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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. 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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"}],"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 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.\ntotal 32\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:41 .\ndrwxr-xr-x 92 simon-pierreboucher staff 2944 oct. 30 00:44 ..\n-rw-r--r-- 1 simon-pierreboucher staff 525 oct. 29 23:41 bundle.json\ndrwxr-xr-x 4 simon-pierreboucher staff 128 oct. 29 23:35 data\n-rw-r--r-- 1 simon-pierreboucher staff 1509 oct. 29 23:35 gate_spec.json\n-rw-r--r-- 1 simon-pierreboucher staff 4324 oct. 29 23:41 TASK.md\ndata:\ntotal 32\ndrwxr-xr-x 4 simon-pierreboucher staff 128 oct. 29 23:35 .\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:41 ..\n-rw-r--r-- 1 simon-pierreboucher staff 11030 oct. 29 23:33 life_expectancy_panel.csv\n-rw-r--r-- 1 simon-pierreboucher staff 1416 oct. 29 23:35 README.md","is_error":true,"tool_use_id":"toolu_01EWAqisrZUnKxG1wYwwkiML"}]},"uuid":"3add444b-47cd-4c6b-9c17-66dc11c4c820","timestamp":"2026-10-30T04:44:10.268Z","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). 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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 \"family\": \"F1\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:41:11.640136+00:00\",\n \"files\": {\n \"TASK.md\": \"979bd92bb25ae3dda2a47ef6fce427a1db4a9c3f848a86bf09c4f424fcfe6dcb\",\n \"data/README.md\": \"e9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0\",\n \"data/life_expectancy_panel.csv\": \"b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac\",\n \"gate_spec.json\": \"5e2280ad93e5a7edb7d300c499d0e2a12c442513edfe1f25b85c24c282bc01bc\"\n }\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\ncountry_name,iso3,year,life_expectancy\nAustralia,AUS,2000,79.234\nAustralia,AUS,2001,79.634\nAustralia,AUS,2002,79.937\nAustralia,AUS,2003,80.239\n 440 data/life_expectancy_panel.csv","is_error":false}]},"uuid":"707557e3-29b3-40ad-aad8-3966527b1bce","timestamp":"2026-10-30T04:44:12.127Z","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 \"family\": \"F1\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:41:11.640136+00:00\",\n \"files\": {\n \"TASK.md\": \"979bd92bb25ae3dda2a47ef6fce427a1db4a9c3f848a86bf09c4f424fcfe6dcb\",\n \"data/README.md\": \"e9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0\",\n \"data/life_expectancy_panel.csv\": \"b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac\",\n \"gate_spec.json\": \"5e2280ad93e5a7edb7d300c499d0e2a12c442513edfe1f25b85c24c282bc01bc\"\n }\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\", 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4) {'country_name': dtype('O'), 'iso3': dtype('O'), 'year': dtype('int64'), 'life_expectancy': dtype('float64')}\n{'country_name': 0, 'iso3': 0, 'year': 0, 'life_expectancy': 1}\n2000 2022 True\n n nu nv names\niso3 \nAUS 23 23 23 [Australia]\nAUT 23 23 23 [Austria]\nBEL 23 23 23 [Belgium]\nBGD 12 12 12 [Bangladesh]\nCAN 23 23 23 [Canada]\nCHE 23 23 22 [Switzerland]\nDEU 23 23 23 [Germany]\nESP 23 23 23 [Spain]\nETH 12 12 12 [Ethiopia]\nFRA 23 23 23 [France]\nGBR 23 23 23 [United Kingdom]\nIND 12 12 12 [India]\nITA 23 23 23 [Italy]\nJPN 23 23 23 [Japan]\nKOR 23 23 23 [Korea, Rep.]\nNGA 12 12 12 [Nigeria]\nNLD 23 23 23 [Netherlands]\nSWE 23 23 23 [Sweden]\nTUR 46 23 46 [Turkey, Turkiye]\nUSA 23 23 23 [United States]\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 NaN\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\n0 0\n['AUS' 'AUT' 'BGD' 'BEL' 'CAN' 'ETH' 'FRA' 'DEU' 'IND' 'ITA' 'JPN' 'KOR'\n 'NLD' 'NGA' 'ESP' 'SWE' 'CHE' 'TUR' 'GBR' 'USA']","is_error":false}]},"uuid":"b314b825-e20f-4b63-b48f-786b7f3999b3","timestamp":"2026-10-30T04:44:17.002Z","toolUseResult":{"stdout":"(439, 4) {'country_name': dtype('O'), 'iso3': dtype('O'), 'year': dtype('int64'), 'life_expectancy': dtype('float64')}\n{'country_name': 0, 'iso3': 0, 'year': 0, 'life_expectancy': 1}\n2000 2022 True\n n nu nv names\niso3 \nAUS 23 23 23 [Australia]\nAUT 23 23 23 [Austria]\nBEL 23 23 23 [Belgium]\nBGD 12 12 12 [Bangladesh]\nCAN 23 23 23 [Canada]\nCHE 23 23 22 [Switzerland]\nDEU 23 23 23 [Germany]\nESP 23 23 23 [Spain]\nETH 12 12 12 [Ethiopia]\nFRA 23 23 23 [France]\nGBR 23 23 23 [United Kingdom]\nIND 12 12 12 [India]\nITA 23 23 23 [Italy]\nJPN 23 23 23 [Japan]\nKOR 23 23 23 [Korea, Rep.]\nNGA 12 12 12 [Nigeria]\nNLD 23 23 23 [Netherlands]\nSWE 23 23 23 [Sweden]\nTUR 46 23 46 [Turkey, Turkiye]\nUSA 23 23 23 [United States]\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 NaN\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\n0 0\n['AUS' 'AUT' 'BGD' 'BEL' 'CAN' 'ETH' 'FRA' 'DEU' 'IND' 'ITA' 'JPN' 'KOR'\n 'NLD' 'NGA' 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[2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]\nETH [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]\nIND [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]\nNGA [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]\n country_name iso3 year life_expectancy diff\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 BGD ETH IND NGA\nyear \n2011 68.499 60.595 67.623 51.490\n2012 68.989 61.443 68.076 51.688\n2013 69.487 62.161 68.499 51.827\n2014 70.016 62.927 68.932 51.940\n2015 70.543 63.592 69.326 51.939\n2016 71.075 64.162 69.709 52.189\n2017 71.606 64.766 70.068 52.397\n2018 72.122 65.257 70.415 52.669\n2019 72.625 65.741 70.746 53.009\n2020 71.421 65.969 70.156 53.072\n2021 71.103 65.330 67.282 53.455\n2022 74.265 66.897 71.698 54.079\n min max\niso3 \nAUS 79.234 83.300\nAUT 78.127 81.895\nBEL 77.722 81.995\nBGD 68.499 74.265\nCAN 79.167 82.159\nCHE 79.680 83.905\nDEU 77.927 81.293\nESP 78.966 83.832\nETH 60.595 66.897\nFRA 79.056 82.827\nGBR 77.741 81.369\nIND 67.282 71.698\nITA 79.778 83.498\nJPN 81.076 84.560\nKOR 75.910 83.527\nNGA 51.490 54.079\nNLD 77.988 82.112\nSWE 79.644 83.110\nTUR 71.931 77.737\nUSA 76.329 78.841","is_error":false}]},"uuid":"01445a76-4400-4943-9a7c-86a322ca6b39","timestamp":"2026-10-30T04:44:22.209Z","toolUseResult":{"stdout":"BGD [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]\nETH [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]\nIND [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]\nNGA [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]\n country_name iso3 year life_expectancy diff\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 BGD ETH IND NGA\nyear \n2011 68.499 60.595 67.623 51.490\n2012 68.989 61.443 68.076 51.688\n2013 69.487 62.161 68.499 51.827\n2014 70.016 62.927 68.932 51.940\n2015 70.543 63.592 69.326 51.939\n2016 71.075 64.162 69.709 52.189\n2017 71.606 64.766 70.068 52.397\n2018 72.122 65.257 70.415 52.669\n2019 72.625 65.741 70.746 53.009\n2020 71.421 65.969 70.156 53.072\n2021 71.103 65.330 67.282 53.455\n2022 74.265 66.897 71.698 54.079\n min max\niso3 \nAUS 79.234 83.300\nAUT 78.127 81.895\nBEL 77.722 81.995\nBGD 68.499 74.265\nCAN 79.167 82.159\nCHE 79.680 83.905\nDEU 77.927 81.293\nESP 78.966 83.832\nETH 60.595 66.897\nFRA 79.056 82.827\nGBR 77.741 81.369\nIND 67.282 71.698\nITA 79.778 83.498\nJPN 81.076 84.560\nKOR 75.910 83.527\nNGA 51.490 54.079\nNLD 77.988 82.112\nSWE 79.644 83.110\nTUR 71.931 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> out/state/contract.md <<'EOF'\n# Contract — F1-conf-02\n**Question.** How much did life expectancy at birth (WDI SP.DYN.LE00.IN) change between 2000 and 2022 across a fixed (balanced) set of countries?\n\n**Estimands.** Balanced set B = iso3 codes with a recorded (non-missing) value in every year 2000..2022 (23 years), each country counted once per year. mean_2000_balanced, mean_2022_balanced = unweighted means over B; change = difference. Composition of B (count, list). Baseline: mean_2022_all_available = unweighted mean over distinct iso3 with a recorded 2022 value. Meaningful as a descriptive unweighted country mean (not population-weighted); comparability over time relies on fixed composition.\n\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 (iso3,country_name,year,life_expectancy; n_analysed rows, unique iso3-year); out/exclusions.csv (step,rule,n_before,n_after,n_removed,rationale; chained); out/report.md (6 sections); out/analysis.py; out/claims.json; state files.\n\n**Information rules.** Only data/life_expectancy_panel.csv. No imputation/interpolation/lookup; empty cell = missing. 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> out/state/contract.md <<'EOF'\n# Contract — F1-conf-02\n**Question.** How much did life expectancy at birth (WDI SP.DYN.LE00.IN) change between 2000 and 2022 across a fixed (balanced) set of countries?\n\n**Estimands.** Balanced set B = iso3 codes with a recorded (non-missing) value in every year 2000..2022 (23 years), each country counted once per year. mean_2000_balanced, mean_2022_balanced = unweighted means over B; change = difference. Composition of B (count, list). Baseline: mean_2022_all_available = unweighted mean over distinct iso3 with a recorded 2022 value. Meaningful as a descriptive unweighted country mean (not population-weighted); comparability over time relies on fixed composition.\n\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 (iso3,country_name,year,life_expectancy; n_analysed rows, unique iso3-year); out/exclusions.csv (step,rule,n_before,n_after,n_removed,rationale; chained); out/report.md (6 sections); out/analysis.py; out/claims.json; state files.\n\n**Information rules.** Only data/life_expectancy_panel.csv. No imputation/interpolation/lookup; empty cell = missing. Country = iso3; at most one obs per country-year (duplicates must be resolved).\n\n**Invalidity conditions.** Counting duplicated country-years twice; using imputed values; empty balanced set (would make infeasible); conflicting duplicate values that cannot be resolved without outside info.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md","description":"Write contract, phase ledger, log"}}]}}37{"type":"api-request","sessionId":"6b44e58f-2b5d-4ced-a69d-25f4de800b68","id":"eb1ad7aa-044f-4f1a-817e-da351b45a5c3","timestamp":"2026-10-30T04:44:34.202Z","version":"2.1.314","querySource":"sdk","shapeHash":"cfc927c3e637eebda92c2b0682ad1cefdc354c12275f00542c0ff7a6c6026c67","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":"251423e8-44bf-4511-b90d-44ca070e985b","keep":10,"tail":["9c174f1504d2c6b5284eef00d8c0b320a524b088bca5f03f801ea697de276fa7","c8b0b3386d190d638410281840b2ada7f9eb35f1e68dacab43be8cc269a13251"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"d84c718813d8ffc2bd3c00fa5e1ecd3cc7aa516e7d0d7a322b6d3c49a85b8b09"}38{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"edc63d8d-cbe2-4f92-aea9-3fb0f69a5f25","sessionId":"6b44e58f-2b5d-4ced-a69d-25f4de800b68"}39{"type":"atis-latch","atis":"","sessionId":"6b44e58f-2b5d-4ced-a69d-25f4de800b68"}40{"parentUuid":"edc63d8d-cbe2-4f92-aea9-3fb0f69a5f25","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp3JgMxyCSjT3bRMgUF","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01H8PPNrCQxDe3kFeBVWbZhM","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit — F1-conf-02\n## data/life_expectancy_panel.csv\n- SHA-256: b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac (matches bundle.json)\n- 439 data rows + header, 4 columns: country_name (str), iso3 (str), year (int), life_expectancy (float, years, 3 decimals).\n- Years 2000–2022 (all 23 present). 20 distinct iso3. 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Not looked up/imputed (rule). These countries are excluded from the balanced set.\n- Consequence: the balanced set is 15 countries, all high-income OECD plus Türkiye; all four low/lower-middle-income countries drop out. The 2022 all-available baseline includes them (19 countries with 2022 values... see results), so it is not comparable to the balanced mean.\n\n## Information-availability\nNo input violates the rules; the only file used is the panel CSV. README.md used for provenance only.\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"sha256 computed and matches bundle.json\",\"schema/dtypes/ranges/missing/duplicates inspected\",\"per-country year coverage inspected\",\"year-on-year jumps inspected\"],\"notes\":\"Issues: TUR duplicated (identical values), CHE 2007 missing, BGD/ETH/IND/NGA only 2011-2022. 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No whitespace issues in iso3/country_name.\n- Value range 51.490 (NGA 2011) – 84.560 (JPN). Largest year-on-year changes are 2020–2022 COVID-era moves (IND −2.87 in 2021, +4.42 in 2022; BGD +3.16 in 2022; USA −1.81 in 2020; ESP −1.60 in 2020) — plausible, kept.\n\n## Anomalies\n1. **Duplicate country-years, TUR**: 46 rows = 23 years under name \"Turkey\" and 23 under \"Turkiye\", same iso3, identical values in all 23 years (an assembly artefact of the two extractions; the WB renamed the country). Handling: keep one row per (iso3, year); since values are identical, choice affects no number. Retained the \"Turkiye\" label (current WB naming; README lists Türkiye). 23 rows removed.\n2. **Missing value**: CHE 2007 has an empty life_expectancy cell. Not imputed (rule). CHE therefore incomplete and excluded from balanced set.\n3. **Truncated coverage**: BGD, ETH, IND, NGA have rows only for 2011–2022 (12 of 23 years); 2000–2010 rows are absent (likely from the narrower extraction). Not looked up/imputed (rule). These countries are excluded from the balanced set.\n- Consequence: the balanced set is 15 countries, all high-income OECD plus Türkiye; all four low/lower-middle-income countries drop out. The 2022 all-available baseline includes them (19 countries with 2022 values... see results), so it is not comparable to the balanced mean.\n\n## Information-availability\nNo input violates the rules; the only file used is the panel CSV. README.md used for provenance only.\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"sha256 computed and matches bundle.json\",\"schema/dtypes/ranges/missing/duplicates inspected\",\"per-country year coverage inspected\",\"year-on-year jumps inspected\"],\"notes\":\"Issues: TUR duplicated (identical values), CHE 2007 missing, BGD/ETH/IND/NGA only 2011-2022. Exclusion ledger produced by analysis.py.\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P1 passed: audit done; decision: dedupe TUR keeping 'Turkiye' label, no imputation.\" >> out/state/log.md","description":"Write data audit and update ledger"}}]}}43{"type":"api-request","sessionId":"6b44e58f-2b5d-4ced-a69d-25f4de800b68","id":"40bc5d02-e684-4f03-a141-a14b4ae4a524","timestamp":"2026-10-30T04:44:45.067Z","version":"2.1.314","querySource":"sdk","shapeHash":"cfc927c3e637eebda92c2b0682ad1cefdc354c12275f00542c0ff7a6c6026c67","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":"eb1ad7aa-044f-4f1a-817e-da351b45a5c3","keep":12,"tail":["df97560806ae1492293a2e09d3b73e963b7f3e9a711c4b97a4a866652fa65b07","f8540b31921134aeed8b1394ade9c19804cfb6fa992484715ed6c6e96569da58"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"f0b5e46e384cf33886350ba8b41328e3dbb0c0001a8860ce75396c837bc64710"}44{"parentUuid":"d5204fe0-b13e-40ac-a13e-04f4adcb357a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp477NbLeZxFiWwxCPp","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XfM5rhoGXAir9DCKwA8crG","name":"Bash","input":{"command":"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/; writes out/results.json, out/analytic.csv, out/exclusions.csv. No randomness.\"\"\"\nimport json\nfrom pathlib import Path\nimport pandas as pd\n\nDATA = Path(\"data/life_expectancy_panel.csv\")\nOUT = Path(\"out\")\nYEARS = list(range(2000, 2023))\n\nraw = pd.read_csv(DATA, dtype={\"country_name\": str, \"iso3\": str})\nn_raw = len(raw)\nledger, issues = [], []\n\ndef step(name, rule, before, after, why):\n ledger.append(dict(step=name, rule=rule, n_before=before, n_after=after,\n n_removed=before - after, rationale=why))\n\ndf = raw[raw[\"year\"].between(2000, 2022)].copy()\nstep(\"1_year_window\", \"keep rows with 2000<=year<=2022\", n_raw, len(df),\n \"Study window; no rows outside it in this file.\")\n\n# Duplicate (iso3, year) rows\ndupmask = df.duplicated([\"iso3\", \"year\"], keep=False)\nif dupmask.any():\n dups = df[dupmask]\n nval = dups.groupby([\"iso3\", \"year\"])[\"life_expectancy\"].nunique(dropna=False)\n if (nval > 1).any():\n raise SystemExit(\"Conflicting duplicate values; cannot resolve without outside information.\")\n for iso, g in dups.groupby(\"iso3\"):\n names = sorted(g[\"country_name\"].unique())\n issues.append({\"issue\": f\"{iso}: {g[['iso3','year']].drop_duplicates().shape[0]} country-years appear twice \"\n f\"under names {names} with identical values (assembly duplicate).\",\n \"handling\": \"Kept one row per (iso3, year); values identical so no number depends on the choice; \"\n \"label 'Turkiye' (current World Bank name) retained where applicable.\"})\n # prefer 'Turkiye' label deterministically, otherwise the last occurrence\n df[\"_pref\"] = (df[\"country_name\"] == \"Turkiye\").astype(int)\n df = df.sort_values([\"iso3\", \"year\", \"_pref\"]).drop_duplicates([\"iso3\", \"year\"], keep=\"last\").drop(columns=\"_pref\")\nbefore = len(raw[raw[\"year\"].between(2000, 2022)])\nstep(\"2_dedupe_iso3_year\", \"one row per (iso3, year); drop exact duplicates\", before, len(df),\n \"Each country contributes at most one observation per year (TUR listed as both Turkey and Turkiye).\")\n\n# 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.\"})\nbefore = len(df)\ndf = df[df[\"life_expectancy\"].notna()]\nstep(\"3_drop_missing_value\", \"drop country-years with empty life_expectancy\", before, len(df),\n \"No imputation allowed; missing country-year has no value.\")\n\n# Coverage\nall_iso = sorted(raw[\"iso3\"].unique())\ncov = df.groupby(\"iso3\")[\"year\"].nunique().reindex(all_iso, fill_value=0)\nbalanced = sorted(cov[cov == len(YEARS)].index)\nincomplete = sorted(set(all_iso) - set(balanced))\nfor iso in incomplete:\n yrs = sorted(df.loc[df.iso3 == iso, \"year\"])\n absent = sorted(set(YEARS) - set(raw.loc[raw.iso3 == iso, \"year\"]))\n if absent:\n issues.append({\"issue\": f\"{iso}: no rows for years {absent[0]}-{absent[-1]} ({len(absent)} years absent; \"\n f\"recorded {yrs[0]}-{yrs[-1]}).\",\n \"handling\": \"Not imputed or looked up; country excluded from balanced set (still in 2022 baseline).\"})\nbefore = len(df)\nanalytic = df[df[\"iso3\"].isin(balanced)].sort_values([\"iso3\", \"year\"])[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]]\nstep(\"4_balanced_panel\", \"keep countries with a recorded value in all 23 years 2000-2022\", before, len(analytic),\n f\"Balanced-panel estimand; excluded incomplete countries: {incomplete}.\")\n\nassert len(analytic) == len(balanced) * len(YEARS)\nassert not analytic.duplicated([\"iso3\", \"year\"]).any()\n\nm2000 = analytic.loc[analytic.year == 2000, \"life_expectancy\"].mean()\nm2022 = analytic.loc[analytic.year == 2022, \"life_expectancy\"].mean()\navail22 = df[df.year == 2022] # deduped, non-missing: one row per iso3\nassert avail22[\"iso3\"].is_unique\nm2022_all = avail22[\"life_expectancy\"].mean()\n\nresults = {\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": len(all_iso),\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(analytic)),\n \"mean_2000_balanced\": round(float(m2000), 6),\n \"mean_2022_balanced\": round(float(m2022), 6),\n \"change_2000_2022_balanced\": round(float(m2022 - m2000), 6),\n \"mean_2022_all_available\": round(float(m2022_all), 6),\n \"n_countries_2022_available\": int(avail22[\"iso3\"].nunique()),\n \"baseline_minus_balanced_2022\": round(float(m2022_all - m2022), 6),\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}\nOUT.mkdir(exist_ok=True)\nanalytic.to_csv(OUT / \"analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(OUT / \"exclusions.csv\", index=False)\nwith open(OUT / \"results.json\", \"w\") as f:\n json.dump(results, f, indent=2, ensure_ascii=False)\nprint(json.dumps({k: v for k, v in results.items() if k != \"data_issues\"}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv; python3 -c \"import json;[print(i) for i in json.load(open('out/results.json'))['data_issues']]\"","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":1159,"cache_read_input_tokens":12705,"output_tokens":2466,"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":1159},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXp46uiRVn96qMrfaBqj","requestRef":"40bc5d02-e684-4f03-a141-a14b4ae4a524","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e74ed1be-293c-4974-b3f6-37ec7743297e","timestamp":"2026-10-30T04:45:03.974Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0152","sessionId":"6b44e58f-2b5d-4ced-a69d-25f4de800b68","version":"2.1.314","gitBranch":"HEAD"}45{"parentUuid":"e74ed1be-293c-4974-b3f6-37ec7743297e","isSidechain":false,"promptId":"51918da9-5c4e-4217-96aa-b3414241f69f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XfM5rhoGXAir9DCKwA8crG","type":"tool_result","content":"{\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 \"baseline_minus_balanced_2022\": -2.861067,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1_year_window,keep rows with 2000<=year<=2022,439,439,0,Study window; no rows outside it in this file.\n2_dedupe_iso3_year,\"one row per (iso3, year); drop exact duplicates\",439,416,23,Each country contributes at most one observation per year (TUR listed as both Turkey and Turkiye).\n3_drop_missing_value,drop country-years with empty life_expectancy,416,415,1,No imputation allowed; missing country-year has no value.\n4_balanced_panel,keep countries with a recorded value in all 23 years 2000-2022,415,345,70,\"Balanced-panel estimand; excluded incomplete countries: ['BGD', 'CHE', 'ETH', 'IND', 'NGA'].\"\n{'issue': \"TUR: 23 country-years appear twice under names ['Turkey', 'Turkiye'] with identical values (assembly duplicate).\", 'handling': \"Kept one row per (iso3, year); values identical so no number depends on the choice; label 'Turkiye' (current World Bank name) retained where applicable.\"}\n{'issue': 'CHE 2007: empty life_expectancy cell.', 'handling': 'Treated as missing (no imputation); country excluded from balanced set.'}\n{'issue': 'BGD: no rows for years 2000-2010 (11 years absent; recorded 2011-2022).', 'handling': 'Not imputed or looked up; country excluded from balanced set (still in 2022 baseline).'}\n{'issue': 'ETH: no rows for years 2000-2010 (11 years absent; recorded 2011-2022).', 'handling': 'Not imputed or looked up; country excluded from balanced set (still in 2022 baseline).'}\n{'issue': 'IND: no rows for years 2000-2010 (11 years absent; recorded 2011-2022).', 'handling': 'Not imputed or looked up; country excluded from balanced set (still in 2022 baseline).'}\n{'issue': 'NGA: no rows for years 2000-2010 (11 years absent; recorded 2011-2022).', 'handling': 'Not imputed or looked up; country excluded from balanced set (still in 2022 baseline).'}","is_error":false}]},"uuid":"d4a534d1-ed23-4b93-a3c3-3ccf0daaa8e0","timestamp":"2026-10-30T04:45:04.216Z","toolUseResult":{"stdout":"{\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 \"baseline_minus_balanced_2022\": -2.861067,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1_year_window,keep rows with 2000<=year<=2022,439,439,0,Study window; no rows outside it in this file.\n2_dedupe_iso3_year,\"one row per (iso3, year); drop exact duplicates\",439,416,23,Each country contributes at most one observation per year (TUR listed as both Turkey and Turkiye).\n3_drop_missing_value,drop country-years with empty life_expectancy,416,415,1,No imputation allowed; missing country-year has no value.\n4_balanced_panel,keep countries with a recorded value in all 23 years 2000-2022,415,345,70,\"Balanced-panel estimand; excluded incomplete countries: ['BGD', 'CHE', 'ETH', 'IND', 'NGA'].\"\n{'issue': \"TUR: 23 country-years appear twice under names ['Turkey', 'Turkiye'] with identical values (assembly duplicate).\", 'handling': \"Kept one row per (iso3, year); values identical so no number depends on the choice; label 'Turkiye' (current World Bank name) retained where applicable.\"}\n{'issue': 'CHE 2007: empty life_expectancy cell.', 'handling': 'Treated as missing (no imputation); country excluded from balanced set.'}\n{'issue': 'BGD: no rows for years 2000-2010 (11 years absent; recorded 2011-2022).', 'handling': 'Not imputed or looked up; country excluded from balanced set (still in 2022 baseline).'}\n{'issue': 'ETH: no rows for years 2000-2010 (11 years absent; recorded 2011-2022).', 'handling': 'Not imputed or looked up; country excluded from balanced set (still in 2022 baseline).'}\n{'issue': 'IND: no rows for years 2000-2010 (11 years absent; recorded 2011-2022).', 'handling': 'Not imputed or looked up; country excluded from balanced set (still in 2022 baseline).'}\n{'issue': 'NGA: no rows for years 2000-2010 (11 years absent; recorded 2011-2022).', 'handling': 'Not imputed or looked up; country excluded from balanced set (still in 2022 baseline).'}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e74ed1be-293c-4974-b3f6-37ec7743297e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0152","sessionId":"6b44e58f-2b5d-4ced-a69d-25f4de800b68","version":"2.1.314","gitBranch":"HEAD"}46{"type":"api-request-blob","sessionId":"6b44e58f-2b5d-4ced-a69d-25f4de800b68","hash":"17199ba868804aa27fb5647b26a665911b6585d7dd4fbdfdc8b383e77f420bf7","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01XfM5rhoGXAir9DCKwA8crG","name":"Bash","input":{"command":"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/; writes out/results.json, out/analytic.csv, out/exclusions.csv. No randomness.\"\"\"\nimport json\nfrom pathlib import Path\nimport pandas as pd\n\nDATA = Path(\"data/life_expectancy_panel.csv\")\nOUT = Path(\"out\")\nYEARS = list(range(2000, 2023))\n\nraw = pd.read_csv(DATA, dtype={\"country_name\": str, \"iso3\": str})\nn_raw = len(raw)\nledger, issues = [], []\n\ndef step(name, rule, before, after, why):\n ledger.append(dict(step=name, rule=rule, n_before=before, n_after=after,\n n_removed=before - after, rationale=why))\n\ndf = raw[raw[\"year\"].between(2000, 2022)].copy()\nstep(\"1_year_window\", \"keep rows with 2000<=year<=2022\", n_raw, len(df),\n \"Study window; no rows outside it in this file.\")\n\n# Duplicate (iso3, year) rows\ndupmask = df.duplicated([\"iso3\", \"year\"], keep=False)\nif dupmask.any():\n dups = df[dupmask]\n nval = dups.groupby([\"iso3\", \"year\"])[\"life_expectancy\"].nunique(dropna=False)\n if (nval > 1).any():\n raise SystemExit(\"Conflicting duplicate values; cannot resolve without outside information.\")\n for iso, g in dups.groupby(\"iso3\"):\n names = sorted(g[\"country_name\"].unique())\n issues.append({\"issue\": f\"{iso}: {g[['iso3','year']].drop_duplicates().shape[0]} country-years appear twice \"\n f\"under names {names} with identical values (assembly duplicate).\",\n \"handling\": \"Kept one row per (iso3, year); values identical so no number depends on the choice; \"\n \"label 'Turkiye' (current World Bank name) retained where applicable.\"})\n # prefer 'Turkiye' label deterministically, otherwise the last occurrence\n df[\"_pref\"] = (df[\"country_name\"] == \"Turkiye\").astype(int)\n df = df.sort_values([\"iso3\", \"year\", \"_pref\"]).drop_duplicates([\"iso3\", \"year\"], keep=\"last\").drop(columns=\"_pref\")\nbefore = len(raw[raw[\"year\"].between(2000, 2022)])\nstep(\"2_dedupe_iso3_year\", \"one row per (iso3, year); drop exact duplicates\", before, len(df),\n \"Each country contributes at most one observation per year (TUR listed as both Turkey and Turkiye).\")\n\n# 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.\"})\nbefore = len(df)\ndf = df[df[\"life_expectancy\"].notna()]\nstep(\"3_drop_missing_value\", \"drop country-years with empty life_expectancy\", before, len(df),\n \"No imputation allowed; missing country-year has no value.\")\n\n# Coverage\nall_iso = sorted(raw[\"iso3\"].unique())\ncov = df.groupby(\"iso3\")[\"year\"].nunique().reindex(all_iso, fill_value=0)\nbalanced = sorted(cov[cov == len(YEARS)].index)\nincomplete = sorted(set(all_iso) - set(balanced))\nfor iso in incomplete:\n yrs = sorted(df.loc[df.iso3 == iso, \"year\"])\n absent = sorted(set(YEARS) - set(raw.loc[raw.iso3 == iso, \"year\"]))\n if absent:\n issues.append({\"issue\": f\"{iso}: no rows for years {absent[0]}-{absent[-1]} ({len(absent)} years absent; \"\n f\"recorded {yrs[0]}-{yrs[-1]}).\",\n \"handling\": \"Not imputed or looked up; country excluded from balanced set (still in 2022 baseline).\"})\nbefore = len(df)\nanalytic = df[df[\"iso3\"].isin(balanced)].sort_values([\"iso3\", \"year\"])[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]]\nstep(\"4_balanced_panel\", \"keep countries with a recorded value in all 23 years 2000-2022\", before, len(analytic),\n f\"Balanced-panel estimand; excluded incomplete countries: {incomplete}.\")\n\nassert len(analytic) == len(balanced) * len(YEARS)\nassert not analytic.duplicated([\"iso3\", \"year\"]).any()\n\nm2000 = analytic.loc[analytic.year == 2000, \"life_expectancy\"].mean()\nm2022 = analytic.loc[analytic.year == 2022, \"life_expectancy\"].mean()\navail22 = df[df.year == 2022] # deduped, non-missing: one row per iso3\nassert avail22[\"iso3\"].is_unique\nm2022_all = avail22[\"life_expectancy\"].mean()\n\nresults = {\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": len(all_iso),\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(analytic)),\n \"mean_2000_balanced\": round(float(m2000), 6),\n \"mean_2022_balanced\": round(float(m2022), 6),\n \"change_2000_2022_balanced\": round(float(m2022 - m2000), 6),\n \"mean_2022_all_available\": round(float(m2022_all), 6),\n \"n_countries_2022_available\": int(avail22[\"iso3\"].nunique()),\n \"baseline_minus_balanced_2022\": round(float(m2022_all - m2022), 6),\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}\nOUT.mkdir(exist_ok=True)\nanalytic.to_csv(OUT / \"analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(OUT / \"exclusions.csv\", index=False)\nwith open(OUT / \"results.json\", \"w\") as f:\n json.dump(results, f, indent=2, ensure_ascii=False)\nprint(json.dumps({k: v for k, v in results.items() if k != \"data_issues\"}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv; python3 -c \"import json;[print(i) for i in json.load(open('out/results.json'))['data_issues']]\"","description":"Write and run analysis script"}}]}}47{"type":"api-request","sessionId":"6b44e58f-2b5d-4ced-a69d-25f4de800b68","id":"795da994-c045-4df1-87ca-e47e439aa535","timestamp":"2026-10-30T04:45:04.218Z","version":"2.1.314","querySource":"sdk","shapeHash":"cfc927c3e637eebda92c2b0682ad1cefdc354c12275f00542c0ff7a6c6026c67","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":"40bc5d02-e684-4f03-a141-a14b4ae4a524","keep":14,"tail":["17199ba868804aa27fb5647b26a665911b6585d7dd4fbdfdc8b383e77f420bf7","f0ce1ac6e3befeea001331d72869d43b488442e4dec8310364e73aa181f7b9d5"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"fe2bbb2c68610890821d33da3679c6e29ab596a9830cea5a1f9e3324a3a62146"}48{"parentUuid":"d4a534d1-ed23-4b93-a3c3-3ccf0daaa8e0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp5X3KJ9NmixqScerFF","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":3665,"cache_read_input_tokens":13864,"output_tokens":1463,"output_tokens_details":{"thinking_tokens":147},"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":3665},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1189,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXp5WnwHcYX59Z7BxrXe","requestRef":"795da994-c045-4df1-87ca-e47e439aa535","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f45921b0-d696-4605-a29c-61250185b809","timestamp":"2026-10-30T04:45:07.171Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0152","sessionId":"6b44e58f-2b5d-4ced-a69d-25f4de800b68","version":"2.1.314","gitBranch":"HEAD"}49{"parentUuid":"f45921b0-d696-4605-a29c-61250185b809","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp5X3KJ9NmixqScerFF","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01EXWNVy5igN7nFEqTvztgWo","name":"Bash","input":{"command":"sed -i '' 's/(19 countries with 2022 values... see results)/(all 20 countries have a 2022 value; key n_countries_2022_available)/' out/state/data_audit.md\ncp out/results.json /tmp/r1.json; shasum -a 256 out/results.json out/analytic.csv out/exclusions.csv > /tmp/h1\nrm out/results.json out/analytic.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null\nshasum -a 256 out/results.json out/analytic.csv out/exclusions.csv | diff - /tmp/h1 && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json,pandas as pd,numpy as np\nr=json.load(open('out/results.json')); a=pd.read_csv('out/analytic.csv'); e=pd.read_csv('out/exclusions.csv')\nraw=pd.read_csv('data/life_expectancy_panel.csv')\nc={}\nc['analytic rows == n_analysed']=len(a)==r['n_analysed']\nc['analytic unique iso3-year']=not a.duplicated(['iso3','year']).any()\nc['analytic no NaN']=a.life_expectancy.notna().all()\nc['each balanced country 23 years']=(a.groupby('iso3').year.nunique()==23).all() and sorted(a.iso3.unique())==r['balanced_countries']\nc['ledger n_before-n_removed=n_after']=(e.n_before-e.n_removed==e.n_after).all()\nc['ledger chained']=(e.n_before.iloc[1:].values==e.n_after.iloc[:-1].values).all()\nc['ledger start=n_input_rows, end=n_analysed']=e.n_before.iloc[0]==r['n_input_rows']['life_expectancy_panel.csv']==len(raw) and e.n_after.iloc[-1]==r['n_analysed']\nc['balanced+incomplete=total']=r['n_countries_balanced']+r['n_countries_incomplete']==r['n_countries_total']==raw.iso3.nunique()\nc['change identity']=abs(r['mean_2022_balanced']-r['mean_2000_balanced']-r['change_2000_2022_balanced'])<1e-5\n# independent recomputation via pivot\np=raw.groupby(['iso3','year']).life_expectancy.first().unstack()\np=p.reindex(columns=range(2000,2023)); bal=p.dropna()\nc['independent pivot balanced set matches']=sorted(bal.index)==r['balanced_countries']\nc['independent mean2000']=abs(bal[2000].mean()-r['mean_2000_balanced'])<1e-6\nc['independent mean2022']=abs(bal[2022].mean()-r['mean_2022_balanced'])<1e-6\nc['independent baseline 2022']=abs(p[2022].dropna().mean()-r['mean_2022_all_available'])<1e-6\nnaive_rows=raw[raw.year==2022].life_expectancy.mean()\nc['magnitudes plausible (40-90 yrs)']=all(40<r[k]<90 for k in ['mean_2000_balanced','mean_2022_balanced','mean_2022_all_available'])\nfor k,v in c.items(): print(f'- {k}: {\"PASS\" if v else \"FAIL\"}')\nprint(f'- info: naive mean over raw 2022 rows (TUR double-counted) = {naive_rows:.6f} vs dedup baseline {r[\"mean_2022_all_available\"]}')\nEOF","description":"Rerun from clean state and run consistency checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3665,"cache_read_input_tokens":13864,"output_tokens":1463,"output_tokens_details":{"thinking_tokens":147},"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":3665},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp5WnwHcYX59Z7BxrXe","requestRef":"795da994-c045-4df1-87ca-e47e439aa535","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1c3f4cf8-163e-409a-b041-2e8f15d3fa9f","timestamp":"2026-10-30T04:45:16.721Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0152","sessionId":"6b44e58f-2b5d-4ced-a69d-25f4de800b68","version":"2.1.314","gitBranch":"HEAD"}50{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"1c3f4cf8-163e-409a-b041-2e8f15d3fa9f","sessionId":"6b44e58f-2b5d-4ced-a69d-25f4de800b68"}51{"type":"atis-latch","atis":"","sessionId":"6b44e58f-2b5d-4ced-a69d-25f4de800b68"}52{"parentUuid":"1c3f4cf8-163e-409a-b041-2e8f15d3fa9f","isSidechain":false,"promptId":"51918da9-5c4e-4217-96aa-b3414241f69f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EXWNVy5igN7nFEqTvztgWo","type":"tool_result","content":"IDENTICAL\n- analytic rows == n_analysed: PASS\n- analytic unique iso3-year: PASS\n- analytic no NaN: PASS\n- each balanced country 23 years: PASS\n- ledger n_before-n_removed=n_after: PASS\n- ledger chained: PASS\n- ledger start=n_input_rows, end=n_analysed: PASS\n- balanced+incomplete=total: PASS\n- change identity: PASS\n- independent pivot balanced set matches: PASS\n- independent mean2000: PASS\n- independent mean2022: PASS\n- independent baseline 2022: PASS\n- magnitudes plausible (40-90 yrs): PASS\n- info: naive mean over raw 2022 rows (TUR double-counted) = 78.639571 vs dedup baseline 78.692","is_error":false}]},"uuid":"cb5012ab-6aba-4b86-abf4-d5801ae00d6a","timestamp":"2026-10-30T04:45:17.158Z","toolUseResult":{"stdout":"IDENTICAL\n- analytic rows == n_analysed: PASS\n- analytic unique iso3-year: PASS\n- analytic no NaN: PASS\n- each balanced country 23 years: PASS\n- ledger n_before-n_removed=n_after: PASS\n- ledger chained: PASS\n- ledger start=n_input_rows, end=n_analysed: PASS\n- balanced+incomplete=total: PASS\n- change identity: PASS\n- independent pivot balanced set matches: PASS\n- independent mean2000: PASS\n- independent mean2022: PASS\n- independent baseline 2022: PASS\n- magnitudes plausible (40-90 yrs): PASS\n- info: naive mean over raw 2022 rows (TUR double-counted) = 78.639571 vs dedup baseline 78.692","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"1c3f4cf8-163e-409a-b041-2e8f15d3fa9f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0152","sessionId":"6b44e58f-2b5d-4ced-a69d-25f4de800b68","version":"2.1.314","gitBranch":"HEAD"}53{"type":"api-request-blob","sessionId":"6b44e58f-2b5d-4ced-a69d-25f4de800b68","hash":"2d5643391c97e55d1af2a36e6b70009453a44d7cac02b7ce076e95bb573b8e28","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01EXWNVy5igN7nFEqTvztgWo","name":"Bash","input":{"command":"sed -i '' 's/(19 countries with 2022 values... see results)/(all 20 countries have a 2022 value; key n_countries_2022_available)/' out/state/data_audit.md\ncp out/results.json /tmp/r1.json; shasum -a 256 out/results.json out/analytic.csv out/exclusions.csv > /tmp/h1\nrm out/results.json out/analytic.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null\nshasum -a 256 out/results.json out/analytic.csv out/exclusions.csv | diff - /tmp/h1 && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json,pandas as pd,numpy as np\nr=json.load(open('out/results.json')); a=pd.read_csv('out/analytic.csv'); e=pd.read_csv('out/exclusions.csv')\nraw=pd.read_csv('data/life_expectancy_panel.csv')\nc={}\nc['analytic rows == n_analysed']=len(a)==r['n_analysed']\nc['analytic unique iso3-year']=not a.duplicated(['iso3','year']).any()\nc['analytic no NaN']=a.life_expectancy.notna().all()\nc['each balanced country 23 years']=(a.groupby('iso3').year.nunique()==23).all() and sorted(a.iso3.unique())==r['balanced_countries']\nc['ledger n_before-n_removed=n_after']=(e.n_before-e.n_removed==e.n_after).all()\nc['ledger chained']=(e.n_before.iloc[1:].values==e.n_after.iloc[:-1].values).all()\nc['ledger start=n_input_rows, end=n_analysed']=e.n_before.iloc[0]==r['n_input_rows']['life_expectancy_panel.csv']==len(raw) and e.n_after.iloc[-1]==r['n_analysed']\nc['balanced+incomplete=total']=r['n_countries_balanced']+r['n_countries_incomplete']==r['n_countries_total']==raw.iso3.nunique()\nc['change identity']=abs(r['mean_2022_balanced']-r['mean_2000_balanced']-r['change_2000_2022_balanced'])<1e-5\n# independent recomputation via pivot\np=raw.groupby(['iso3','year']).life_expectancy.first().unstack()\np=p.reindex(columns=range(2000,2023)); bal=p.dropna()\nc['independent pivot balanced set matches']=sorted(bal.index)==r['balanced_countries']\nc['independent mean2000']=abs(bal[2000].mean()-r['mean_2000_balanced'])<1e-6\nc['independent mean2022']=abs(bal[2022].mean()-r['mean_2022_balanced'])<1e-6\nc['independent baseline 2022']=abs(p[2022].dropna().mean()-r['mean_2022_all_available'])<1e-6\nnaive_rows=raw[raw.year==2022].life_expectancy.mean()\nc['magnitudes plausible (40-90 yrs)']=all(40<r[k]<90 for k in ['mean_2000_balanced','mean_2022_balanced','mean_2022_all_available'])\nfor k,v in c.items(): print(f'- {k}: {\"PASS\" if v else \"FAIL\"}')\nprint(f'- info: naive mean over raw 2022 rows (TUR double-counted) = {naive_rows:.6f} vs dedup baseline {r[\"mean_2022_all_available\"]}')\nEOF","description":"Rerun from clean state and run consistency 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> out/state/checks.md <<'EOF'\n# Verification checks (P4) — 2026-10-30\nReproducibility: deleted out/results.json, out/analytic.csv, out/exclusions.csv; re-ran `~/wp29/venv/bin/python out/analysis.py`; SHA-256 of all three files identical to the first run — PASS.\n\nInternal consistency (all PASS):\n- analytic rows == n_analysed (345); unique (iso3, year); no NaN; each balanced country has 23 years; country list matches balanced_countries.\n- Exclusion ledger: n_before − n_removed = n_after each row; chained; starts at 439 = rows read; ends at 345 = n_analysed. Total removed 94 = 23 duplicates + 1 missing + 70 incomplete-country rows.\n- n_countries_balanced (15) + n_countries_incomplete (5) = n_countries_total (20).\n- change = mean_2022 − mean_2000 (identity holds to 1e-5).\n- Independent recomputation via iso3×year pivot reproduces balanced set, both balanced means and the 2022 baseline (<1e-6).\n- Magnitudes within 40–90 years; balanced 2022 mean (81.55) > all-available 2022 mean (78.69), as expected since the low-life-expectancy countries are excluded from the balanced set.\n- Info: a naive mean over raw 2022 rows that double-counts TUR would be 78.639571 (vs 78.692 deduplicated) — confirms dedup matters for the baseline.\nNo CI pairs, no chronology pairs, no random seed (deterministic).\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P2']={\"status\":\"passed\",\"checks\":[\"baseline mean_2022_all_available computed (78.692, 20 countries, deduplicated)\",\"independently recomputed in P4\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"out/analysis.py reads only data/, writes results.json, analytic.csv, exclusions.csv\",\"no randomness; 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re-ran `~/wp29/venv/bin/python out/analysis.py`; SHA-256 of all three files identical to the first run — PASS.\n\nInternal consistency (all PASS):\n- analytic rows == n_analysed (345); unique (iso3, year); no NaN; each balanced country has 23 years; country list matches balanced_countries.\n- Exclusion ledger: n_before − n_removed = n_after each row; chained; starts at 439 = rows read; ends at 345 = n_analysed. Total removed 94 = 23 duplicates + 1 missing + 70 incomplete-country rows.\n- n_countries_balanced (15) + n_countries_incomplete (5) = n_countries_total (20).\n- change = mean_2022 − mean_2000 (identity holds to 1e-5).\n- Independent recomputation via iso3×year pivot reproduces balanced set, both balanced means and the 2022 baseline (<1e-6).\n- Magnitudes within 40–90 years; balanced 2022 mean (81.55) > all-available 2022 mean (78.69), as expected since the low-life-expectancy countries are excluded from the balanced set.\n- Info: a naive mean over raw 2022 rows that double-counts TUR would be 78.639571 (vs 78.692 deduplicated) — confirms dedup matters for the baseline.\nNo CI pairs, no chronology pairs, no random seed (deterministic).\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P2']={\"status\":\"passed\",\"checks\":[\"baseline mean_2022_all_available computed (78.692, 20 countries, deduplicated)\",\"independently recomputed in P4\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"out/analysis.py reads only data/, writes results.json, analytic.csv, exclusions.csv\",\"no randomness; random_seed null\"],\"notes\":\"\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run: identical SHA-256 for all 3 outputs\",\"14 consistency checks PASS (see out/state/checks.md)\"],\"notes\":\"\"}\np['P5']={\"status\":\"running\",\"checks\":[],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P2/P3 passed: analysis.py written, baseline and balanced means computed.\n- 2026-10-30 P4 passed: reproducibility + consistency checks all PASS.\" >> out/state/log.md","description":"Record checks and update phase ledger"}}]}}59{"type":"api-request","sessionId":"6b44e58f-2b5d-4ced-a69d-25f4de800b68","id":"305372f2-928a-4cbd-8a2c-b45b3cf22878","timestamp":"2026-10-30T04:45:27.096Z","version":"2.1.314","querySource":"sdk","shapeHash":"cfc927c3e637eebda92c2b0682ad1cefdc354c12275f00542c0ff7a6c6026c67","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":"8ace84f9-fbe9-4147-b347-85b6228b0a34","keep":18,"tail":["932583c39b9f7d8075a3ef3edb8f0e0e254e50fce17e8ceff3fc6f0cc5c42492","5bf354d58b5f7cc00dfdf15ddad829808d0feb92e08f5d2676f0deb6d75e5439"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"56aae2833678f8bdce8b1ade62e0b164d0a0625dbe5f8245ec53ac250d7b59d3"}60{"parentUuid":"9d861994-b1f7-481a-a653-b70933188528","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp7CnWxAY6PCAVAqfk7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Fgx18X2gDrxtLAEb2jH9sX","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 (World Bank WDI `SP.DYN.LE00.IN`) change between 2000 and 2022 across a fixed set of countries? The estimand is the unweighted mean over countries that have a recorded value in every year 2000–2022 (the balanced panel), with each country counted once. I also report a naive descriptive baseline for 2022: the mean over every distinct country with a 2022 value.\n\n## Data and exclusions\nInput: `data/life_expectancy_panel.csv`, 439 data rows (`n_input_rows`), 20 distinct iso3 codes (`n_countries_total`), years 2000–2022. I found three data problems, all listed in `data_issues`:\n1. **Duplicate Türkiye rows.** TUR appears twice for each of the 23 years, once as \"Turkey\" and once as \"Turkiye\", with identical values. This is an assembly duplicate. I kept one row per (iso3, year). Because the values are identical, no number depends on which row is kept.\n2. **One missing value.** The CHE (Switzerland) 2007 cell is empty. It was not imputed, so CHE is incomplete.\n3. **Short coverage for four countries.** BGD, ETH, IND and NGA only have rows for 2011–2022; their 2000–2010 rows are absent. These values were not looked up or imputed, so all four are incomplete.\n\nExclusion ledger (`out/exclusions.csv`): 439 rows read → 439 after the year-window filter (0 removed) → 416 after deduplication (23 removed) → 415 after dropping the missing value (1 removed) → 345 after keeping only balanced countries (70 removed). The final 345 is `n_analysed`, i.e. 15 countries × 23 years.\n\n## Method\nI deduplicated on (iso3, year) after confirming the duplicate values were identical; the script stops if duplicate values conflict. I then dropped empty values and defined the balanced set as the iso3 codes with 23 distinct years of recorded values. For 2000 and 2022 I took the simple unweighted mean over that set. The baseline is the unweighted mean over every distinct iso3 with a recorded 2022 value, after deduplication. Everything is deterministic: no randomness is used, so `random_seed` = null. `out/analysis.py` regenerates all outputs.\n\n## Results\n- Balanced set: 15 countries (`n_countries_balanced`): AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA (`balanced_countries`). Five countries are incomplete (`n_countries_incomplete`): BGD, CHE, ETH, IND, NGA (`incomplete_countries`).\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, 2022 mean over all 20 countries with a 2022 value: **78.692 years** (`mean_2022_all_available`, `n_countries_2022_available` = 20). That is 2.861 years below the balanced 2022 mean (`baseline_minus_balanced_2022` = −2.861067). The gap exists because the baseline includes the four lower-life-expectancy countries that drop out of the balanced set. The baseline should not be compared with the 2000 balanced mean.\n- Feasibility: `feasibility` = \"complete\". The estimand as defined could be computed in full.\n\n## Uncertainty\nThese are exact descriptive means over a fixed, fully listed set of countries, so I did not compute a sampling confidence interval. The remaining uncertainty is about measurement and composition, not sampling:\n- WDI estimates are model-based and get revised between releases.\n- Values are rounded to 3 decimals, which shifts the means by less than 0.001 years.\n- The balanced set depends on assembly errors in the file (see the next section).\n\n## Limitations and open issues\n- **The balanced set is not representative.** It contains only high-income OECD countries plus Türkiye. All four low and lower-middle-income countries (BGD, ETH, IND, NGA) drop out, apparently because their 2000–2010 rows were lost in the narrower of the two extractions, not because the World Bank has no data for those years. Switzerland drops out because of a single empty 2007 cell. The +3.49-year change therefore describes this 15-country set only and cannot be generalised to all 20 countries. The task rules forbid looking up or imputing the missing values, so I did not try to recover them.\n- The means are unweighted country means, not population-weighted.\n- The country label kept for TUR is \"Turkiye\". This is a cosmetic choice and affects no number.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"15 of the 20 countries have a recorded life expectancy in every year 2000-2022; BGD, CHE, ETH, IND and NGA are incomplete.\",\"results_keys\":[\"n_countries_total\",\"n_countries_balanced\",\"n_countries_incomplete\",\"balanced_countries\",\"incomplete_countries\"],\"status\":\"supported\",\"caveat\":\"Incompleteness of BGD/ETH/IND/NGA appears to be an assembly artefact (2000-2010 rows absent); CHE lacks only 2007.\"},\n {\"id\":\"C2\",\"text\":\"The 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; describes a 15-country set of high-income countries plus Turkiye only.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 countries with a 2022 value is 78.692 years, 2.861 years below the balanced 2022 mean, because it includes the lower-life-expectancy countries excluded from the balanced set.\",\"results_keys\":[\"mean_2022_all_available\",\"n_countries_2022_available\",\"baseline_minus_balanced_2022\"],\"status\":\"supported\",\"caveat\":\"Descriptive baseline, not the estimand; computed after removing duplicated TUR rows.\"},\n {\"id\":\"C4\",\"text\":\"The analytic dataset has 345 country-years after removing 23 duplicate TUR rows, 1 missing value and 70 rows of incomplete countries from 439 input rows.\",\"results_keys\":[\"n_input_rows\",\"n_analysed\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\nr=json.load(open('out/results.json')); c=json.load(open('out/claims.json'))\nassert all(k in r for x in c for k in x['results_keys']); print(\"claims keys OK\")\np=json.load(open('out/state/phases.json'))\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md has all 6 required sections\",\"claims.json keys all exist in results.json (checked)\",\"all report numbers taken from results.json\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\ngrep '^## ' out/report.md; echo \"- 2026-10-30 P5 passed: report and claims written; task complete.\" >> out/state/log.md; ls out out/state","description":"Write report and claims, finalize ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1105,"cache_read_input_tokens":19269,"output_tokens":2681,"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":1105},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXp7CbrG3WTXg6UUpX5c","requestRef":"305372f2-928a-4cbd-8a2c-b45b3cf22878","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"fb95e689-88fc-443c-8eed-18637c69f51c","timestamp":"2026-10-30T04:45:53.035Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0152","sessionId":"6b44e58f-2b5d-4ced-a69d-25f4de800b68","version":"2.1.314","gitBranch":"HEAD"}61{"parentUuid":"fb95e689-88fc-443c-8eed-18637c69f51c","isSidechain":false,"promptId":"51918da9-5c4e-4217-96aa-b3414241f69f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Fgx18X2gDrxtLAEb2jH9sX","type":"tool_result","content":"claims keys OK\n## Question\n## Data and exclusions\n## Method\n## Results\n## Uncertainty\n## Limitations and open issues\nout:\nanalysis.py\nanalytic.csv\nclaims.json\nexclusions.csv\nreport.md\nresults.json\nstate\n\nout/state:\nchecks.md\ncontract.md\ndata_audit.md\nlog.md\nphases.json","is_error":false}]},"uuid":"9d97b4ed-3e6a-45e2-a9b8-7243b5997b31","timestamp":"2026-10-30T04:45:53.104Z","toolUseResult":{"stdout":"claims keys OK\n## Question\n## Data and exclusions\n## Method\n## Results\n## Uncertainty\n## Limitations and open issues\nout:\nanalysis.py\nanalytic.csv\nclaims.json\nexclusions.csv\nreport.md\nresults.json\nstate\n\nout/state:\nchecks.md\ncontract.md\ndata_audit.md\nlog.md\nphases.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"fb95e689-88fc-443c-8eed-18637c69f51c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0152","sessionId":"6b44e58f-2b5d-4ced-a69d-25f4de800b68","version":"2.1.314","gitBranch":"HEAD"}62{"type":"api-request-blob","sessionId":"6b44e58f-2b5d-4ced-a69d-25f4de800b68","hash":"17a2dab995cfdeedda7033e09d9fb157417887b2f3ee643f5a2c3993ba1cf21f","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01Fgx18X2gDrxtLAEb2jH9sX","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 (World Bank WDI `SP.DYN.LE00.IN`) change between 2000 and 2022 across a fixed set of countries? The estimand is the unweighted mean over countries that have a recorded value in every year 2000–2022 (the balanced panel), with each country counted once. I also report a naive descriptive baseline for 2022: the mean over every distinct country with a 2022 value.\n\n## Data and exclusions\nInput: `data/life_expectancy_panel.csv`, 439 data rows (`n_input_rows`), 20 distinct iso3 codes (`n_countries_total`), years 2000–2022. I found three data problems, all listed in `data_issues`:\n1. **Duplicate Türkiye rows.** TUR appears twice for each of the 23 years, once as \"Turkey\" and once as \"Turkiye\", with identical values. This is an assembly duplicate. I kept one row per (iso3, year). Because the values are identical, no number depends on which row is kept.\n2. **One missing value.** The CHE (Switzerland) 2007 cell is empty. It was not imputed, so CHE is incomplete.\n3. **Short coverage for four countries.** BGD, ETH, IND and NGA only have rows for 2011–2022; their 2000–2010 rows are absent. These values were not looked up or imputed, so all four are incomplete.\n\nExclusion ledger (`out/exclusions.csv`): 439 rows read → 439 after the year-window filter (0 removed) → 416 after deduplication (23 removed) → 415 after dropping the missing value (1 removed) → 345 after keeping only balanced countries (70 removed). The final 345 is `n_analysed`, i.e. 15 countries × 23 years.\n\n## Method\nI deduplicated on (iso3, year) after confirming the duplicate values were identical; the script stops if duplicate values conflict. I then dropped empty values and defined the balanced set as the iso3 codes with 23 distinct years of recorded values. For 2000 and 2022 I took the simple unweighted mean over that set. The baseline is the unweighted mean over every distinct iso3 with a recorded 2022 value, after deduplication. Everything is deterministic: no randomness is used, so `random_seed` = null. `out/analysis.py` regenerates all outputs.\n\n## Results\n- Balanced set: 15 countries (`n_countries_balanced`): AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA (`balanced_countries`). Five countries are incomplete (`n_countries_incomplete`): BGD, CHE, ETH, IND, NGA (`incomplete_countries`).\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, 2022 mean over all 20 countries with a 2022 value: **78.692 years** (`mean_2022_all_available`, `n_countries_2022_available` = 20). That is 2.861 years below the balanced 2022 mean (`baseline_minus_balanced_2022` = −2.861067). The gap exists because the baseline includes the four lower-life-expectancy countries that drop out of the balanced set. The baseline should not be compared with the 2000 balanced mean.\n- Feasibility: `feasibility` = \"complete\". The estimand as defined could be computed in full.\n\n## Uncertainty\nThese are exact descriptive means over a fixed, fully listed set of countries, so I did not compute a sampling confidence interval. The remaining uncertainty is about measurement and composition, not sampling:\n- WDI estimates are model-based and get revised between releases.\n- Values are rounded to 3 decimals, which shifts the means by less than 0.001 years.\n- The balanced set depends on assembly errors in the file (see the next section).\n\n## Limitations and open issues\n- **The balanced set is not representative.** It contains only high-income OECD countries plus Türkiye. All four low and lower-middle-income countries (BGD, ETH, IND, NGA) drop out, apparently because their 2000–2010 rows were lost in the narrower of the two extractions, not because the World Bank has no data for those years. Switzerland drops out because of a single empty 2007 cell. The +3.49-year change therefore describes this 15-country set only and cannot be generalised to all 20 countries. The task rules forbid looking up or imputing the missing values, so I did not try to recover them.\n- The means are unweighted country means, not population-weighted.\n- The country label kept for TUR is \"Turkiye\". This is a cosmetic choice and affects no number.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"15 of the 20 countries have a recorded life expectancy in every year 2000-2022; BGD, CHE, ETH, IND and NGA are incomplete.\",\"results_keys\":[\"n_countries_total\",\"n_countries_balanced\",\"n_countries_incomplete\",\"balanced_countries\",\"incomplete_countries\"],\"status\":\"supported\",\"caveat\":\"Incompleteness of BGD/ETH/IND/NGA appears to be an assembly artefact (2000-2010 rows absent); CHE lacks only 2007.\"},\n {\"id\":\"C2\",\"text\":\"The 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; describes a 15-country set of high-income countries plus Turkiye only.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 countries with a 2022 value is 78.692 years, 2.861 years below the balanced 2022 mean, because it includes the lower-life-expectancy countries excluded from the balanced set.\",\"results_keys\":[\"mean_2022_all_available\",\"n_countries_2022_available\",\"baseline_minus_balanced_2022\"],\"status\":\"supported\",\"caveat\":\"Descriptive baseline, not the estimand; computed after removing duplicated TUR rows.\"},\n {\"id\":\"C4\",\"text\":\"The analytic dataset has 345 country-years after removing 23 duplicate TUR rows, 1 missing value and 70 rows of incomplete countries from 439 input rows.\",\"results_keys\":[\"n_input_rows\",\"n_analysed\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\nr=json.load(open('out/results.json')); c=json.load(open('out/claims.json'))\nassert all(k in r for x in c for k in x['results_keys']); print(\"claims keys OK\")\np=json.load(open('out/state/phases.json'))\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md has all 6 required sections\",\"claims.json keys all exist in results.json (checked)\",\"all report numbers taken from results.json\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\ngrep '^## ' out/report.md; echo \"- 2026-10-30 P5 passed: report and claims written; task complete.\" >> out/state/log.md; ls out out/state","description":"Write report and claims, finalize 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life expectancy across the 15 countries with a value in every year from 2000 to 2022 rose from **78.060 years to 81.553 years, an increase of 3.493 years**. All the files the task asks for are in `out/`. Re-running `out/analysis.py` from scratch produced byte-identical outputs, and an independent recalculation matched every number.\n\n**Problems found in the data file**\n- **Türkiye listed twice:** each of its 23 years appears once as \"Turkey\" and once as \"Turkiye\", with identical values. I kept one row per year, so the result doesn't depend on which one.\n- **One missing value:** Switzerland has no value for 2007. Nothing was filled in, so Switzerland is left out of the 15-country average.\n- **Four countries cut short:** Bangladesh, Ethiopia, India and Nigeria only have rows for 2011–2022. Their 2000–2010 rows look like they were lost when the file was put together, but the rules forbid filling them in, so all four are left out.\n\n**Results**\n- **Country set:** 15 of the 20 countries are complete: AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA.\n- **Rows used:** 345 country-years (15 × 23). The 439 rows read lose 23 duplicates, 1 missing value and 70 rows from incomplete countries.\n- **Comparison figure for 2022:** the simple average over all 20 countries with a 2022 value is 78.692 years. That is 2.861 years lower than the 15-country figure, because it includes the four lower-life-expectancy countries. It should not be compared with the 2000 figure.\n- **Feasibility:** marked \"complete\", since the average as the task defines it could be calculated in full.\n\n**Limitation:** the 15 countries are all high-income OECD countries plus Türkiye. The +3.49-year change describes only them, not all 20 countries. The averages are also not weighted by population. 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