\nITA    23\nDEU    23\nFRA    23\nCAN    23\nBEL    23\nUSA    23\nIND    12\nNGA    12\nETH    12\nBGD    12\nName: count, dtype: int64\niso3\nAUS          [Australia]\nAUT            [Austria]\nBEL            [Belgium]\nBGD         [Bangladesh]\nCAN             [Canada]\nCHE        [Switzerland]\nDEU            [Germany]\nESP              [Spain]\nETH           [Ethiopia]\nFRA             [France]\nGBR     [United Kingdom]\nIND              [India]\nITA              [Italy]\nJPN              [Japan]\nKOR        [Korea, Rep.]\nNGA            [Nigeria]\nNLD        [Netherlands]\nSWE             [Sweden]\nTUR    [Turkey, Turkiye]\nUSA      [United States]\nName: country_name, dtype: object\nyear\n2000    17\n2001    17\n2002    17\n2003    17\n2004    17\n2005    17\n2006    17\n2007    17\n2008    17\n2009    17\n2010    17\n2011    21\n2012    21\n2013    21\n2014    21\n2015    21\n2016    21\n2017    21\n2018    21\n2019    21\n2020    21\n2021    21\n2022    21\nName: count, dtype: int64\n    country_name iso3  year life_expectancy\n347       Turkey  TUR  2000          71.931\n370      Turkiye  TUR  2000          71.931\n348       Turkey  TUR  2001          72.271\n371      Turkiye  TUR  2001          72.271\n349       Turkey  TUR  2002          72.605\n372      Turkiye  TUR  2002          72.605\n350       Turkey  TUR  2003          72.906\n373      Turkiye  TUR  2003          72.906\n351       Turkey  TUR  2004          73.223\n374      Turkiye  TUR  2004          73.223\n352       Turkey  TUR  2005          73.510\n375      Turkiye  TUR  2005          73.510\n353       Turkey  TUR  2006          73.804\n376      Turkiye  TUR  2006          73.804\n354       Turkey  TUR  2007          74.129\n377      Turkiye  TUR  2007          74.129\n355       Turkey  TUR  2008          74.380\n378      Turkiye  TUR  2008          74.380\n356       Turkey  TUR  2009          74.679\n379      Turkiye  TUR  2009          74.679\n357       Turkey  TUR  2010          74.964\n380      Turkiye  TUR  2010          74.964\n358       Turkey  TUR  2011          74.823\n381      Turkiye  TUR  2011          74.823\n359       Turkey  TUR  2012          75.552\n382      Turkiye  TUR  2012          75.552\n360       Turkey  TUR  2013          76.171\n383      Turkiye  TUR  2013          76.171\n361       Turkey  TUR  2014          76.452\n384      Turkiye  TUR  2014          76.452\n362       Turkey  TUR  2015          76.539\n385      Turkiye  TUR  2015          76.539\n363       Turkey  TUR  2016          76.595\n386      Turkiye  TUR  2016          76.595\n364       Turkey  TUR  2017          77.037\n387      Turkiye  TUR  2017          77.037\n365       Turkey  TUR  2018          77.454\n388      Turkiye  TUR  2018          77.454\n366       Turkey  TUR  2019          77.737\n389      Turkiye  TUR  2019          77.737\n367       Turkey  TUR  2020          76.525\n390      Turkiye  TUR  2020          76.525\n368       Turkey  TUR  2021          75.722\n391      Turkiye  TUR  2021          75.722\n369       Turkey  TUR  2022          77.591\n392      Turkiye  TUR  2022          77.591\n    country_name iso3  year life_expectancy\n331  Switzerland  CHE  2007                \ncount    438.000000\nmean      78.466521\nstd        6.073816\nmin       51.490000\n25%       77.738000\n50%       80.517500\n75%       81.803500\nmax       84.560000\nName: life_expectancy, dtype: float64\nEmpty DataFrame\nColumns: [country_name, iso3, year, life_expectancy]\nIndex: []\nEmpty DataFrame\nColumns: [country_name, iso3, year, life_expectancy]\nIndex: []","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e0344427-4425-4352-afa4-1db75e099c2f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","hash":"6f8222eaecaa49d09d03b314e87f7453b3c8a71469d4edcc0c9e7dc93c639cf8","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_0181EBxfrt9LpuDCKjusaV5s","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json | head -80; shasum -a 256 data/*; wc -l data/*.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/life_expectancy_panel.csv',dtype=str,keep_default_na=False)\nprint(d.shape, d.columns.tolist())\nprint(d.head())\nprint(d.iso3.value_counts())\nprint(d.groupby('iso3').country_name.unique())\nprint(d.year.value_counts().sort_index())\nprint(d[d.duplicated(['iso3','year'],keep=False)].sort_values(['iso3','year']))\nprint(d[d.life_expectancy==''])\nle=pd.to_numeric(d.life_expectancy,errors='coerce'); print(le.describe()); print(d[le.isna()&(d.life_expectancy!='')])\nprint(d[(le<30)|(le>90)])\n\"","description":"Inspect data and 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toolu_0125ykPvnB1z58p3YoDXeUtk","name":"Bash","input":{"command":"~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/life_expectancy_panel.csv')\nprint(d.dtypes); print(d.iso3.str.len().value_counts()); print(sorted(d.year.unique())[:3], d.year.max())\nd=d.drop_duplicates(['iso3','year']).sort_values(['iso3','year'])\nd['dif']=d.groupby('iso3').life_expectancy.diff()\nprint(d[d.dif.abs()>1.5])\nprint(d[d.year<2012].groupby('iso3').size())\n\"; ls ~/wp29/gates/","description":"Further anomaly checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3495,"cache_read_input_tokens":6114,"output_tokens":652,"output_tokens_details":{"thinking_tokens":360},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":3495},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXosQrfYAHCR22VerBPu","requestRef":"c5bbfa0a-6c4b-4d55-b3d2-56ecd1636250","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ffaa9e50-5e0a-402a-81f0-8cfe6e452298","timestamp":"2026-10-30T04:42:26.955Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"ffaa9e50-5e0a-402a-81f0-8cfe6e452298","isSidechain":false,"promptId":"61b6de4b-9d29-4b28-ad1f-ee1837fef690","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0125ykPvnB1z58p3YoDXeUtk","type":"tool_result","content":"country_name        object\niso3                object\nyear                 int64\nlife_expectancy    float64\ndtype: object\niso3\n3    439\nName: count, dtype: int64\n[np.int64(2000), np.int64(2001), np.int64(2002)] 2022\n      country_name iso3  year  life_expectancy    dif\n57      Bangladesh  BGD  2022           74.265  3.162\n298          Spain  ESP  2020           82.232 -1.600\n115       Ethiopia  ETH  2022           66.897  1.567\n172          India  IND  2021           67.282 -2.874\n173          India  IND  2022           71.698  4.416\n369         Turkey  TUR  2022           77.591  1.869\n436  United States  USA  2020           76.980 -1.808\niso3\nAUS    12\nAUT    12\nBEL    12\nBGD     1\nCAN    12\nCHE    12\nDEU    12\nESP    12\nETH     1\nFRA    12\nGBR    12\nIND     1\nITA    12\nJPN    12\nKOR    12\nNGA     1\nNLD    12\nSWE    12\nTUR    12\nUSA    12\ndtype: int64\npgar_gate.py","is_error":false}]},"uuid":"e9517add-4533-405c-b376-960e550ef2c1","timestamp":"2026-10-30T04:42:27.264Z","toolUseResult":{"stdout":"country_name        object\niso3                object\nyear                 int64\nlife_expectancy    float64\ndtype: object\niso3\n3    439\nName: count, dtype: int64\n[np.int64(2000), np.int64(2001), np.int64(2002)] 2022\n      country_name iso3  year  life_expectancy    dif\n57      Bangladesh  BGD  2022           74.265  3.162\n298          Spain  ESP  2020           82.232 -1.600\n115       Ethiopia  ETH  2022           66.897  1.567\n172          India  IND  2021           67.282 -2.874\n173          India  IND  2022           71.698  4.416\n369         Turkey  TUR  2022           77.591  1.869\n436  United States  USA  2020           76.980 -1.808\niso3\nAUS    12\nAUT    12\nBEL    12\nBGD     1\nCAN    12\nCHE    12\nDEU    12\nESP    12\nETH     1\nFRA    12\nGBR    12\nIND     1\nITA    12\nJPN    12\nKOR    12\nNGA     1\nNLD    12\nSWE    12\nTUR    12\nUSA    12\ndtype: int64\npgar_gate.py","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"ffaa9e50-5e0a-402a-81f0-8cfe6e452298","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","version":"2.1.314","gitBranch":"HEAD"}26{"type":"api-request-blob","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","hash":"50bdb75628e0f2a7d4344fa368208ec6cd3f52adabe29ace3da0fc7842129ef2","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_0125ykPvnB1z58p3YoDXeUtk","name":"Bash","input":{"command":"~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/life_expectancy_panel.csv')\nprint(d.dtypes); print(d.iso3.str.len().value_counts()); print(sorted(d.year.unique())[:3], d.year.max())\nd=d.drop_duplicates(['iso3','year']).sort_values(['iso3','year'])\nd['dif']=d.groupby('iso3').life_expectancy.diff()\nprint(d[d.dif.abs()>1.5])\nprint(d[d.year<2012].groupby('iso3').size())\n\"; ls ~/wp29/gates/","description":"Further anomaly checks"}}]}}27{"type":"api-request","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","id":"b8dceb0e-ca25-47bc-a020-5e0d30a27270","timestamp":"2026-10-30T04:42:27.268Z","version":"2.1.314","querySource":"sdk","shapeHash":"a1cd04e6fff80a2d5aff51f927b5817a142afba0e4f59b9832d822fdceaa6b91","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"c5bbfa0a-6c4b-4d55-b3d2-56ecd1636250","keep":6,"tail":["50bdb75628e0f2a7d4344fa368208ec6cd3f52adabe29ace3da0fc7842129ef2","8f85b22e2140e56e7244d25a7272dc79f19eed8081222921ee413ae77273dd69"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"9a4c1e74770dbc90f0862ad3685a65b1f382430dba09624d1726308d8dcf96c2"}28{"parentUuid":"e9517add-4533-405c-b376-960e550ef2c1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoswozvyg7SPfrdV8YZ","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1047,"cache_read_input_tokens":9609,"output_tokens":2788,"output_tokens_details":{"thinking_tokens":109},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1047},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":815,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXoswX8eYxZ8DpquVcDW","requestRef":"b8dceb0e-ca25-47bc-a020-5e0d30a27270","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"77206825-3e87-411f-9a00-d8082bef3997","timestamp":"2026-10-30T04:42:29.342Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"77206825-3e87-411f-9a00-d8082bef3997","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoswozvyg7SPfrdV8YZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HzbrXZNG1UkvhvaLyDHFPC","name":"Bash","input":{"command":"mkdir -p out/state && cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-02: balanced-panel mean life expectancy 2000-2022.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/life_expectancy_panel.csv. Deterministic; no randomness used.\"\"\"\nimport json, os\nimport pandas as pd\n\nIN = \"data/life_expectancy_panel.csv\"\nOUT = \"out\"\nYEARS = list(range(2000, 2023))\nos.makedirs(OUT, exist_ok=True)\n\nraw = pd.read_csv(IN, dtype={\"country_name\": str, \"iso3\": str}, keep_default_na=True)\nn_raw = len(raw)\nissues, excl = [], []\n\ndef step(name, rule, before, after, why):\n    excl.append(dict(step=name, rule=rule, n_before=before, n_after=after,\n                     n_removed=before - after, rationale=why))\n\ndf = raw.copy()\ndf[\"iso3\"] = df[\"iso3\"].str.strip()\ndf[\"year\"] = df[\"year\"].astype(int)\n\n# 1. Exact/conflicting duplicates of (iso3, year)\ndup = df[df.duplicated([\"iso3\", \"year\"], keep=False)]\nif len(dup):\n    g = dup.groupby([\"iso3\", \"year\"])[\"life_expectancy\"].nunique(dropna=False)\n    conflicting = g[g > 1]\n    if len(conflicting):\n        raise SystemExit(f\"Conflicting duplicate values: {conflicting}\")\n    for iso, sub in dup.groupby(\"iso3\"):\n        names = sorted(sub.country_name.unique())\n        issues.append({\n            \"issue\": f\"{iso}: {sub.duplicated(['iso3','year']).sum()} duplicated (iso3, year) rows \"\n                     f\"from the two extractions, under country names {names}; values identical in every duplicated year\",\n            \"handling\": \"Kept one row per (iso3, year) (first occurrence); country identified by iso3; \"\n                        \"removed rows logged in exclusions.csv\"})\nb = len(df)\ndf = df.drop_duplicates([\"iso3\", \"year\"], keep=\"first\")\nstep(\"1_dedupe\", \"drop duplicate (iso3, year) rows with identical values\", b, len(df),\n     \"each country contributes at most one observation per year (TASK rule); duplicates are assembly artefacts\")\n\n# harmonise country name per iso3 (use most recent name in file for display)\nname_map = {}\nfor iso, sub in raw.groupby(\"iso3\"):\n    names = list(dict.fromkeys(sub.country_name))\n    name_map[iso.strip()] = names[-1]\n    if len(names) > 1:\n        issues.append({\"issue\": f\"{iso}: multiple country_name spellings {names}\",\n                       \"handling\": f\"Identified by iso3; analytic.csv uses '{names[-1]}' (last-listed name)\"})\ndf[\"country_name\"] = df[\"iso3\"].map(name_map)\n\n# out-of-window years\nb = len(df)\ndf = df[df.year.between(2000, 2022)]\nstep(\"2_year_window\", \"keep years 2000-2022\", b, len(df), \"estimand window\")\n\n# 3. Missing values\nmiss = df[df.life_expectancy.isna()]\nfor _, r in miss.iterrows():\n    issues.append({\"issue\": f\"{r.iso3} {r.year}: empty life_expectancy cell\",\n                   \"handling\": \"Treated as missing (no imputation); country excluded from balanced set\"})\nb = len(df)\ndf = df[df.life_expectancy.notna()]\nstep(\"3_drop_missing\", \"drop rows with empty life_expectancy\", b, len(df),\n     \"missing values are not imputed (TASK rule)\")\n\n# rows absent for countries\nfor iso, sub in df.groupby(\"iso3\"):\n    present = set(sub.year)\n    absent = [y for y in YEARS if y not in present and not ((miss.iso3 == iso) & (miss.year == y)).any()]\n    if absent:\n        issues.append({\"issue\": f\"{iso}: no rows at all for years {min(absent)}-{max(absent)} ({len(absent)} years); \"\n                                f\"likely from the narrower earlier extraction\",\n                       \"handling\": \"Treated as missing (no imputation); country excluded from balanced set; \"\n                                   \"its 2022 value still enters the all-available baseline\"})\n\n# plausibility\nif not df.life_expectancy.between(20, 100).all():\n    raise SystemExit(\"implausible values\")\n\nall_countries = sorted(raw.iso3.str.strip().unique())\ncounts = df.groupby(\"iso3\").year.nunique()\nbalanced = sorted(counts[counts == len(YEARS)].index)\nincomplete = sorted(set(all_countries) - set(balanced))\n\nb = len(df)\nana = df[df.iso3.isin(balanced)].sort_values([\"iso3\", \"year\"])\nstep(\"4_balanced\", \"keep countries with a recorded value in every year 2000-2022\", b, len(ana),\n     f\"balanced-panel estimand; excluded incomplete countries {incomplete}\")\nana = ana[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]]\nassert len(ana) == len(balanced) * len(YEARS)\nassert not ana.duplicated([\"iso3\", \"year\"]).any()\nana.to_csv(f\"{OUT}/analytic.csv\", index=False)\n\nex = pd.DataFrame(excl)\nassert ex.n_before.iloc[0] == n_raw and ex.n_after.iloc[-1] == len(ana)\nex.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nm00 = float(ana[ana.year == 2000].life_expectancy.mean())\nm22 = float(ana[ana.year == 2022].life_expectancy.mean())\navail22 = df[df.year == 2022].drop_duplicates(\"iso3\")\nm22_all = float(avail22.life_expectancy.mean())\n\n# per-year balanced means (supplementary, not required)\nper_year = ana.groupby(\"year\").life_expectancy.mean().round(6)\nper_year.to_csv(f\"{OUT}/balanced_means_by_year.csv\")\n\nres = {\n    \"task_id\": \"F1-conf-02\",\n    \"n_countries_total\": len(all_countries),\n    \"n_countries_balanced\": len(balanced),\n    \"n_countries_incomplete\": len(incomplete),\n    \"balanced_countries\": balanced,\n    \"incomplete_countries\": incomplete,\n    \"n_analysed\": int(len(ana)),\n    \"mean_2000_balanced\": round(m00, 6),\n    \"mean_2022_balanced\": round(m22, 6),\n    \"change_2000_2022_balanced\": round(m22 - m00, 6),\n    \"mean_2022_all_available\": round(m22_all, 6),\n    \"n_countries_2022_available\": int(len(avail22)),\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"life_expectancy_panel.csv\": int(n_raw)},\n    \"random_seed\": None,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_countries_balanced\", \"n_analysed\", \"mean_2000_balanced\",\n                                      \"mean_2022_balanced\", \"change_2000_2022_balanced\",\n                                      \"mean_2022_all_available\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && cat out/results.json | head -60","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1047,"cache_read_input_tokens":9609,"output_tokens":2788,"output_tokens_details":{"thinking_tokens":109},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1047},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXoswX8eYxZ8DpquVcDW","requestRef":"b8dceb0e-ca25-47bc-a020-5e0d30a27270","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c1a32462-c0c8-4fa7-8c9f-c8a9e4bc04d6","timestamp":"2026-10-30T04:42:49.770Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","version":"2.1.314","gitBranch":"HEAD"}30{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"c1a32462-c0c8-4fa7-8c9f-c8a9e4bc04d6","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f"}31{"type":"atis-latch","atis":"","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f"}32{"parentUuid":"c1a32462-c0c8-4fa7-8c9f-c8a9e4bc04d6","isSidechain":false,"promptId":"61b6de4b-9d29-4b28-ad1f-ee1837fef690","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HzbrXZNG1UkvhvaLyDHFPC","type":"tool_result","content":"{\n \"n_countries_balanced\": 15,\n \"n_analysed\": 345,\n \"mean_2000_balanced\": 78.060267,\n \"mean_2022_balanced\": 81.553067,\n \"change_2000_2022_balanced\": 3.4928,\n \"mean_2022_all_available\": 78.692\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1_dedupe,\"drop duplicate (iso3, year) rows with identical values\",439,416,23,each country contributes at most one observation per year (TASK rule); duplicates are assembly artefacts\n2_year_window,keep years 2000-2022,416,416,0,estimand window\n3_drop_missing,drop rows with empty life_expectancy,416,415,1,missing values are not imputed (TASK rule)\n4_balanced,keep countries with a recorded value in every year 2000-2022,415,345,70,\"balanced-panel estimand; excluded incomplete countries ['BGD', 'CHE', 'ETH', 'IND', 'NGA']\"\n{\n  \"task_id\": \"F1-conf-02\",\n  \"n_countries_total\": 20,\n  \"n_countries_balanced\": 15,\n  \"n_countries_incomplete\": 5,\n  \"balanced_countries\": [\n    \"AUS\",\n    \"AUT\",\n    \"BEL\",\n    \"CAN\",\n    \"DEU\",\n    \"ESP\",\n    \"FRA\",\n    \"GBR\",\n    \"ITA\",\n    \"JPN\",\n    \"KOR\",\n    \"NLD\",\n    \"SWE\",\n    \"TUR\",\n    \"USA\"\n  ],\n  \"incomplete_countries\": [\n    \"BGD\",\n    \"CHE\",\n    \"ETH\",\n    \"IND\",\n    \"NGA\"\n  ],\n  \"n_analysed\": 345,\n  \"mean_2000_balanced\": 78.060267,\n  \"mean_2022_balanced\": 81.553067,\n  \"change_2000_2022_balanced\": 3.4928,\n  \"mean_2022_all_available\": 78.692,\n  \"n_countries_2022_available\": 20,\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"TUR: 23 duplicated (iso3, year) rows from the two extractions, under country names ['Turkey', 'Turkiye']; values identical in every duplicated year\",\n      \"handling\": \"Kept one row per (iso3, year) (first occurrence); country identified by iso3; removed rows logged in exclusions.csv\"\n    },\n    {\n      \"issue\": \"TUR: multiple country_name spellings ['Turkey', 'Turkiye']\",\n      \"handling\": \"Identified by iso3; analytic.csv uses 'Turkiye' (last-listed name)\"\n    },\n    {\n      \"issue\": \"CHE 2007: empty life_expectancy cell\",\n      \"handling\": \"Treated as missing (no imputation); country excluded from balanced set\"\n    },\n    {\n      \"issue\": \"BGD: no rows at all for years 2000-2010 (11 years); likely from the narrower earlier extraction\",\n      \"handling\": \"Treated as missing (no imputation); country excluded from balanced set; its 2022 value still enters the all-available baseline\"\n    },\n    {\n      \"issue\": \"ETH: no rows at all for years 2000-2010 (11 years); likely from the narrower earlier extraction\",\n      \"handling\": \"Treated as missing (no imputation); country excluded from balanced set; its 2022 value still enters the all-available baseline\"\n    },\n    {\n      \"issue\": \"IND: no rows at all for years 2000-2010 (11 years); likely from the narrower earlier extraction\",","is_error":false}]},"uuid":"57a034d6-21ba-4e2e-af4b-93e5004cd6c4","timestamp":"2026-10-30T04:42:50.068Z","toolUseResult":{"stdout":"{\n \"n_countries_balanced\": 15,\n \"n_analysed\": 345,\n \"mean_2000_balanced\": 78.060267,\n \"mean_2022_balanced\": 81.553067,\n \"change_2000_2022_balanced\": 3.4928,\n \"mean_2022_all_available\": 78.692\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1_dedupe,\"drop duplicate (iso3, year) rows with identical values\",439,416,23,each country contributes at most one observation per year (TASK rule); duplicates are assembly artefacts\n2_year_window,keep years 2000-2022,416,416,0,estimand window\n3_drop_missing,drop rows with empty life_expectancy,416,415,1,missing values are not imputed (TASK rule)\n4_balanced,keep countries with a recorded value in every year 2000-2022,415,345,70,\"balanced-panel estimand; excluded incomplete countries ['BGD', 'CHE', 'ETH', 'IND', 'NGA']\"\n{\n  \"task_id\": \"F1-conf-02\",\n  \"n_countries_total\": 20,\n  \"n_countries_balanced\": 15,\n  \"n_countries_incomplete\": 5,\n  \"balanced_countries\": [\n    \"AUS\",\n    \"AUT\",\n    \"BEL\",\n    \"CAN\",\n    \"DEU\",\n    \"ESP\",\n    \"FRA\",\n    \"GBR\",\n    \"ITA\",\n    \"JPN\",\n    \"KOR\",\n    \"NLD\",\n    \"SWE\",\n    \"TUR\",\n    \"USA\"\n  ],\n  \"incomplete_countries\": [\n    \"BGD\",\n    \"CHE\",\n    \"ETH\",\n    \"IND\",\n    \"NGA\"\n  ],\n  \"n_analysed\": 345,\n  \"mean_2000_balanced\": 78.060267,\n  \"mean_2022_balanced\": 81.553067,\n  \"change_2000_2022_balanced\": 3.4928,\n  \"mean_2022_all_available\": 78.692,\n  \"n_countries_2022_available\": 20,\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"TUR: 23 duplicated (iso3, year) rows from the two extractions, under country names ['Turkey', 'Turkiye']; values identical in every duplicated year\",\n      \"handling\": \"Kept one row per (iso3, year) (first occurrence); country identified by iso3; removed rows logged in exclusions.csv\"\n    },\n    {\n      \"issue\": \"TUR: multiple country_name spellings ['Turkey', 'Turkiye']\",\n      \"handling\": \"Identified by iso3; analytic.csv uses 'Turkiye' (last-listed name)\"\n    },\n    {\n      \"issue\": \"CHE 2007: empty life_expectancy cell\",\n      \"handling\": \"Treated as missing (no imputation); country excluded from balanced set\"\n    },\n    {\n      \"issue\": \"BGD: no rows at all for years 2000-2010 (11 years); likely from the narrower earlier extraction\",\n      \"handling\": \"Treated as missing (no imputation); country excluded from balanced set; its 2022 value still enters the all-available baseline\"\n    },\n    {\n      \"issue\": \"ETH: no rows at all for years 2000-2010 (11 years); likely from the narrower earlier extraction\",\n      \"handling\": \"Treated as missing (no imputation); country excluded from balanced set; its 2022 value still enters the all-available baseline\"\n    },\n    {\n      \"issue\": \"IND: no rows at all for years 2000-2010 (11 years); likely from the narrower earlier extraction\",","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"c1a32462-c0c8-4fa7-8c9f-c8a9e4bc04d6","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","version":"2.1.314","gitBranch":"HEAD"}33{"type":"api-request-blob","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","hash":"0ba1aad1ca304de097d740fc8e91235ec67e5445191e6bf4e862d8cc28ba7e1e","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSqgcKEAgTGAI4AUIIdGhpbmtpbmcSDBxb1URAuEGsRMrd5RoMV2krBw0N8WG+ci6LIjCH8ILBNNgaWZpamHOE7c6SAfB/HvhBqIDLj+/RJk83rnKnxp+kn1p+gI1ZpuNMUBIqxwbw7nUDlScSs9zkzqYsL/SXTgylEbdIjGrrSJxKgd8urLg4SjzjoST5ZGIhAUBW/8ysD6yzbl3e8I7PPYGC0dEkjKEMr12bSfwv7cO7i8Bg58sRT9emmVKMcVe2LySF6D5uGCqpKqqUFq8dogV+1E6tEZXy0UNbdiMksYqt9JklBbU26clhOSD3bF0GRbklh8Kw7/+cylTEQRpGZBmMVWgtgrVB6ddHsmuWBuiNoJ2AyMBoHXheSDch8SWFCZoHPL9Kw55XYp/mEC5pmez/4uh79ehle+5urI5p3Qu+lN4GzU+/NCcJngO2YgKrnb6BmAb8HugXEUA5Nrz/Y07lwLFoBimW4z9bxIEJ605oOZiJ0sqx4/YQ/LkUD9lnPppKesuxXxj0o4qe+ay9C+gSqdNBKTH+Mn7sKMQnOyA+UKhNJ+1lShO4WyeQmKp19i430OSUyIJ317xYgpAOs83NBLJQC4kPrP+8GHrmvjDttMYc0VFnzDXzKuvG1I4LI+IWq3V3Kh/MKp0+5Y6ZO4GkDvR2oxNcDJBPUTrtHq+2DhFMeZj3hCSBjsBx4VGXW/LIg9IQ4vuL2SFKiGM1DS7yr5HDGuLwurk3x5eYmIhirHJzqf+ok6/puCGfCf3tSPxCTVHzv8z38nb+cS5Y7JZrt/pUVZuZM6vu4JO/XYzKbNDSE2VyqDgCf/RjRNp4/CBVa9ZYhRTZ0tlBkY6ZdSZ00UkfUHD5jb4Aagu1Q9gP/a3raFbzQ3wQJHKuLUaj1K4GPDJTlPbhC83onkg/8XW4FhlPLpz7QE6nurAMmgA4TyUwr5i/KzqYz5s4rqF1e+P2yMst4UVZfvx4Anv0l+cULlzJEzIuwVP4cW67ASAFApAe/4fpOLWsjxBUYfYSD3URDK2DdqVB0sza4vijlE2D/sF9gfk01Y2C2mCbykJ4OMvGPQ81A6hsJszRd98rYvVAwAleaa2EzYFBSeVXXVZS0egh2CEFKpmAwn7jiYCF6inP1IMN2Rud9RoCzC0CefbAJ50SZQTsl0zfcJoizEFSWpu6rzG0SkPR4MakwrL7vxW7uWiqsvZ2c72s/uZdk3W8j5MoRQRKE5QIxG4ODXT4/Sflw+SiXKyCshgB"},{"type":"tool_use","id":"toolu_01HzbrXZNG1UkvhvaLyDHFPC","name":"Bash","input":{"command":"mkdir -p out/state && cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-02: balanced-panel mean life expectancy 2000-2022.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/life_expectancy_panel.csv. Deterministic; no randomness used.\"\"\"\nimport json, os\nimport pandas as pd\n\nIN = \"data/life_expectancy_panel.csv\"\nOUT = \"out\"\nYEARS = list(range(2000, 2023))\nos.makedirs(OUT, exist_ok=True)\n\nraw = pd.read_csv(IN, dtype={\"country_name\": str, \"iso3\": str}, keep_default_na=True)\nn_raw = len(raw)\nissues, excl = [], []\n\ndef step(name, rule, before, after, why):\n    excl.append(dict(step=name, rule=rule, n_before=before, n_after=after,\n                     n_removed=before - after, rationale=why))\n\ndf = raw.copy()\ndf[\"iso3\"] = df[\"iso3\"].str.strip()\ndf[\"year\"] = df[\"year\"].astype(int)\n\n# 1. Exact/conflicting duplicates of (iso3, year)\ndup = df[df.duplicated([\"iso3\", \"year\"], keep=False)]\nif len(dup):\n    g = dup.groupby([\"iso3\", \"year\"])[\"life_expectancy\"].nunique(dropna=False)\n    conflicting = g[g > 1]\n    if len(conflicting):\n        raise SystemExit(f\"Conflicting duplicate values: {conflicting}\")\n    for iso, sub in dup.groupby(\"iso3\"):\n        names = sorted(sub.country_name.unique())\n        issues.append({\n            \"issue\": f\"{iso}: {sub.duplicated(['iso3','year']).sum()} duplicated (iso3, year) rows \"\n                     f\"from the two extractions, under country names {names}; values identical in every duplicated year\",\n            \"handling\": \"Kept one row per (iso3, year) (first occurrence); country identified by iso3; \"\n                        \"removed rows logged in exclusions.csv\"})\nb = len(df)\ndf = df.drop_duplicates([\"iso3\", \"year\"], keep=\"first\")\nstep(\"1_dedupe\", \"drop duplicate (iso3, year) rows with identical values\", b, len(df),\n     \"each country contributes at most one observation per year (TASK rule); duplicates are assembly artefacts\")\n\n# harmonise country name per iso3 (use most recent name in file for display)\nname_map = {}\nfor iso, sub in raw.groupby(\"iso3\"):\n    names = list(dict.fromkeys(sub.country_name))\n    name_map[iso.strip()] = names[-1]\n    if len(names) > 1:\n        issues.append({\"issue\": f\"{iso}: multiple country_name spellings {names}\",\n                       \"handling\": f\"Identified by iso3; analytic.csv uses '{names[-1]}' (last-listed name)\"})\ndf[\"country_name\"] = df[\"iso3\"].map(name_map)\n\n# out-of-window years\nb = len(df)\ndf = df[df.year.between(2000, 2022)]\nstep(\"2_year_window\", \"keep years 2000-2022\", b, len(df), \"estimand window\")\n\n# 3. Missing values\nmiss = df[df.life_expectancy.isna()]\nfor _, r in miss.iterrows():\n    issues.append({\"issue\": f\"{r.iso3} {r.year}: empty life_expectancy cell\",\n                   \"handling\": \"Treated as missing (no imputation); country excluded from balanced set\"})\nb = len(df)\ndf = df[df.life_expectancy.notna()]\nstep(\"3_drop_missing\", \"drop rows with empty life_expectancy\", b, len(df),\n     \"missing values are not imputed (TASK rule)\")\n\n# rows absent for countries\nfor iso, sub in df.groupby(\"iso3\"):\n    present = set(sub.year)\n    absent = [y for y in YEARS if y not in present and not ((miss.iso3 == iso) & (miss.year == y)).any()]\n    if absent:\n        issues.append({\"issue\": f\"{iso}: no rows at all for years {min(absent)}-{max(absent)} ({len(absent)} years); \"\n                                f\"likely from the narrower earlier extraction\",\n                       \"handling\": \"Treated as missing (no imputation); country excluded from balanced set; \"\n                                   \"its 2022 value still enters the all-available baseline\"})\n\n# plausibility\nif not df.life_expectancy.between(20, 100).all():\n    raise SystemExit(\"implausible values\")\n\nall_countries = sorted(raw.iso3.str.strip().unique())\ncounts = df.groupby(\"iso3\").year.nunique()\nbalanced = sorted(counts[counts == len(YEARS)].index)\nincomplete = sorted(set(all_countries) - set(balanced))\n\nb = len(df)\nana = df[df.iso3.isin(balanced)].sort_values([\"iso3\", \"year\"])\nstep(\"4_balanced\", \"keep countries with a recorded value in every year 2000-2022\", b, len(ana),\n     f\"balanced-panel estimand; excluded incomplete countries {incomplete}\")\nana = ana[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]]\nassert len(ana) == len(balanced) * len(YEARS)\nassert not ana.duplicated([\"iso3\", \"year\"]).any()\nana.to_csv(f\"{OUT}/analytic.csv\", index=False)\n\nex = pd.DataFrame(excl)\nassert ex.n_before.iloc[0] == n_raw and ex.n_after.iloc[-1] == len(ana)\nex.to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nm00 = float(ana[ana.year == 2000].life_expectancy.mean())\nm22 = float(ana[ana.year == 2022].life_expectancy.mean())\navail22 = df[df.year == 2022].drop_duplicates(\"iso3\")\nm22_all = float(avail22.life_expectancy.mean())\n\n# per-year balanced means (supplementary, not required)\nper_year = ana.groupby(\"year\").life_expectancy.mean().round(6)\nper_year.to_csv(f\"{OUT}/balanced_means_by_year.csv\")\n\nres = {\n    \"task_id\": \"F1-conf-02\",\n    \"n_countries_total\": len(all_countries),\n    \"n_countries_balanced\": len(balanced),\n    \"n_countries_incomplete\": len(incomplete),\n    \"balanced_countries\": balanced,\n    \"incomplete_countries\": incomplete,\n    \"n_analysed\": int(len(ana)),\n    \"mean_2000_balanced\": round(m00, 6),\n    \"mean_2022_balanced\": round(m22, 6),\n    \"change_2000_2022_balanced\": round(m22 - m00, 6),\n    \"mean_2022_all_available\": round(m22_all, 6),\n    \"n_countries_2022_available\": int(len(avail22)),\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"life_expectancy_panel.csv\": int(n_raw)},\n    \"random_seed\": None,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_countries_balanced\", \"n_analysed\", \"mean_2000_balanced\",\n                                      \"mean_2022_balanced\", \"change_2000_2022_balanced\",\n                                      \"mean_2022_all_available\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && cat out/results.json | head -60","description":"Write and run analysis 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Now state files, report, claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":654,"cache_read_input_tokens":14666,"output_tokens":2065,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":654},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXov42NDod2zPimyYTrm","requestRef":"7a56aa89-3f46-46b6-b650-01b1f733d27d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"adfd2bbd-175c-4341-b70e-fe8ff5d67054","timestamp":"2026-10-30T04:42:57.259Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"adfd2bbd-175c-4341-b70e-fe8ff5d67054","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXov4KiNUHV316TbPeds","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013PTU2HiKhHYtmJNZFUoYrG","name":"Bash","input":{"command":"cd out/state\ncat > contract.md <<'EOF'\n# Contract — F1-conf-02\n**Question:** change in life expectancy at birth 2000→2022 across a fixed (balanced) set of countries.\n**Estimands:** for each year, unweighted mean of life expectancy over countries (iso3) with a recorded value in every year 2000–2022, each counted once; main: mean 2000, mean 2022, difference; composition of the balanced set. Meaningful assuming WDI values comparable across years and one obs per country-year. Unweighted country mean (not population-weighted).\n**Baseline:** mean of 2022 over all distinct countries with a recorded 2022 value (descriptive only).\n**Outputs:** out/results.json (task_id, n_countries_total, n_countries_balanced, n_countries_incomplete, balanced_countries, n_analysed, mean_2000_balanced, mean_2022_balanced, change_2000_2022_balanced, mean_2022_all_available, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed), out/analytic.csv, out/exclusions.csv, out/report.md, out/analysis.py, out/claims.json.\n**Info rules:** only the CSV; no imputation/interpolation/lookup; empty cell = missing; country = iso3; ≤1 obs per country-year.\n**Invalid if:** duplicates counted twice; conflicting duplicate values (would be unresolvable without external info); missing values imputed; no country complete (infeasible).\nEOF\ncat > data_audit.md <<'EOF'\n# Data audit\n- `data/life_expectancy_panel.csv` SHA-256 b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac; 439 data rows, 4 columns (country_name str, iso3 str, year int, life_expectancy float, years).\n- `data/README.md` SHA-256 e9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0 (provenance; the SHA in README refers to raw API response, not this CSV).\n- Years 2000–2022; 20 distinct iso3 (all 3-char); range of values 51.49–84.56 (plausible).\n- Anomalies: (1) TUR appears 46 times: 23 rows \"Turkey\" + 23 rows \"Turkiye\", identical values for every year → dedupe on (iso3, year). (2) CHE 2007 empty cell → missing. (3) BGD, ETH, IND, NGA only have rows 2011–2022 (rows absent 2000–2010) → incomplete. (4) Year-on-year jumps >1.5y checked: 2020–2022 COVID-era moves (ESP, USA, IND, TUR) and BGD/ETH 2022 — plausible/consistent with source; left unchanged (no external lookup allowed).\n- No info-availability violations: single input, used as is.\nEOF\ncat > checks.md <<'EOF'\n# Checks\n1. Rerun of analysis.py → results.json byte-identical (cmp): PASS.\n2. Independent recomputation via pivot_table/dropna: 15 balanced countries, same list, means 78.060267/81.553067, change 3.4928, baseline 78.692 over 20 countries: PASS.\n3. analytic.csv: 345 rows = 15×23, no duplicate (iso3, year), each country 23 rows: PASS.\n4. Exclusion ledger: 439→416→416→415→345, n_before−n_removed=n_after each row, chains: PASS (asserted in script).\n5. n_countries_balanced + n_countries_incomplete = 15+5 = 20 = n_countries_total: PASS.\n6. change = mean_2022 − mean_2000 (3.4928): PASS.\n7. Magnitude vs baseline: balanced 2022 mean (81.55) > all-available (78.69) as expected since excluded set contains 4 lower-LE countries (BGD, ETH, IND, NGA): PASS (plausible).\n8. Duplicate conflict check: TUR duplicates have identical values (script aborts otherwise): PASS.\nEOF\ncat > log.md <<'EOF'\n# Log\n- 2026-10-30 P0 contract written.\n- 2026-10-30 P1 audit: TUR duplicate names, CHE 2007 missing, 4 countries start 2011. Decision: dedupe TUR on iso3, no imputation.\n- 2026-10-30 P2/P3 analysis.py written; baseline mean_2022_all_available computed with same dedupe rule.\n- 2026-10-30 P4 verification checks pass; gate run.\nEOF\ncd ../..; cat > out/state/phases.json <<'EOF'\n{\n \"P0\": {\"status\": \"passed\", \"checks\": [\"contract.md written with estimands, outputs, rules\"], \"notes\": \"\"},\n \"P1\": {\"status\": \"passed\", \"checks\": [\"SHA-256, schema, duplicates, missing values audited in data_audit.md\", \"exclusions.csv chains 439->345\"], \"notes\": \"\"},\n \"P2\": {\"status\": \"passed\", \"checks\": [\"baseline mean_2022_all_available=78.692 over 20 distinct countries\"], \"notes\": \"\"},\n \"P3\": {\"status\": \"passed\", \"checks\": [\"analysis.py runs and writes results.json, analytic.csv, exclusions.csv; no randomness\"], \"notes\": \"\"},\n \"P4\": {\"status\": \"running\", \"checks\": [\"rerun byte-identical\", \"independent recomputation matches\", \"count identities hold (checks.md)\"], \"notes\": \"\"},\n \"P5\": {\"status\": \"pending\", \"checks\": [], \"notes\": \"\"}\n}\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | tail -20","description":"Write state files and run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":654,"cache_read_input_tokens":14666,"output_tokens":2065,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":654},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXov42NDod2zPimyYTrm","requestRef":"7a56aa89-3f46-46b6-b650-01b1f733d27d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4d6f31a2-261c-4f90-ab08-ffffd17e69f4","timestamp":"2026-10-30T04:43:14.405Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","version":"2.1.314","gitBranch":"HEAD"}42{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"4d6f31a2-261c-4f90-ab08-ffffd17e69f4","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f"}43{"type":"atis-latch","atis":"","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f"}44{"parentUuid":"4d6f31a2-261c-4f90-ab08-ffffd17e69f4","isSidechain":false,"promptId":"61b6de4b-9d29-4b28-ad1f-ee1837fef690","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013PTU2HiKhHYtmJNZFUoYrG","type":"tool_result","content":"  [ok] key:mean_2000_balanced: ok\n  [ok] key:mean_2022_balanced: ok\n  [ok] key:change_2000_2022_balanced: ok\n  [ok] key:mean_2022_all_available: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 4 steps consistent\n  [ok] reproducibility: regenerated in 0.3s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0145/out/state/gate_receipts/20261030T044314.750228Z.json","is_error":false}]},"uuid":"588c7f39-d7ad-432a-b61c-be9d7164e68b","timestamp":"2026-10-30T04:43:14.758Z","toolUseResult":{"stdout":"  [ok] key:mean_2000_balanced: ok\n  [ok] key:mean_2022_balanced: ok\n  [ok] key:change_2000_2022_balanced: ok\n  [ok] key:mean_2022_all_available: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 4 steps consistent\n  [ok] reproducibility: regenerated in 0.3s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0145/out/state/gate_receipts/20261030T044314.750228Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4d6f31a2-261c-4f90-ab08-ffffd17e69f4","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","version":"2.1.314","gitBranch":"HEAD"}45{"type":"api-request-blob","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","hash":"8269f099300e756545556a13ffc37635abdc40658a1728a3a0bcdd7486851f8d","message":{"role":"assistant","content":[{"type":"text","text":"Verified. Now state files, report, claims."},{"type":"tool_use","id":"toolu_013PTU2HiKhHYtmJNZFUoYrG","name":"Bash","input":{"command":"cd out/state\ncat > contract.md <<'EOF'\n# Contract — F1-conf-02\n**Question:** change in life expectancy at birth 2000→2022 across a fixed (balanced) set of countries.\n**Estimands:** for each year, unweighted mean of life expectancy over countries (iso3) with a recorded value in every year 2000–2022, each counted once; main: mean 2000, mean 2022, difference; composition of the balanced set. Meaningful assuming WDI values comparable across years and one obs per country-year. Unweighted country mean (not population-weighted).\n**Baseline:** mean of 2022 over all distinct countries with a recorded 2022 value (descriptive only).\n**Outputs:** out/results.json (task_id, n_countries_total, n_countries_balanced, n_countries_incomplete, balanced_countries, n_analysed, mean_2000_balanced, mean_2022_balanced, change_2000_2022_balanced, mean_2022_all_available, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed), out/analytic.csv, out/exclusions.csv, out/report.md, out/analysis.py, out/claims.json.\n**Info rules:** only the CSV; no imputation/interpolation/lookup; empty cell = missing; country = iso3; ≤1 obs per country-year.\n**Invalid if:** duplicates counted twice; conflicting duplicate values (would be unresolvable without external info); missing values imputed; no country complete (infeasible).\nEOF\ncat > data_audit.md <<'EOF'\n# Data audit\n- `data/life_expectancy_panel.csv` SHA-256 b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac; 439 data rows, 4 columns (country_name str, iso3 str, year int, life_expectancy float, years).\n- `data/README.md` SHA-256 e9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0 (provenance; the SHA in README refers to raw API response, not this CSV).\n- Years 2000–2022; 20 distinct iso3 (all 3-char); range of values 51.49–84.56 (plausible).\n- Anomalies: (1) TUR appears 46 times: 23 rows \"Turkey\" + 23 rows \"Turkiye\", identical values for every year → dedupe on (iso3, year). (2) CHE 2007 empty cell → missing. (3) BGD, ETH, IND, NGA only have rows 2011–2022 (rows absent 2000–2010) → incomplete. (4) Year-on-year jumps >1.5y checked: 2020–2022 COVID-era moves (ESP, USA, IND, TUR) and BGD/ETH 2022 — plausible/consistent with source; left unchanged (no external lookup allowed).\n- No info-availability violations: single input, used as is.\nEOF\ncat > checks.md <<'EOF'\n# Checks\n1. Rerun of analysis.py → results.json byte-identical (cmp): PASS.\n2. Independent recomputation via pivot_table/dropna: 15 balanced countries, same list, means 78.060267/81.553067, change 3.4928, baseline 78.692 over 20 countries: PASS.\n3. analytic.csv: 345 rows = 15×23, no duplicate (iso3, year), each country 23 rows: PASS.\n4. Exclusion ledger: 439→416→416→415→345, n_before−n_removed=n_after each row, chains: PASS (asserted in script).\n5. n_countries_balanced + n_countries_incomplete = 15+5 = 20 = n_countries_total: PASS.\n6. change = mean_2022 − mean_2000 (3.4928): PASS.\n7. Magnitude vs baseline: balanced 2022 mean (81.55) > all-available (78.69) as expected since excluded set contains 4 lower-LE countries (BGD, ETH, IND, NGA): PASS (plausible).\n8. Duplicate conflict check: TUR duplicates have identical values (script aborts otherwise): PASS.\nEOF\ncat > log.md <<'EOF'\n# Log\n- 2026-10-30 P0 contract written.\n- 2026-10-30 P1 audit: TUR duplicate names, CHE 2007 missing, 4 countries start 2011. Decision: dedupe TUR on iso3, no imputation.\n- 2026-10-30 P2/P3 analysis.py written; baseline mean_2022_all_available computed with same dedupe rule.\n- 2026-10-30 P4 verification checks pass; gate run.\nEOF\ncd ../..; cat > out/state/phases.json <<'EOF'\n{\n \"P0\": {\"status\": \"passed\", \"checks\": [\"contract.md written with estimands, outputs, rules\"], \"notes\": \"\"},\n \"P1\": {\"status\": \"passed\", \"checks\": [\"SHA-256, schema, duplicates, missing values audited in data_audit.md\", \"exclusions.csv chains 439->345\"], \"notes\": \"\"},\n \"P2\": {\"status\": \"passed\", \"checks\": [\"baseline mean_2022_all_available=78.692 over 20 distinct countries\"], \"notes\": \"\"},\n \"P3\": {\"status\": \"passed\", \"checks\": [\"analysis.py runs and writes results.json, analytic.csv, exclusions.csv; no randomness\"], \"notes\": \"\"},\n \"P4\": {\"status\": \"running\", \"checks\": [\"rerun byte-identical\", \"independent recomputation matches\", \"count identities hold (checks.md)\"], \"notes\": \"\"},\n \"P5\": {\"status\": \"pending\", \"checks\": [], \"notes\": \"\"}\n}\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | tail -20","description":"Write state files and run gate"}}]}}46{"type":"api-request","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","id":"71907b21-38cc-4ae6-a3ac-4d933ae2dc4f","timestamp":"2026-10-30T04:43:14.762Z","version":"2.1.314","querySource":"sdk","shapeHash":"a1cd04e6fff80a2d5aff51f927b5817a142afba0e4f59b9832d822fdceaa6b91","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"7a56aa89-3f46-46b6-b650-01b1f733d27d","keep":12,"tail":["8269f099300e756545556a13ffc37635abdc40658a1728a3a0bcdd7486851f8d","daa7c2e8be8e107a94e66ca7516188c8c01d586f0fa33452244711e57c4779e8"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"e1395c1c0e398d3eb6371a190ab2500c06542b9a6f37c796af46b55f4f200ff2"}47{"parentUuid":"588c7f39-d7ad-432a-b61c-be9d7164e68b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXowSrZwGYRdLzqTNwin","type":"message","role":"assistant","content":[{"type":"text","text":"Expected failures (report/claims not yet written). Writing them now."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2509,"cache_read_input_tokens":15320,"output_tokens":2330,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2509},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXowSZxYn6ZWJj4VYALV","requestRef":"71907b21-38cc-4ae6-a3ac-4d933ae2dc4f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"93b87e06-d5a8-4360-95f7-8a3167e4be62","timestamp":"2026-10-30T04:43:15.970Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","version":"2.1.314","gitBranch":"HEAD"}48{"parentUuid":"93b87e06-d5a8-4360-95f7-8a3167e4be62","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXowSrZwGYRdLzqTNwin","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XZG7U4GoSh6ZAK9VyqpaJS","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Question\nHow much did life expectancy at birth (WDI `SP.DYN.LE00.IN`) change from 2000 to 2022 in a fixed set of countries? The estimand is the unweighted mean over countries (by iso3) with a recorded value in every year 2000–2022, each counted once.\n\n## Data and exclusions\nInput: `data/life_expectancy_panel.csv`, 439 data rows (`n_input_rows`), 20 distinct countries (`n_countries_total`). Problems found (`data_issues`):\n- Türkiye (TUR) appears twice for every year, under the names \"Turkey\" and \"Turkiye\". The two copies have identical values. I kept one row per (iso3, year), which removed 23 rows.\n- Switzerland (CHE) has an empty cell for 2007. I treated it as missing and did not impute it, so CHE is not in the balanced set.\n- Bangladesh, Ethiopia, India and Nigeria (BGD, ETH, IND, NGA) have no rows for 2000–2010. They are not in the balanced set.\n\nExclusion ledger (`out/exclusions.csv`): 439 rows → 416 after removing duplicates → 416 after the year-window filter → 415 after dropping the missing cell → 345 after keeping only balanced countries (`n_analysed`). The balanced set has 15 countries (`n_countries_balanced`, `balanced_countries`: AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA). 5 countries are incomplete (`n_countries_incomplete`).\n\n## Method\nFor each year I took the simple mean over the 15 balanced countries (`out/analysis.py`; `out/balanced_means_by_year.csv` is a supplementary output). The change is the 2022 mean minus the 2000 mean. The baseline is the mean over all distinct countries with a recorded 2022 value (20 countries, `n_countries_2022_available`), with TUR counted once. The method uses no randomness (`random_seed` = null).\n\n## Results\n- Balanced mean in 2000: **78.060 years** (`mean_2000_balanced` = 78.060267).\n- Balanced mean in 2022: **81.553 years** (`mean_2022_balanced` = 81.553067).\n- Change from 2000 to 2022: **+3.493 years** (`change_2000_2022_balanced` = 3.4928).\n- Baseline, mean over all 20 countries with a 2022 value: 78.692 years (`mean_2022_all_available`). This is lower than the balanced 2022 mean because four of the five excluded countries (BGD, ETH, IND, NGA) have lower life expectancy. The baseline is not a measure of change over time.\n- Feasibility: `feasibility` = \"complete\".\n\n## Uncertainty\nThese are descriptive means over a fixed set of countries; they are not estimates from a sample. I therefore report no sampling standard errors or confidence intervals. The remaining uncertainty is measurement and revision error in the WDI figures, which I cannot quantify from this file. I checked the figures independently with a pivot/dropna recomputation, which matched exactly, and a re-run of the script gave byte-identical output (`out/state/checks.md`).\n\n## Limitations and open issues\n- The balanced set contains only high- and upper-middle-income countries (all OECD members). Switzerland is excluded because of a single missing year (2007), and four lower-income countries are excluded because they have no data before 2011. The result therefore does not represent all 20 countries.\n- The countries are equally weighted, not weighted by population.\n- I resolved the TUR duplicates only because both copies agree exactly. The script stops if a duplicate pair ever has conflicting values.\n- Year-on-year jumps of more than 1.5 years occur in 2020–2022 (e.g. ESP, USA, IND, TUR, BGD). I kept them unchanged, since the task allows no external lookup. None of the countries with such jumps in 2000 or 2022 affects the balanced estimand except through its own recorded values.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"15 of 20 countries have a recorded value in every year 2000-2022; 345 country-years analysed.\",\"results_keys\":[\"n_countries_balanced\",\"n_countries_total\",\"n_countries_incomplete\",\"balanced_countries\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Counts after collapsing duplicated TUR rows.\"},\n {\"id\":\"C2\",\"text\":\"Balanced-panel mean life expectancy rose from 78.060 years in 2000 to 81.553 years in 2022, a change of +3.493 years.\",\"results_keys\":[\"mean_2000_balanced\",\"mean_2022_balanced\",\"change_2000_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Unweighted; balanced set is high/upper-middle income only.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries is 78.692 years, lower than the balanced 2022 mean because lower-life-expectancy countries are excluded from the balanced set.\",\"results_keys\":[\"mean_2022_all_available\",\"mean_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Descriptive baseline only.\"},\n {\"id\":\"C4\",\"text\":\"Data issues: TUR duplicated under two names with identical values (deduplicated), CHE 2007 missing, BGD/ETH/IND/NGA lacking 2000-2010.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"No imputation applied.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json,glob\np=json.load(open('out/state/phases.json'))\nr=sorted(glob.glob('out/state/gate_receipts/*.json'))\np['P4']['status']='passed'; p['P4']['checks'].append(f'gate run at end of P4: {r[-1]} (only report/claims/phase-ledger failures, expected before P5)')\np['P5']['status']='passed'; p['P5']['checks']=['report.md with 6 sections citing keys','claims.json written','final gate run (see log)']\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v \"\\[ok\\]\"","description":"Write report, claims, update phases, run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2509,"cache_read_input_tokens":15320,"output_tokens":2330,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2509},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXowSZxYn6ZWJj4VYALV","requestRef":"71907b21-38cc-4ae6-a3ac-4d933ae2dc4f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"001d6595-aeb0-41af-9f0c-e4e17e55c5c2","timestamp":"2026-10-30T04:43:34.192Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","version":"2.1.314","gitBranch":"HEAD"}49{"parentUuid":"001d6595-aeb0-41af-9f0c-e4e17e55c5c2","isSidechain":false,"promptId":"61b6de4b-9d29-4b28-ad1f-ee1837fef690","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XZG7U4GoSh6ZAK9VyqpaJS","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 32 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0145/out/state/gate_receipts/20261030T044334.563855Z.json","is_error":false}]},"uuid":"99cb1f67-1b7d-4118-a8bf-e73004749ce9","timestamp":"2026-10-30T04:43:34.571Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 32 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0145/out/state/gate_receipts/20261030T044334.563855Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"001d6595-aeb0-41af-9f0c-e4e17e55c5c2","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0145","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","version":"2.1.314","gitBranch":"HEAD"}50{"type":"api-request-blob","sessionId":"8d187715-4efa-432b-a0f0-90b989c5452f","hash":"038945c191aecaf0e0cc233737618ab9db49298c82b88ec17f2c328086a44b8b","message":{"role":"assistant","content":[{"type":"text","text":"Expected failures (report/claims not yet written). Writing them now."},{"type":"tool_use","id":"toolu_01XZG7U4GoSh6ZAK9VyqpaJS","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Question\nHow much did life expectancy at birth (WDI `SP.DYN.LE00.IN`) change from 2000 to 2022 in a fixed set of countries? The estimand is the unweighted mean over countries (by iso3) with a recorded value in every year 2000–2022, each counted once.\n\n## Data and exclusions\nInput: `data/life_expectancy_panel.csv`, 439 data rows (`n_input_rows`), 20 distinct countries (`n_countries_total`). Problems found (`data_issues`):\n- Türkiye (TUR) appears twice for every year, under the names \"Turkey\" and \"Turkiye\". The two copies have identical values. I kept one row per (iso3, year), which removed 23 rows.\n- Switzerland (CHE) has an empty cell for 2007. I treated it as missing and did not impute it, so CHE is not in the balanced set.\n- Bangladesh, Ethiopia, India and Nigeria (BGD, ETH, IND, NGA) have no rows for 2000–2010. They are not in the balanced set.\n\nExclusion ledger (`out/exclusions.csv`): 439 rows → 416 after removing duplicates → 416 after the year-window filter → 415 after dropping the missing cell → 345 after keeping only balanced countries (`n_analysed`). The balanced set has 15 countries (`n_countries_balanced`, `balanced_countries`: AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA). 5 countries are incomplete (`n_countries_incomplete`).\n\n## Method\nFor each year I took the simple mean over the 15 balanced countries (`out/analysis.py`; `out/balanced_means_by_year.csv` is a supplementary output). The change is the 2022 mean minus the 2000 mean. The baseline is the mean over all distinct countries with a recorded 2022 value (20 countries, `n_countries_2022_available`), with TUR counted once. The method uses no randomness (`random_seed` = null).\n\n## Results\n- Balanced mean in 2000: **78.060 years** (`mean_2000_balanced` = 78.060267).\n- Balanced mean in 2022: **81.553 years** (`mean_2022_balanced` = 81.553067).\n- Change from 2000 to 2022: **+3.493 years** (`change_2000_2022_balanced` = 3.4928).\n- Baseline, mean over all 20 countries with a 2022 value: 78.692 years (`mean_2022_all_available`). This is lower than the balanced 2022 mean because four of the five excluded countries (BGD, ETH, IND, NGA) have lower life expectancy. The baseline is not a measure of change over time.\n- Feasibility: `feasibility` = \"complete\".\n\n## Uncertainty\nThese are descriptive means over a fixed set of countries; they are not estimates from a sample. I therefore report no sampling standard errors or confidence intervals. The remaining uncertainty is measurement and revision error in the WDI figures, which I cannot quantify from this file. I checked the figures independently with a pivot/dropna recomputation, which matched exactly, and a re-run of the script gave byte-identical output (`out/state/checks.md`).\n\n## Limitations and open issues\n- The balanced set contains only high- and upper-middle-income countries (all OECD members). Switzerland is excluded because of a single missing year (2007), and four lower-income countries are excluded because they have no data before 2011. The result therefore does not represent all 20 countries.\n- The countries are equally weighted, not weighted by population.\n- I resolved the TUR duplicates only because both copies agree exactly. The script stops if a duplicate pair ever has conflicting values.\n- Year-on-year jumps of more than 1.5 years occur in 2020–2022 (e.g. ESP, USA, IND, TUR, BGD). I kept them unchanged, since the task allows no external lookup. None of the countries with such jumps in 2000 or 2022 affects the balanced estimand except through its own recorded values.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"15 of 20 countries have a recorded value in every year 2000-2022; 345 country-years analysed.\",\"results_keys\":[\"n_countries_balanced\",\"n_countries_total\",\"n_countries_incomplete\",\"balanced_countries\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Counts after collapsing duplicated TUR rows.\"},\n {\"id\":\"C2\",\"text\":\"Balanced-panel mean life expectancy rose from 78.060 years in 2000 to 81.553 years in 2022, a change of +3.493 years.\",\"results_keys\":[\"mean_2000_balanced\",\"mean_2022_balanced\",\"change_2000_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Unweighted; balanced set is high/upper-middle income only.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries is 78.692 years, lower than the balanced 2022 mean because lower-life-expectancy countries are excluded from the balanced set.\",\"results_keys\":[\"mean_2022_all_available\",\"mean_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Descriptive baseline only.\"},\n {\"id\":\"C4\",\"text\":\"Data issues: TUR duplicated under two names with identical values (deduplicated), CHE 2007 missing, BGD/ETH/IND/NGA lacking 2000-2010.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"No imputation applied.\"}\n]\nEOF\n~/wp29/venv/bin/python - 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A separate recomputation matched these numbers exactly, re-running `out/analysis.py` reproduces identical output, and the final gate run passed (0 of 32 checks failed).\n\n| Quantity | Value |\n|---|---|\n| Mean over the 15 complete countries, 2000 | 78.060 years |\n| Mean over the 15 complete countries, 2022 | 81.553 years |\n| Change, 2000 to 2022 | +3.493 years |\n| Baseline: mean over all 20 countries in 2022 | 78.692 years |\n| Country-years analysed | 345 (15 × 23) |\n\n**What I found in the data:**\n- **Duplicate Türkiye rows:** Türkiye (TUR) appears twice for every year, once as \"Turkey\" and once as \"Turkiye\", with identical values. I kept one row per country-year, which removed 23 rows. The script stops if a duplicate pair ever disagrees.\n- **Switzerland:** 2007 is blank, so Switzerland is left out of the complete set. 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