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It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n"}],"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. 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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). 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SHA-256 of the raw API response: `ead88ee94d5b6cc6f8a055c9f2ec79f232c37faf8baca0ea122dbfc0550ff27e`\n- License: World Bank open data, Creative Commons Attribution 4.0 (CC BY-4.0).\n- Columns: `country_name` (string), `iso3` (ISO-3166 alpha-3 code as returned by the API), `year` (integer), `life_expectancy` (years, 3 decimals). Values were rounded to 3 decimals; nothing else was changed in the values.\n- The panel was assembled by the task author from two extractions of the API response (one earlier, narrower extraction and one later, wider one) and may contain assembly errors. An empty `life_expectancy` cell means no value was recorded for that country-year in the file.\n{\n \"task_id\": \"F1-conf-02\",\n \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/analytic.csv\", \"out/exclusions.csv\"],\n \"results_keys\": {\n \"task_id\": {\"type\": \"string\", \"nullable\": false},\n \"n_countries_total\": {\"type\": \"integer\", \"nullable\": false},\n \"n_countries_balanced\": {\"type\": \"integer\", \"nullable\": false},\n \"n_countries_incomplete\": {\"type\": \"integer\", \"nullable\": false},\n \"balanced_countries\": {\"type\": \"array\", \"nullable\": false},\n \"n_analysed\": {\"type\": \"integer\", \"nullable\": false},\n \"mean_2000_balanced\": {\"type\": \"number\", \"nullable\": false},\n \"mean_2022_balanced\": {\"type\": \"number\", \"nullable\": false},\n \"change_2000_2022_balanced\": {\"type\": \"number\", \"nullable\": false},\n \"mean_2022_all_available\": {\"type\": \"number\", \"nullable\": false},\n \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n },\n \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n \"family_checks\": [\"exclusion_ledger_sums\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\"],\n \"ci_pairs\": [],\n \"positive_keys\": [],\n \"chronology_pairs\": [],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-6\n}\nb3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac data/life_expectancy_panel.csv\ne9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0 data/README.md\n 440 data/life_expectancy_panel.csv\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","is_error":false}]},"uuid":"e7e5a3ed-aa0e-4020-9962-6efb5f29a5d6","timestamp":"2026-10-30T04:46:09.252Z","toolUseResult":{"stdout":"# Data provenance — F1-conf-02\n\n`life_expectancy_panel.csv` — observed data (not simulated): **Life expectancy at birth, total (years)**, indicator `SP.DYN.LE00.IN`, World Development Indicators (World Bank, source id 2, release last updated 2026-10-08), for 20 countries (Australia, Austria, Bangladesh, Belgium, Canada, Ethiopia, France, Germany, India, Italy, Japan, Korea Rep., Netherlands, Nigeria, Spain, Sweden, Switzerland, Türkiye, United Kingdom, United States), years 2000–2022.\n\n- Source URL (JSON API): `https://api.worldbank.org/v2/country/CAN;USA;GBR;FRA;DEU;ITA;ESP;JPN;KOR;AUS;SWE;NLD;CHE;BEL;AUT;TUR;IND;NGA;ETH;BGD/indicator/SP.DYN.LE00.IN?format=json&per_page=2000&date=2000:2022`\n- Retrieved: 2026-10-30T03:25Z. SHA-256 of the raw API response: `ead88ee94d5b6cc6f8a055c9f2ec79f232c37faf8baca0ea122dbfc0550ff27e`\n- License: World Bank open data, Creative Commons Attribution 4.0 (CC BY-4.0).\n- Columns: `country_name` (string), `iso3` (ISO-3166 alpha-3 code as returned by the API), `year` (integer), `life_expectancy` (years, 3 decimals). Values were rounded to 3 decimals; nothing else was changed in the values.\n- The panel was assembled by the task author from two extractions of the API response (one earlier, narrower extraction and one later, wider one) and may contain assembly errors. An empty `life_expectancy` cell means no value was recorded for that country-year in the file.\n{\n \"task_id\": \"F1-conf-02\",\n \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/analytic.csv\", \"out/exclusions.csv\"],\n \"results_keys\": {\n \"task_id\": {\"type\": \"string\", \"nullable\": false},\n \"n_countries_total\": {\"type\": \"integer\", \"nullable\": false},\n \"n_countries_balanced\": {\"type\": \"integer\", \"nullable\": false},\n \"n_countries_incomplete\": {\"type\": \"integer\", \"nullable\": false},\n \"balanced_countries\": {\"type\": \"array\", \"nullable\": false},\n \"n_analysed\": {\"type\": \"integer\", \"nullable\": false},\n \"mean_2000_balanced\": {\"type\": \"number\", \"nullable\": false},\n \"mean_2022_balanced\": {\"type\": \"number\", \"nullable\": false},\n \"change_2000_2022_balanced\": {\"type\": \"number\", \"nullable\": false},\n \"mean_2022_all_available\": {\"type\": \"number\", \"nullable\": false},\n \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n },\n \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n \"family_checks\": [\"exclusion_ledger_sums\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\"],\n \"ci_pairs\": [],\n \"positive_keys\": [],\n \"chronology_pairs\": [],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-6\n}\nb3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac data/life_expectancy_panel.csv\ne9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0 data/README.md\n 440 data/life_expectancy_panel.csv\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","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"179eca75-cabc-4507-bf86-fa26180df47b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0156","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","version":"2.1.314","gitBranch":"HEAD"}19{"type":"api-request-blob","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","hash":"57e5223c1adbae46d44c425e5a7539235ef4573e021814acc95e4152c9aa63eb","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01C3hfdoo6KSQWKQjMw2h3Rp","name":"Bash","input":{"command":"cat data/README.md; 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~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/life_expectancy_panel.csv',dtype={'iso3':str})\nprint(d.shape, d.dtypes)\nprint(d.isna().sum())\nprint(d.groupby(['iso3']).agg(n=('year','size'),nn=('life_expectancy','count'),names=('country_name',lambda s:sorted(set(s))),ymin=('year','min'),ymax=('year','max')))\ndup=d[d.duplicated(['iso3','year'],keep=False)].sort_values(['iso3','year']); print(dup)\nprint(d[d.life_expectancy.isna()])\nprint(d.life_expectancy.describe())\nprint(sorted(d.year.unique()))\nprint(d.iso3.unique(), d.country_name.unique())\n\"","description":"Audit data file"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1631,"cache_read_input_tokens":5508,"output_tokens":388,"output_tokens_details":{"thinking_tokens":20},"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":1631},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpAJrEWJCFsTUJ689hn","requestRef":"454cc64c-0906-4625-a40c-1b11e5074acb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0cf0d55e-a75e-4300-b3ec-e7376833a142","timestamp":"2026-10-30T04:46:13.553Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0156","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"0cf0d55e-a75e-4300-b3ec-e7376833a142","isSidechain":false,"promptId":"c10f33de-72c4-40b2-98ba-bcc768d7e713","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014C78URhKZgU6SgfJGWPMYA","type":"tool_result","content":"(439, 4) country_name object\niso3 object\nyear int64\nlife_expectancy float64\ndtype: object\ncountry_name 0\niso3 0\nyear 0\nlife_expectancy 1\ndtype: int64\n n nn names ymin ymax\niso3 \nAUS 23 23 [Australia] 2000 2022\nAUT 23 23 [Austria] 2000 2022\nBEL 23 23 [Belgium] 2000 2022\nBGD 12 12 [Bangladesh] 2011 2022\nCAN 23 23 [Canada] 2000 2022\nCHE 23 22 [Switzerland] 2000 2022\nDEU 23 23 [Germany] 2000 2022\nESP 23 23 [Spain] 2000 2022\nETH 12 12 [Ethiopia] 2011 2022\nFRA 23 23 [France] 2000 2022\nGBR 23 23 [United Kingdom] 2000 2022\nIND 12 12 [India] 2011 2022\nITA 23 23 [Italy] 2000 2022\nJPN 23 23 [Japan] 2000 2022\nKOR 23 23 [Korea, Rep.] 2000 2022\nNGA 12 12 [Nigeria] 2011 2022\nNLD 23 23 [Netherlands] 2000 2022\nSWE 23 23 [Sweden] 2000 2022\nTUR 46 46 [Turkey, Turkiye] 2000 2022\nUSA 23 23 [United States] 2000 2022\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\n[np.int64(2000), np.int64(2001), np.int64(2002), np.int64(2003), np.int64(2004), np.int64(2005), np.int64(2006), np.int64(2007), np.int64(2008), np.int64(2009), np.int64(2010), np.int64(2011), np.int64(2012), np.int64(2013), np.int64(2014), np.int64(2015), np.int64(2016), np.int64(2017), np.int64(2018), np.int64(2019), np.int64(2020), np.int64(2021), np.int64(2022)]\n['AUS' 'AUT' 'BGD' 'BEL' 'CAN' 'ETH' 'FRA' 'DEU' 'IND' 'ITA' 'JPN' 'KOR'\n 'NLD' 'NGA' 'ESP' 'SWE' 'CHE' 'TUR' 'GBR' 'USA'] ['Australia' 'Austria' 'Bangladesh' 'Belgium' 'Canada' 'Ethiopia' 'France'\n 'Germany' 'India' 'Italy' 'Japan' 'Korea, Rep.' 'Netherlands' 'Nigeria'\n 'Spain' 'Sweden' 'Switzerland' 'Turkey' 'Turkiye' 'United Kingdom'\n 'United States']","is_error":false}]},"uuid":"c2a6697e-2691-408c-a262-4761d06170ff","timestamp":"2026-10-30T04:46:13.772Z","toolUseResult":{"stdout":"(439, 4) country_name object\niso3 object\nyear int64\nlife_expectancy float64\ndtype: object\ncountry_name 0\niso3 0\nyear 0\nlife_expectancy 1\ndtype: int64\n n nn names ymin ymax\niso3 \nAUS 23 23 [Australia] 2000 2022\nAUT 23 23 [Austria] 2000 2022\nBEL 23 23 [Belgium] 2000 2022\nBGD 12 12 [Bangladesh] 2011 2022\nCAN 23 23 [Canada] 2000 2022\nCHE 23 22 [Switzerland] 2000 2022\nDEU 23 23 [Germany] 2000 2022\nESP 23 23 [Spain] 2000 2022\nETH 12 12 [Ethiopia] 2011 2022\nFRA 23 23 [France] 2000 2022\nGBR 23 23 [United Kingdom] 2000 2022\nIND 12 12 [India] 2011 2022\nITA 23 23 [Italy] 2000 2022\nJPN 23 23 [Japan] 2000 2022\nKOR 23 23 [Korea, Rep.] 2000 2022\nNGA 12 12 [Nigeria] 2011 2022\nNLD 23 23 [Netherlands] 2000 2022\nSWE 23 23 [Sweden] 2000 2022\nTUR 46 46 [Turkey, Turkiye] 2000 2022\nUSA 23 23 [United States] 2000 2022\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\n[np.int64(2000), np.int64(2001), np.int64(2002), np.int64(2003), np.int64(2004), np.int64(2005), np.int64(2006), np.int64(2007), np.int64(2008), np.int64(2009), np.int64(2010), np.int64(2011), np.int64(2012), np.int64(2013), np.int64(2014), np.int64(2015), np.int64(2016), np.int64(2017), np.int64(2018), np.int64(2019), np.int64(2020), np.int64(2021), np.int64(2022)]\n['AUS' 'AUT' 'BGD' 'BEL' 'CAN' 'ETH' 'FRA' 'DEU' 'IND' 'ITA' 'JPN' 'KOR'\n 'NLD' 'NGA' 'ESP' 'SWE' 'CHE' 'TUR' 'GBR' 'USA'] ['Australia' 'Austria' 'Bangladesh' 'Belgium' 'Canada' 'Ethiopia' 'France'\n 'Germany' 'India' 'Italy' 'Japan' 'Korea, Rep.' 'Netherlands' 'Nigeria'\n 'Spain' 'Sweden' 'Switzerland' 'Turkey' 'Turkiye' 'United Kingdom'\n 'United 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year life_expectancy dy\n329 Switzerland CHE 2005 81.237 0.149\n330 Switzerland CHE 2006 81.490 0.253\n331 Switzerland CHE 2007 NaN NaN\n332 Switzerland CHE 2008 81.993 NaN\n333 Switzerland CHE 2009 82.044 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gap"}}]}}32{"type":"api-request","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","id":"7e66205b-8b6b-4773-80a6-c67576cf893e","timestamp":"2026-10-30T04:46:22.471Z","version":"2.1.314","querySource":"sdk","shapeHash":"0892a2f21af1cb909ef336153e0be4e6572badc413b435944bb044302b171f19","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":"03d081d7-57bc-4f6c-8629-092a9407c755","keep":8,"tail":["886b933c10a385289bc881f4dd779c5b8ec7a7734c5d398091185ed6de4d47f2","4cb8fc640abdff4610a1af04bfbd01f00c331b17e5707762f08f6fec3523349c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"1f4a522d898dd7ae12cfefb12d01cc4574821393652f6de27856c31d2809fc9b"}33{"parentUuid":"ffe14c54-3aa1-4ecf-b004-a8fa06aabe86","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpBHcbcJtyJgg3W7rDa","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSkAYKEAgTGAI4AUIIdGhpbmtpbmcSDLZN9KWmKxzxM61tqRoMoCqTMLDqlwX/nIbxIjAlbGk7LKvxCG6JN5hbjStwiUi47ZAZ5h0kOtVjqgTR0zpFM+qtZ9sY2YtTc8w+zpMqrQWy6H954Hk9cyFthtS0K+ioW9uacY0w9vwYKvlzB/7Yqz5ZZTX8fNKzifmu2rqbNXehHdlQGfkPVkAVVyCcr1UOsMy1MAHu75XHRN9knX4vqyGBDx898T+prhT2FaC1wKutSorKObg4exZPbMgz62S5PYLFgVXBgNBBcXqleUY6Ly/2B59Zc7xTULW5mmxXvQ6dmdOyLfuvGxBybBEoWttxqfkuZnw3uu18BeLoLzUgeyhTmBAAiSa7b28c7qym9Q/cA9CVZ7K9OdIgiJQD6oczvO/krGLscMWmwXH5yueP+Abn+1eirQM/1mKWeBYgxuaFcIL3yF64Sp3CrYWIlE3yVf1hFtpyjPyJ6QfTO7REGsSmCXcuU8CFiNIk5lE82Og9/xQX0FjNbuBYSbHAG0VB2mr8NcuNPWFLzdwn7yGg5w78HeHSzASAiY1BPb0lsd04I6Qy03hD6DXtaf26NN+uBG/RiJp7z6fb0ojdzzCqRcMnboBWhu+GiR49TARCQxRbdgjMCtIUQnnklD5kjgWdMcSMfdBxsD9a5Lx8NU49VFGubE/sTVOMcEXP8Qv8+FeU/f3gz1IZJDAbyVQ+EDE6hfQsHDtyo8Dfelf/jk8vKeBykA6AlpfsRZ5h6DtTZbmCkr/5eAVxEEbRjcm/7VHUyZ7BuLa1M0RrVjWrnW8l1eejvDKWBbOb1f/w70drrVytpgPLZjcPSYifInqIOUIwczpy0AOUltGXIEnJvo921b/gAb+WCwkf4+xqUtmYSVlbAhXs+tJcG+yErUjbs6gqKCZuSj73GlUaZx52iHr6UyDiczbQJL8MSD9cVsvjV5ROW+/207wXeIYtypsaB/xG3NFCu+dwI81sz0hfhsETraPplUOCszQDA42uk6fNjbzPc+qIyNpCHdkT72ZKGAE="}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":954,"cache_read_input_tokens":9485,"output_tokens":2514,"output_tokens_details":{"thinking_tokens":26},"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":954},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":363,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpBHLyQCknYnCRYEpHD","requestRef":"7e66205b-8b6b-4773-80a6-c67576cf893e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4a0fbb72-e8b1-4d13-83b0-70043f12b3b0","timestamp":"2026-10-30T04:46:23.948Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0156","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","version":"2.1.314","gitBranch":"HEAD"}34{"parentUuid":"4a0fbb72-e8b1-4d13-83b0-70043f12b3b0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpBHcbcJtyJgg3W7rDa","type":"message","role":"assistant","content":[{"type":"text","text":"Data audit is done. Now I'll write the analysis script."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":954,"cache_read_input_tokens":9485,"output_tokens":2514,"output_tokens_details":{"thinking_tokens":26},"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":954},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpBHLyQCknYnCRYEpHD","requestRef":"7e66205b-8b6b-4773-80a6-c67576cf893e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ae77fd17-e0cf-4e1a-ae08-cea0dc1ee48d","timestamp":"2026-10-30T04:46:23.950Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0156","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"ae77fd17-e0cf-4e1a-ae08-cea0dc1ee48d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpBHcbcJtyJgg3W7rDa","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0132o8MRTKYTfQ35KvFagg4w","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-02: balanced-panel mean life expectancy 2000-2022.\nReads only data/life_expectancy_panel.csv; writes out/results.json, out/analytic.csv, out/exclusions.csv.\nDeterministic; no randomness used.\"\"\"\nimport json, os\nimport pandas as pd\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nIN = os.path.join(ROOT, \"data\", \"life_expectancy_panel.csv\")\nOUT = os.path.join(ROOT, \"out\")\nYEARS = list(range(2000, 2023))\n\nraw = pd.read_csv(IN, dtype={\"iso3\": str, \"country_name\": str},\n keep_default_na=False, na_values=[\"\"])\nraw[\"iso3\"] = raw[\"iso3\"].str.strip()\nn_raw = len(raw)\nissues, excl = [], []\n\ndef step(name, rule, before, after, why):\n excl.append(dict(step=name, rule=rule, n_before=before, n_after=after,\n n_removed=before - after, rationale=why))\n\n# 1. Duplicate (iso3, year) rows\nd = raw.copy()\ndup_mask = d.duplicated([\"iso3\", \"year\"], keep=False)\ndups = d[dup_mask]\nfor iso, g in dups.groupby(\"iso3\"):\n conflicting = g.groupby(\"year\")[\"life_expectancy\"].nunique(dropna=False).gt(1).sum()\n if conflicting:\n raise RuntimeError(f\"Conflicting duplicate values for {iso}; manual resolution needed\")\n names = sorted(g[\"country_name\"].unique())\n issues.append({\n \"issue\": f\"{iso} appears twice for each of {g['year'].nunique()} years under names {names} \"\n f\"(assembly of two extractions); duplicate values are identical\",\n \"handling\": \"Kept one row per (iso3, year) (identity is iso3); dropped the duplicate rows; \"\n \"country_name harmonised to the last-listed spelling in the file\"})\n# keep the last occurrence (later, wider extraction) -> name 'Turkiye'\nd = d.drop_duplicates([\"iso3\", \"year\"], keep=\"last\")\n# harmonise country_name per iso3 to the name of its last row\nlast_name = d.groupby(\"iso3\")[\"country_name\"].last()\nd[\"country_name\"] = d[\"iso3\"].map(last_name)\nstep(\"1_dedupe\", \"drop duplicate (iso3, year) rows; keep one per country-year\",\n n_raw, len(d), \"each country contributes at most one observation per year; duplicates had identical values\")\n\n# 2. Missing values\nmiss = d[d[\"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 the balanced set\"})\nn0 = len(d)\nd = d[d[\"life_expectancy\"].notna()]\nstep(\"2_drop_missing\", \"drop rows with empty life_expectancy\", n0, len(d),\n \"no imputation allowed; a country-year without a recorded value is missing\")\n\n# 3. Year range check\nn0 = len(d)\nd = d[d[\"year\"].between(2000, 2022)]\nstep(\"3_year_range\", \"keep years 2000-2022\", n0, len(d), \"study window\")\n\n# 4. Balanced panel\nyrs = d.groupby(\"iso3\")[\"year\"].apply(lambda s: set(s))\nbalanced = sorted([i for i, s in yrs.items() if set(YEARS) <= s])\nall_iso = sorted(raw[\"iso3\"].unique())\nincomplete = [i for i in all_iso if i not in balanced]\nfor i in incomplete:\n have = sorted(yrs.get(i, set()))\n if have and have == list(range(have[0], have[-1] + 1)) and have[0] > 2000:\n issues.append({\"issue\": f\"{i}: rows only for {have[0]}-{have[-1]} (years 2000-{have[0]-1} absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in 2022 all-available baseline)\"})\nn0 = len(d)\na = d[d[\"iso3\"].isin(balanced)].sort_values([\"iso3\", \"year\"]).reset_index(drop=True)\nstep(\"4_balanced_panel\", \"keep countries with a recorded value in every year 2000-2022\", n0, len(a),\n f\"balanced-panel estimand; excluded countries: {', '.join(incomplete)}\")\nassert len(a) == len(balanced) * len(YEARS)\nassert not a.duplicated([\"iso3\", \"year\"]).any()\n\na[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]].to_csv(os.path.join(OUT, \"analytic.csv\"), index=False)\nex = pd.DataFrame(excl)\nex.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\nassert ex[\"n_removed\"].sum() == n_raw - len(a)\n\nm00 = float(a.loc[a.year == 2000, \"life_expectancy\"].mean())\nm22 = float(a.loc[a.year == 2022, \"life_expectancy\"].mean())\navail22 = d[d.year == 2022].drop_duplicates(\"iso3\") # already deduplicated\nm22_all = float(avail22[\"life_expectancy\"].mean())\n\nres = {\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 \"n_analysed\": int(len(a)),\n \"mean_2000_balanced\": round(m00, 6),\n \"mean_2022_balanced\": round(m22, 6),\n \"change_2000_2022_balanced\": round(m22 - m00, 6),\n \"mean_2022_all_available\": round(m22_all, 6),\n \"n_countries_2022_available\": int(avail22[\"iso3\"].nunique()),\n \"incomplete_countries\": incomplete,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"life_expectancy_panel.csv\": int(n_raw)},\n \"random_seed\": None,\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2, ensure_ascii=False)\nprint(json.dumps({k: res[k] for k in [\"n_countries_balanced\", \"n_analysed\", \"mean_2000_balanced\",\n \"mean_2022_balanced\", \"change_2000_2022_balanced\",\n \"mean_2022_all_available\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && cat out/results.json | head -80","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":954,"cache_read_input_tokens":9485,"output_tokens":2514,"output_tokens_details":{"thinking_tokens":26},"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":954},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXpBHLyQCknYnCRYEpHD","requestRef":"7e66205b-8b6b-4773-80a6-c67576cf893e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"52c8c887-7b57-4891-9dd7-371e868cae06","timestamp":"2026-10-30T04:46:42.052Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0156","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","version":"2.1.314","gitBranch":"HEAD"}36{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"52c8c887-7b57-4891-9dd7-371e868cae06","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5"}37{"type":"atis-latch","atis":"","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5"}38{"parentUuid":"52c8c887-7b57-4891-9dd7-371e868cae06","isSidechain":false,"promptId":"c10f33de-72c4-40b2-98ba-bcc768d7e713","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0132o8MRTKYTfQ35KvFagg4w","type":"tool_result","content":"{\n \"n_countries_balanced\": 15,\n \"n_analysed\": 345,\n \"mean_2000_balanced\": 78.060267,\n \"mean_2022_balanced\": 81.553067,\n \"change_2000_2022_balanced\": 3.4928,\n \"mean_2022_all_available\": 78.692\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1_dedupe,\"drop duplicate (iso3, year) rows; keep one per country-year\",439,416,23,each country contributes at most one observation per year; duplicates had identical values\n2_drop_missing,drop rows with empty life_expectancy,416,415,1,no imputation allowed; a country-year without a recorded value is missing\n3_year_range,keep years 2000-2022,415,415,0,study window\n4_balanced_panel,keep countries with a recorded value in every year 2000-2022,415,345,70,\"balanced-panel estimand; excluded countries: BGD, CHE, ETH, IND, NGA\"\n{\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": 20,\n \"n_countries_balanced\": 15,\n \"n_countries_incomplete\": 5,\n \"balanced_countries\": [\n \"AUS\",\n \"AUT\",\n \"BEL\",\n \"CAN\",\n \"DEU\",\n \"ESP\",\n \"FRA\",\n \"GBR\",\n \"ITA\",\n \"JPN\",\n \"KOR\",\n \"NLD\",\n \"SWE\",\n \"TUR\",\n \"USA\"\n ],\n \"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 \"incomplete_countries\": [\n \"BGD\",\n \"CHE\",\n \"ETH\",\n \"IND\",\n \"NGA\"\n ],\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"TUR appears twice for each of 23 years under names ['Turkey', 'Turkiye'] (assembly of two extractions); duplicate values are identical\",\n \"handling\": \"Kept one row per (iso3, year) (identity is iso3); dropped the duplicate rows; country_name harmonised to the last-listed spelling in the file\"\n },\n {\n \"issue\": \"CHE 2007: empty life_expectancy cell\",\n \"handling\": \"Treated as missing (no imputation); country excluded from the balanced set\"\n },\n {\n \"issue\": \"BGD: rows only for 2011-2022 (years 2000-2010 absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in 2022 all-available baseline)\"\n },\n {\n \"issue\": \"ETH: rows only for 2011-2022 (years 2000-2010 absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in 2022 all-available baseline)\"\n },\n {\n \"issue\": \"IND: rows only for 2011-2022 (years 2000-2010 absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in 2022 all-available baseline)\"\n },\n {\n \"issue\": \"NGA: rows only for 2011-2022 (years 2000-2010 absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in 2022 all-available baseline)\"\n }\n ],\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null\n}","is_error":false}]},"uuid":"3c0493da-51f1-46e4-831b-33a1a0c58c9d","timestamp":"2026-10-30T04:46:42.283Z","toolUseResult":{"stdout":"{\n \"n_countries_balanced\": 15,\n \"n_analysed\": 345,\n \"mean_2000_balanced\": 78.060267,\n \"mean_2022_balanced\": 81.553067,\n \"change_2000_2022_balanced\": 3.4928,\n \"mean_2022_all_available\": 78.692\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1_dedupe,\"drop duplicate (iso3, year) rows; keep one per country-year\",439,416,23,each country contributes at most one observation per year; duplicates had identical values\n2_drop_missing,drop rows with empty life_expectancy,416,415,1,no imputation allowed; a country-year without a recorded value is missing\n3_year_range,keep years 2000-2022,415,415,0,study window\n4_balanced_panel,keep countries with a recorded value in every year 2000-2022,415,345,70,\"balanced-panel estimand; excluded countries: BGD, CHE, ETH, IND, NGA\"\n{\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": 20,\n \"n_countries_balanced\": 15,\n \"n_countries_incomplete\": 5,\n \"balanced_countries\": [\n \"AUS\",\n \"AUT\",\n \"BEL\",\n \"CAN\",\n \"DEU\",\n \"ESP\",\n \"FRA\",\n \"GBR\",\n \"ITA\",\n \"JPN\",\n \"KOR\",\n \"NLD\",\n \"SWE\",\n \"TUR\",\n \"USA\"\n ],\n \"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 \"incomplete_countries\": [\n \"BGD\",\n \"CHE\",\n \"ETH\",\n \"IND\",\n \"NGA\"\n ],\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"TUR appears twice for each of 23 years under names ['Turkey', 'Turkiye'] (assembly of two extractions); duplicate values are identical\",\n \"handling\": \"Kept one row per (iso3, year) (identity is iso3); dropped the duplicate rows; country_name harmonised to the last-listed spelling in the file\"\n },\n {\n \"issue\": \"CHE 2007: empty life_expectancy cell\",\n \"handling\": \"Treated as missing (no imputation); country excluded from the balanced set\"\n },\n {\n \"issue\": \"BGD: rows only for 2011-2022 (years 2000-2010 absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in 2022 all-available baseline)\"\n },\n {\n \"issue\": \"ETH: rows only for 2011-2022 (years 2000-2010 absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in 2022 all-available baseline)\"\n },\n {\n \"issue\": \"IND: rows only for 2011-2022 (years 2000-2010 absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in 2022 all-available baseline)\"\n },\n {\n \"issue\": \"NGA: rows only for 2011-2022 (years 2000-2010 absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in 2022 all-available baseline)\"\n }\n ],\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"52c8c887-7b57-4891-9dd7-371e868cae06","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0156","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","version":"2.1.314","gitBranch":"HEAD"}39{"type":"api-request-blob","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","hash":"94f73f7480b12d42e8a20878fb5e0f01f0b45d12ac4040c972d1f179ce07977f","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Data audit is done. Now I'll write the analysis script."},{"type":"tool_use","id":"toolu_0132o8MRTKYTfQ35KvFagg4w","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-02: balanced-panel mean life expectancy 2000-2022.\nReads only data/life_expectancy_panel.csv; writes out/results.json, out/analytic.csv, out/exclusions.csv.\nDeterministic; no randomness used.\"\"\"\nimport json, os\nimport pandas as pd\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nIN = os.path.join(ROOT, \"data\", \"life_expectancy_panel.csv\")\nOUT = os.path.join(ROOT, \"out\")\nYEARS = list(range(2000, 2023))\n\nraw = pd.read_csv(IN, dtype={\"iso3\": str, \"country_name\": str},\n keep_default_na=False, na_values=[\"\"])\nraw[\"iso3\"] = raw[\"iso3\"].str.strip()\nn_raw = len(raw)\nissues, excl = [], []\n\ndef step(name, rule, before, after, why):\n excl.append(dict(step=name, rule=rule, n_before=before, n_after=after,\n n_removed=before - after, rationale=why))\n\n# 1. Duplicate (iso3, year) rows\nd = raw.copy()\ndup_mask = d.duplicated([\"iso3\", \"year\"], keep=False)\ndups = d[dup_mask]\nfor iso, g in dups.groupby(\"iso3\"):\n conflicting = g.groupby(\"year\")[\"life_expectancy\"].nunique(dropna=False).gt(1).sum()\n if conflicting:\n raise RuntimeError(f\"Conflicting duplicate values for {iso}; manual resolution needed\")\n names = sorted(g[\"country_name\"].unique())\n issues.append({\n \"issue\": f\"{iso} appears twice for each of {g['year'].nunique()} years under names {names} \"\n f\"(assembly of two extractions); duplicate values are identical\",\n \"handling\": \"Kept one row per (iso3, year) (identity is iso3); dropped the duplicate rows; \"\n \"country_name harmonised to the last-listed spelling in the file\"})\n# keep the last occurrence (later, wider extraction) -> name 'Turkiye'\nd = d.drop_duplicates([\"iso3\", \"year\"], keep=\"last\")\n# harmonise country_name per iso3 to the name of its last row\nlast_name = d.groupby(\"iso3\")[\"country_name\"].last()\nd[\"country_name\"] = d[\"iso3\"].map(last_name)\nstep(\"1_dedupe\", \"drop duplicate (iso3, year) rows; keep one per country-year\",\n n_raw, len(d), \"each country contributes at most one observation per year; duplicates had identical values\")\n\n# 2. Missing values\nmiss = d[d[\"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 the balanced set\"})\nn0 = len(d)\nd = d[d[\"life_expectancy\"].notna()]\nstep(\"2_drop_missing\", \"drop rows with empty life_expectancy\", n0, len(d),\n \"no imputation allowed; a country-year without a recorded value is missing\")\n\n# 3. Year range check\nn0 = len(d)\nd = d[d[\"year\"].between(2000, 2022)]\nstep(\"3_year_range\", \"keep years 2000-2022\", n0, len(d), \"study window\")\n\n# 4. Balanced panel\nyrs = d.groupby(\"iso3\")[\"year\"].apply(lambda s: set(s))\nbalanced = sorted([i for i, s in yrs.items() if set(YEARS) <= s])\nall_iso = sorted(raw[\"iso3\"].unique())\nincomplete = [i for i in all_iso if i not in balanced]\nfor i in incomplete:\n have = sorted(yrs.get(i, set()))\n if have and have == list(range(have[0], have[-1] + 1)) and have[0] > 2000:\n issues.append({\"issue\": f\"{i}: rows only for {have[0]}-{have[-1]} (years 2000-{have[0]-1} absent from file)\",\n \"handling\": \"Not imputed; country excluded from the balanced set (still counted in 2022 all-available baseline)\"})\nn0 = len(d)\na = d[d[\"iso3\"].isin(balanced)].sort_values([\"iso3\", \"year\"]).reset_index(drop=True)\nstep(\"4_balanced_panel\", \"keep countries with a recorded value in every year 2000-2022\", n0, len(a),\n f\"balanced-panel estimand; excluded countries: {', '.join(incomplete)}\")\nassert len(a) == len(balanced) * len(YEARS)\nassert not a.duplicated([\"iso3\", \"year\"]).any()\n\na[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]].to_csv(os.path.join(OUT, \"analytic.csv\"), index=False)\nex = pd.DataFrame(excl)\nex.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\nassert ex[\"n_removed\"].sum() == n_raw - len(a)\n\nm00 = float(a.loc[a.year == 2000, \"life_expectancy\"].mean())\nm22 = float(a.loc[a.year == 2022, \"life_expectancy\"].mean())\navail22 = d[d.year == 2022].drop_duplicates(\"iso3\") # already deduplicated\nm22_all = float(avail22[\"life_expectancy\"].mean())\n\nres = {\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 \"n_analysed\": int(len(a)),\n \"mean_2000_balanced\": round(m00, 6),\n \"mean_2022_balanced\": round(m22, 6),\n \"change_2000_2022_balanced\": round(m22 - m00, 6),\n \"mean_2022_all_available\": round(m22_all, 6),\n \"n_countries_2022_available\": int(avail22[\"iso3\"].nunique()),\n \"incomplete_countries\": incomplete,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"life_expectancy_panel.csv\": int(n_raw)},\n \"random_seed\": None,\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2, ensure_ascii=False)\nprint(json.dumps({k: res[k] for k in [\"n_countries_balanced\", \"n_analysed\", \"mean_2000_balanced\",\n \"mean_2022_balanced\", \"change_2000_2022_balanced\",\n \"mean_2022_all_available\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && cat out/results.json | head -80","description":"Write and run analysis script"}}]}}40{"type":"api-request","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","id":"e5d9cd59-c3bd-4b51-bf41-5916e7b5003e","timestamp":"2026-10-30T04:46:42.286Z","version":"2.1.314","querySource":"sdk","shapeHash":"0892a2f21af1cb909ef336153e0be4e6572badc413b435944bb044302b171f19","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":"7e66205b-8b6b-4773-80a6-c67576cf893e","keep":10,"tail":["94f73f7480b12d42e8a20878fb5e0f01f0b45d12ac4040c972d1f179ce07977f","19f4a0c5bf2b2c235b53a4e7e74975068a56c4e92df5f47854a0d0a858315b33"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"7132e3fe75cf6c4eb9afca2b7a13afb0e678770635178221b893838ade5efc68"}41{"parentUuid":"3c0493da-51f1-46e4-831b-33a1a0c58c9d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpCkPwW3wyRq9xFP5kN","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":3786,"cache_read_input_tokens":10439,"output_tokens":1339,"output_tokens_details":{"thinking_tokens":241},"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":3786},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2664,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpCkC2Vypk6RScAmG54","requestRef":"e5d9cd59-c3bd-4b51-bf41-5916e7b5003e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"50be8669-119c-4188-8a05-a7a01079f904","timestamp":"2026-10-30T04:46:46.270Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0156","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","version":"2.1.314","gitBranch":"HEAD"}42{"parentUuid":"50be8669-119c-4188-8a05-a7a01079f904","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpCkPwW3wyRq9xFP5kN","type":"message","role":"assistant","content":[{"type":"text","text":"Now I'll add a few descriptive keys for reporting, then check that a rerun gives identical output."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3786,"cache_read_input_tokens":10439,"output_tokens":1339,"output_tokens_details":{"thinking_tokens":241},"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":3786},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpCkC2Vypk6RScAmG54","requestRef":"e5d9cd59-c3bd-4b51-bf41-5916e7b5003e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2a505e5e-609d-4673-a743-02e072de78fd","timestamp":"2026-10-30T04:46:46.273Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0156","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","version":"2.1.314","gitBranch":"HEAD"}43{"parentUuid":"2a505e5e-609d-4673-a743-02e072de78fd","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpCkPwW3wyRq9xFP5kN","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LqacAEg4W7dno83fU1Ebnh","name":"Bash","input":{"command":"cd out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace('''res = {''','''chg = (a[a.year == 2022].set_index(\"iso3\")[\"life_expectancy\"]\n - a[a.year == 2000].set_index(\"iso3\")[\"life_expectancy\"])\nres = {''')\ns=s.replace(''' \"incomplete_countries\": incomplete,''',''' \"incomplete_countries\": incomplete,\n \"gap_2022_all_available_minus_balanced\": round(m22_all - m22, 6),\n \"per_country_change_min\": round(float(chg.min()), 6),\n \"per_country_change_min_iso3\": str(chg.idxmin()),\n \"per_country_change_max\": round(float(chg.max()), 6),\n \"per_country_change_max_iso3\": str(chg.idxmax()),\n \"per_country_change_sd\": round(float(chg.std(ddof=1)), 6),''')\nopen('analysis.py','w').write(s)\nEOF\ncd .. && rm -f out/results.json out/analytic.csv out/exclusions.csv && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cp out/results.json /tmp/r1.json && md5 -q out/analytic.csv out/exclusions.csv > /tmp/h1 && rm out/results.json out/analytic.csv out/exclusions.csv && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r1.json && md5 -q out/analytic.csv out/exclusions.csv | diff - /tmp/h1 && echo IDENTICAL; grep -A1 -E 'gap|per_country' out/results.json; wc -l out/analytic.csv\n~/wp29/venv/bin/python -c \"\nimport pandas as pd,json\nr=json.load(open('out/results.json'));a=pd.read_csv('out/analytic.csv');e=pd.read_csv('out/exclusions.csv')\nprint(len(a)==r['n_analysed'], not a.duplicated(['iso3','year']).any(), a.life_expectancy.notna().all())\nprint((e.n_before-e.n_removed==e.n_after).all(), (e.n_before.iloc[1:].values==e.n_after.iloc[:-1].values).all(), e.n_before.iloc[0]==439, e.n_after.iloc[-1]==r['n_analysed'])\nprint(abs(r['change_2000_2022_balanced']-(r['mean_2022_balanced']-r['mean_2000_balanced']))<1e-9, r['n_countries_balanced']+r['n_countries_incomplete']==r['n_countries_total'])\nprint(sorted(a.iso3.unique())==r['balanced_countries'], a.groupby('iso3').year.nunique().eq(23).all())\n\"","description":"Add descriptive keys, test reproducibility and consistency"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3786,"cache_read_input_tokens":10439,"output_tokens":1339,"output_tokens_details":{"thinking_tokens":241},"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":3786},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXpCkC2Vypk6RScAmG54","requestRef":"e5d9cd59-c3bd-4b51-bf41-5916e7b5003e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"bb43996f-e2bd-4f01-a252-a75c0ac7be0f","timestamp":"2026-10-30T04:46:53.652Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0156","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","version":"2.1.314","gitBranch":"HEAD"}44{"parentUuid":"bb43996f-e2bd-4f01-a252-a75c0ac7be0f","isSidechain":false,"promptId":"c10f33de-72c4-40b2-98ba-bcc768d7e713","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LqacAEg4W7dno83fU1Ebnh","type":"tool_result","content":"IDENTICAL\n \"gap_2022_all_available_minus_balanced\": -2.861067,\n \"per_country_change_min\": 0.797,\n \"per_country_change_min_iso3\": \"USA\",\n \"per_country_change_max\": 6.77,\n \"per_country_change_max_iso3\": \"KOR\",\n \"per_country_change_sd\": 1.408806,\n \"feasibility\": \"complete\",\n 346 out/analytic.csv\nTrue True True\nTrue True True True\nTrue True\nTrue True","is_error":false}]},"uuid":"5827d30f-6acc-4de3-b493-5325081cafd0","timestamp":"2026-10-30T04:46:54.274Z","toolUseResult":{"stdout":"IDENTICAL\n \"gap_2022_all_available_minus_balanced\": -2.861067,\n \"per_country_change_min\": 0.797,\n \"per_country_change_min_iso3\": \"USA\",\n \"per_country_change_max\": 6.77,\n \"per_country_change_max_iso3\": \"KOR\",\n \"per_country_change_sd\": 1.408806,\n \"feasibility\": \"complete\",\n 346 out/analytic.csv\nTrue True True\nTrue True True True\nTrue True\nTrue True","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"bb43996f-e2bd-4f01-a252-a75c0ac7be0f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0156","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","version":"2.1.314","gitBranch":"HEAD"}45{"type":"api-request-blob","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","hash":"900cd8b1dba66e51e6c387102d053a85a12171fb4766341f3787d9a7503ec76e","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Now I'll add a few descriptive keys for reporting, then check that a rerun gives identical output."},{"type":"tool_use","id":"toolu_01LqacAEg4W7dno83fU1Ebnh","name":"Bash","input":{"command":"cd out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace('''res = {''','''chg = (a[a.year == 2022].set_index(\"iso3\")[\"life_expectancy\"]\n - a[a.year == 2000].set_index(\"iso3\")[\"life_expectancy\"])\nres = {''')\ns=s.replace(''' \"incomplete_countries\": incomplete,''',''' \"incomplete_countries\": incomplete,\n \"gap_2022_all_available_minus_balanced\": round(m22_all - m22, 6),\n \"per_country_change_min\": round(float(chg.min()), 6),\n \"per_country_change_min_iso3\": str(chg.idxmin()),\n \"per_country_change_max\": round(float(chg.max()), 6),\n \"per_country_change_max_iso3\": str(chg.idxmax()),\n \"per_country_change_sd\": round(float(chg.std(ddof=1)), 6),''')\nopen('analysis.py','w').write(s)\nEOF\ncd .. && rm -f out/results.json out/analytic.csv out/exclusions.csv && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cp out/results.json /tmp/r1.json && md5 -q out/analytic.csv out/exclusions.csv > /tmp/h1 && rm out/results.json out/analytic.csv out/exclusions.csv && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r1.json && md5 -q out/analytic.csv out/exclusions.csv | diff - /tmp/h1 && echo IDENTICAL; grep -A1 -E 'gap|per_country' out/results.json; wc -l out/analytic.csv\n~/wp29/venv/bin/python -c \"\nimport pandas as pd,json\nr=json.load(open('out/results.json'));a=pd.read_csv('out/analytic.csv');e=pd.read_csv('out/exclusions.csv')\nprint(len(a)==r['n_analysed'], not a.duplicated(['iso3','year']).any(), a.life_expectancy.notna().all())\nprint((e.n_before-e.n_removed==e.n_after).all(), (e.n_before.iloc[1:].values==e.n_after.iloc[:-1].values).all(), e.n_before.iloc[0]==439, e.n_after.iloc[-1]==r['n_analysed'])\nprint(abs(r['change_2000_2022_balanced']-(r['mean_2022_balanced']-r['mean_2000_balanced']))<1e-9, r['n_countries_balanced']+r['n_countries_incomplete']==r['n_countries_total'])\nprint(sorted(a.iso3.unique())==r['balanced_countries'], a.groupby('iso3').year.nunique().eq(23).all())\n\"","description":"Add descriptive keys, test reproducibility and consistency"}}]}}46{"type":"api-request","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","id":"0f502357-265c-4106-b81e-a6f93c5b47ff","timestamp":"2026-10-30T04:46:54.277Z","version":"2.1.314","querySource":"sdk","shapeHash":"0892a2f21af1cb909ef336153e0be4e6572badc413b435944bb044302b171f19","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":"e5d9cd59-c3bd-4b51-bf41-5916e7b5003e","keep":12,"tail":["900cd8b1dba66e51e6c387102d053a85a12171fb4766341f3787d9a7503ec76e","f873caf97f59d3bf0c6b58c63f29d878bdc55785a560dc677701944db3078617"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"067f7bd1699745b7f4d678ff2cc16d082b661b7c72c67c17c4681691f34f012b"}47{"parentUuid":"5827d30f-6acc-4de3-b493-5325081cafd0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpDdcBdakKyr8EAr25J","type":"message","role":"assistant","content":[{"type":"text","text":"Every check passed. Now I'll write the state files, report and claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1505,"cache_read_input_tokens":14225,"output_tokens":4514,"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":1505},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpDdLJyng8iA4zV6jGa","requestRef":"0f502357-265c-4106-b81e-a6f93c5b47ff","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3f1905d5-a4ed-4fae-a11f-b0b5799fabe4","timestamp":"2026-10-30T04:46:55.454Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0156","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","version":"2.1.314","gitBranch":"HEAD"}48{"parentUuid":"3f1905d5-a4ed-4fae-a11f-b0b5799fabe4","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpDdcBdakKyr8EAr25J","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NyCAXWGDdWqf9KVKooWrS2","name":"Bash","input":{"command":"cd out && cat > state/contract.md <<'EOF'\n# Contract — F1-conf-02\n**Question:** How did life expectancy at birth (WDI SP.DYN.LE00.IN) change from 2000 to 2022 across a fixed set of countries?\n**Estimands:** For each year, the unweighted mean of life expectancy over the countries (identified by iso3) that have a recorded value in every year 2000–2022, each counted once. Main quantities: balanced mean 2000, balanced mean 2022, their difference, and the balanced set's composition. Descriptive baseline: unweighted 2022 mean over all distinct countries with a recorded 2022 value. Meaningful as descriptive means of a fixed set of countries. They are not population-weighted, and they are not representative of any wider population.\n**Outputs:** out/results.json (task_id, n_countries_total, n_countries_balanced, n_countries_incomplete, balanced_countries, n_analysed, mean_2000_balanced, mean_2022_balanced, change_2000_2022_balanced, mean_2022_all_available, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed); out/analytic.csv; out/exclusions.csv; out/report.md; out/analysis.py; out/claims.json.\n**Information rules:** use only the CSV. Do not impute, interpolate or look up values. Empty cell = missing. A country is identified by iso3, with at most one observation per year.\n**Invalidity:** double-counting duplicate country-years; imputing gaps; conflicting duplicates that cannot be resolved; an empty balanced set.\nEOF\ncat > state/data_audit.md <<'EOF'\n# Data audit\n- `data/life_expectancy_panel.csv` SHA-256 b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac; 439 data rows, 4 columns (country_name str, iso3 str, year int, life_expectancy float, years). Years 2000–2022. 20 distinct iso3 codes, 21 distinct names.\n- `data/README.md` SHA-256 e9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0 (provenance only).\n- Value range 51.49–84.56 years (plausible). Year-over-year jumps over 1.5 years occur only around 2020–2022 (COVID; e.g. IND, USA, ESP), so they are plausible and kept.\n- Anomaly 1: TUR has 46 rows, each year appearing as both \"Turkey\" and \"Turkiye\" with identical values. This is an assembly duplicate. Deduplicated on (iso3, year), keeping the last row (\"Turkiye\").\n- Anomaly 2: CHE 2007 has an empty life_expectancy cell → missing, not imputed. CHE is therefore not balanced.\n- Anomaly 3: BGD, ETH, IND and NGA have rows only for 2011–2022 (likely the narrower extraction) → not balanced.\n- No other duplicates, no whitespace in codes, no out-of-range years.\n- Information-availability: no input violates the rules. Only the CSV is used.\nEOF\ncat > state/checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (deleted outputs, ran twice): results.json byte-identical, analytic.csv/exclusions.csv md5 identical | PASS |\n| analytic.csv rows == n_analysed (345) and no duplicate (iso3, year) and no missing values | PASS |\n| every balanced country has 23 distinct years; set equals balanced_countries | PASS |\n| exclusion ledger: n_before − n_removed = n_after on each row; chaining; first n_before = 439; last n_after = 345 | PASS |\n| change = mean_2022 − mean_2000 | PASS |\n| n_balanced + n_incomplete = n_total (15+5=20) | PASS |\n| Magnitudes: means 78–82 years are plausible. The 2022 baseline (78.69) is below the balanced mean (81.55) because the baseline includes 4 lower-life-expectancy countries | PASS |\n| Duplicates for TUR have identical values (script raises an error on conflict) | PASS |\nEOF\ncat > state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0: read TASK.md, wrote contract.\n- 2026-10-30 P1: audit done. Decisions: dedupe TUR on (iso3, year) (identical values); CHE 2007 missing → CHE excluded; BGD/ETH/IND/NGA start 2011 → excluded.\n- 2026-10-30 P2: baseline mean_2022_all_available = 78.692 (20 countries).\n- 2026-10-30 P3: analysis.py written; added descriptive keys (gap, per-country change range/sd).\n- 2026-10-30 P4: reproducibility and consistency checks all pass.\n- 2026-10-30 P5: report and claims written.\nEOF\ncat > report.md <<'EOF'\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Question\nHow much did life expectancy at birth (WDI `SP.DYN.LE00.IN`) change between 2000 and 2022? The comparison is made over a fixed set of countries, namely those with a recorded value in every year from 2000 to 2022. The estimand is the unweighted mean across those countries.\n\n## Data and exclusions\n- Input: `data/life_expectancy_panel.csv`, 439 data rows (`n_input_rows`), 20 distinct iso3 countries (`n_countries_total`).\n- Data problems found (`data_issues`):\n - Türkiye (TUR) appears twice for all 23 years, once as \"Turkey\" and once as \"Turkiye\", with identical values. I kept one row per (iso3, year), which removes 23 rows.\n - Switzerland (CHE) has an empty value for 2007. This is treated as missing and not imputed.\n - Bangladesh, Ethiopia, India and Nigeria have rows only for 2011–2022.\n- Exclusion ledger (`out/exclusions.csv`): 439 → 416 after deduplication → 415 after dropping the empty cell → 415 after the year-window check → 345 after keeping only balanced countries (`n_analysed`).\n- Balanced set: 15 countries (`n_countries_balanced`, `balanced_countries`): AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA.\n- Excluded: 5 countries (`n_countries_incomplete`, `incomplete_countries`): BGD, CHE, ETH, IND, NGA.\n\n## Method\nEach step was applied in order:\n1. Deduplicate on (iso3, year), checking first that the duplicate values are identical.\n2. Drop empty values.\n3. Keep only the countries whose recorded years cover all of 2000–2022.\n4. For each year, take the simple mean over those countries.\n\nThe baseline is the simple mean over all distinct countries with a recorded 2022 value (`n_countries_2022_available` = 20). No randomness is used (`random_seed` = null). `out/analysis.py` regenerates all outputs deterministically.\n\n## Results\n- Balanced mean in 2000: **78.060 years** (`mean_2000_balanced`).\n- Balanced mean in 2022: **81.553 years** (`mean_2022_balanced`).\n- Change from 2000 to 2022: **+3.493 years** (`change_2000_2022_balanced`).\n- Baseline 2022 mean over all 20 countries: 78.692 years (`mean_2022_all_available`). This is 2.861 years lower than the balanced 2022 mean (`gap_2022_all_available_minus_balanced`). The gap reflects composition: the baseline includes four lower-life-expectancy countries that are absent from the balanced set. It does not reflect any difference in time trends.\n- Change by country within the balanced set: from +0.797 years for USA (`per_country_change_min`, `per_country_change_min_iso3`) to +6.770 years for KOR (`per_country_change_max`, `per_country_change_max_iso3`).\n\n## Uncertainty\nThe estimands are exact descriptive means for a fixed, fully observed set of countries, so there is no sampling uncertainty to report and no confidence interval is given. Countries differ in how much life expectancy changed: the standard deviation of the per-country change is 1.409 years (`per_country_change_sd`). The WDI values are themselves modelled estimates that may be revised, and that uncertainty is not quantified here.\n\n## Limitations and open issues\n- The balanced set is mostly high-income countries; Türkiye is the only middle-income country left. All four low- and lower-middle-income countries drop out, and so does Switzerland, because of a single missing year (2007). The +3.49-year change therefore should not be read as a global or representative figure.\n- The means are unweighted, not population-weighted.\n- I assumed the missing CHE 2007 value and the missing 2000–2010 years for BGD/ETH/IND/NGA are file-assembly gaps; the README notes possible assembly errors. As the task requires, they were not filled from any outside source.\n- For TUR, the country name was harmonised to \"Turkiye\". The values were unaffected.\n- Feasibility: complete (`feasibility`).\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The balanced panel contains 15 of the 20 countries (345 country-years); BGD, CHE, ETH, IND and NGA are excluded as incomplete.\",\"results_keys\":[\"n_countries_balanced\",\"n_countries_total\",\"n_countries_incomplete\",\"balanced_countries\",\"incomplete_countries\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"CHE is excluded because of a single empty cell (2007).\"},\n {\"id\":\"C2\",\"text\":\"Balanced-panel mean life expectancy rose from 78.060 years in 2000 to 81.553 years in 2022, a change of +3.493 years.\",\"results_keys\":[\"mean_2000_balanced\",\"mean_2022_balanced\",\"change_2000_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Unweighted mean over a mostly high-income set; not representative globally.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries (78.692) is 2.861 years below the balanced 2022 mean, because of composition differences.\",\"results_keys\":[\"mean_2022_all_available\",\"gap_2022_all_available_minus_balanced\",\"n_countries_2022_available\"],\"status\":\"supported\",\"caveat\":\"Descriptive baseline only; not the estimand.\"},\n {\"id\":\"C4\",\"text\":\"The TUR rows were duplicated under two names with identical values; deduplicating on (iso3, year) removed 23 rows.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"Country name harmonised to 'Turkiye'.\"},\n {\"id\":\"C5\",\"text\":\"Per-country changes within the balanced set range from +0.797 (USA) to +6.770 (KOR) years, with an SD of 1.409.\",\"results_keys\":[\"per_country_change_min\",\"per_country_change_min_iso3\",\"per_country_change_max\",\"per_country_change_max_iso3\",\"per_country_change_sd\"],\"status\":\"supported\",\"caveat\":\"Describes dispersion across countries; not sampling uncertainty.\"}\n]\nEOF\ncat > state/phases.json <<'EOF'\n{\n \"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 recorded\",\"schema/missing/duplicates audited\",\"exclusion ledger sums verified (439-94=345)\",\"no availability-rule violations\"],\"notes\":\"TUR duplicates, CHE 2007 missing, 4 countries start 2011\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"baseline mean_2022_all_available computed = 78.692 over 20 countries\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, no seed needed\"],\"notes\":\"\"},\n \"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical results.json and identical csv hashes\",\"consistency checks in checks.md all PASS\"],\"notes\":\"\"},\n \"P5\":{\"status\":\"passed\",\"checks\":[\"report.md has all 6 sections, quantitative statements cite keys\",\"claims.json keys exist in results.json\"],\"notes\":\"\"}\n}\nEOF\ncd .. && ~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'))\nfor c in json.load(open('out/claims.json')):\n assert all(k in r for k in c['results_keys']),c['id']\nprint('claims ok')\"; ls out","description":"Write state files, report, claims"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1505,"cache_read_input_tokens":14225,"output_tokens":4514,"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":1505},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpDdLJyng8iA4zV6jGa","requestRef":"0f502357-265c-4106-b81e-a6f93c5b47ff","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d14ce80e-dc99-4d65-9793-a682e4159250","timestamp":"2026-10-30T04:47:31.395Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0156","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","version":"2.1.314","gitBranch":"HEAD"}49{"parentUuid":"d14ce80e-dc99-4d65-9793-a682e4159250","isSidechain":false,"promptId":"c10f33de-72c4-40b2-98ba-bcc768d7e713","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NyCAXWGDdWqf9KVKooWrS2","type":"tool_result","content":"claims ok\nanalysis.py\nanalytic.csv\nclaims.json\nexclusions.csv\nreport.md\nresults.json\nstate","is_error":false}]},"uuid":"2b93cf31-3f6f-45ab-93fb-dcc75a428264","timestamp":"2026-10-30T04:47:31.481Z","toolUseResult":{"stdout":"claims ok\nanalysis.py\nanalytic.csv\nclaims.json\nexclusions.csv\nreport.md\nresults.json\nstate","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":true},"sandboxed":false,"sourceToolAssistantUUID":"d14ce80e-dc99-4d65-9793-a682e4159250","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0156","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","version":"2.1.314","gitBranch":"HEAD"}50{"type":"api-request-blob","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","hash":"a5c7f5399b6d45896f9e8777e9e5f792e97a41bebc165851497147ff4dffea81","message":{"role":"assistant","content":[{"type":"text","text":"Every check passed. Now I'll write the state files, report and claims."},{"type":"tool_use","id":"toolu_01NyCAXWGDdWqf9KVKooWrS2","name":"Bash","input":{"command":"cd out && cat > state/contract.md <<'EOF'\n# Contract — F1-conf-02\n**Question:** How did life expectancy at birth (WDI SP.DYN.LE00.IN) change from 2000 to 2022 across a fixed set of countries?\n**Estimands:** For each year, the unweighted mean of life expectancy over the countries (identified by iso3) that have a recorded value in every year 2000–2022, each counted once. Main quantities: balanced mean 2000, balanced mean 2022, their difference, and the balanced set's composition. Descriptive baseline: unweighted 2022 mean over all distinct countries with a recorded 2022 value. Meaningful as descriptive means of a fixed set of countries. They are not population-weighted, and they are not representative of any wider population.\n**Outputs:** out/results.json (task_id, n_countries_total, n_countries_balanced, n_countries_incomplete, balanced_countries, n_analysed, mean_2000_balanced, mean_2022_balanced, change_2000_2022_balanced, mean_2022_all_available, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed); out/analytic.csv; out/exclusions.csv; out/report.md; out/analysis.py; out/claims.json.\n**Information rules:** use only the CSV. Do not impute, interpolate or look up values. Empty cell = missing. A country is identified by iso3, with at most one observation per year.\n**Invalidity:** double-counting duplicate country-years; imputing gaps; conflicting duplicates that cannot be resolved; an empty balanced set.\nEOF\ncat > state/data_audit.md <<'EOF'\n# Data audit\n- `data/life_expectancy_panel.csv` SHA-256 b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac; 439 data rows, 4 columns (country_name str, iso3 str, year int, life_expectancy float, years). Years 2000–2022. 20 distinct iso3 codes, 21 distinct names.\n- `data/README.md` SHA-256 e9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0 (provenance only).\n- Value range 51.49–84.56 years (plausible). Year-over-year jumps over 1.5 years occur only around 2020–2022 (COVID; e.g. IND, USA, ESP), so they are plausible and kept.\n- Anomaly 1: TUR has 46 rows, each year appearing as both \"Turkey\" and \"Turkiye\" with identical values. This is an assembly duplicate. Deduplicated on (iso3, year), keeping the last row (\"Turkiye\").\n- Anomaly 2: CHE 2007 has an empty life_expectancy cell → missing, not imputed. CHE is therefore not balanced.\n- Anomaly 3: BGD, ETH, IND and NGA have rows only for 2011–2022 (likely the narrower extraction) → not balanced.\n- No other duplicates, no whitespace in codes, no out-of-range years.\n- Information-availability: no input violates the rules. Only the CSV is used.\nEOF\ncat > state/checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (deleted outputs, ran twice): results.json byte-identical, analytic.csv/exclusions.csv md5 identical | PASS |\n| analytic.csv rows == n_analysed (345) and no duplicate (iso3, year) and no missing values | PASS |\n| every balanced country has 23 distinct years; set equals balanced_countries | PASS |\n| exclusion ledger: n_before − n_removed = n_after on each row; chaining; first n_before = 439; last n_after = 345 | PASS |\n| change = mean_2022 − mean_2000 | PASS |\n| n_balanced + n_incomplete = n_total (15+5=20) | PASS |\n| Magnitudes: means 78–82 years are plausible. The 2022 baseline (78.69) is below the balanced mean (81.55) because the baseline includes 4 lower-life-expectancy countries | PASS |\n| Duplicates for TUR have identical values (script raises an error on conflict) | PASS |\nEOF\ncat > state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0: read TASK.md, wrote contract.\n- 2026-10-30 P1: audit done. Decisions: dedupe TUR on (iso3, year) (identical values); CHE 2007 missing → CHE excluded; BGD/ETH/IND/NGA start 2011 → excluded.\n- 2026-10-30 P2: baseline mean_2022_all_available = 78.692 (20 countries).\n- 2026-10-30 P3: analysis.py written; added descriptive keys (gap, per-country change range/sd).\n- 2026-10-30 P4: reproducibility and consistency checks all pass.\n- 2026-10-30 P5: report and claims written.\nEOF\ncat > report.md <<'EOF'\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Question\nHow much did life expectancy at birth (WDI `SP.DYN.LE00.IN`) change between 2000 and 2022? The comparison is made over a fixed set of countries, namely those with a recorded value in every year from 2000 to 2022. The estimand is the unweighted mean across those countries.\n\n## Data and exclusions\n- Input: `data/life_expectancy_panel.csv`, 439 data rows (`n_input_rows`), 20 distinct iso3 countries (`n_countries_total`).\n- Data problems found (`data_issues`):\n - Türkiye (TUR) appears twice for all 23 years, once as \"Turkey\" and once as \"Turkiye\", with identical values. I kept one row per (iso3, year), which removes 23 rows.\n - Switzerland (CHE) has an empty value for 2007. This is treated as missing and not imputed.\n - Bangladesh, Ethiopia, India and Nigeria have rows only for 2011–2022.\n- Exclusion ledger (`out/exclusions.csv`): 439 → 416 after deduplication → 415 after dropping the empty cell → 415 after the year-window check → 345 after keeping only balanced countries (`n_analysed`).\n- Balanced set: 15 countries (`n_countries_balanced`, `balanced_countries`): AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA.\n- Excluded: 5 countries (`n_countries_incomplete`, `incomplete_countries`): BGD, CHE, ETH, IND, NGA.\n\n## Method\nEach step was applied in order:\n1. Deduplicate on (iso3, year), checking first that the duplicate values are identical.\n2. Drop empty values.\n3. Keep only the countries whose recorded years cover all of 2000–2022.\n4. For each year, take the simple mean over those countries.\n\nThe baseline is the simple mean over all distinct countries with a recorded 2022 value (`n_countries_2022_available` = 20). No randomness is used (`random_seed` = null). `out/analysis.py` regenerates all outputs deterministically.\n\n## Results\n- Balanced mean in 2000: **78.060 years** (`mean_2000_balanced`).\n- Balanced mean in 2022: **81.553 years** (`mean_2022_balanced`).\n- Change from 2000 to 2022: **+3.493 years** (`change_2000_2022_balanced`).\n- Baseline 2022 mean over all 20 countries: 78.692 years (`mean_2022_all_available`). This is 2.861 years lower than the balanced 2022 mean (`gap_2022_all_available_minus_balanced`). The gap reflects composition: the baseline includes four lower-life-expectancy countries that are absent from the balanced set. It does not reflect any difference in time trends.\n- Change by country within the balanced set: from +0.797 years for USA (`per_country_change_min`, `per_country_change_min_iso3`) to +6.770 years for KOR (`per_country_change_max`, `per_country_change_max_iso3`).\n\n## Uncertainty\nThe estimands are exact descriptive means for a fixed, fully observed set of countries, so there is no sampling uncertainty to report and no confidence interval is given. Countries differ in how much life expectancy changed: the standard deviation of the per-country change is 1.409 years (`per_country_change_sd`). The WDI values are themselves modelled estimates that may be revised, and that uncertainty is not quantified here.\n\n## Limitations and open issues\n- The balanced set is mostly high-income countries; Türkiye is the only middle-income country left. All four low- and lower-middle-income countries drop out, and so does Switzerland, because of a single missing year (2007). The +3.49-year change therefore should not be read as a global or representative figure.\n- The means are unweighted, not population-weighted.\n- I assumed the missing CHE 2007 value and the missing 2000–2010 years for BGD/ETH/IND/NGA are file-assembly gaps; the README notes possible assembly errors. As the task requires, they were not filled from any outside source.\n- For TUR, the country name was harmonised to \"Turkiye\". The values were unaffected.\n- Feasibility: complete (`feasibility`).\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The balanced panel contains 15 of the 20 countries (345 country-years); BGD, CHE, ETH, IND and NGA are excluded as incomplete.\",\"results_keys\":[\"n_countries_balanced\",\"n_countries_total\",\"n_countries_incomplete\",\"balanced_countries\",\"incomplete_countries\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"CHE is excluded because of a single empty cell (2007).\"},\n {\"id\":\"C2\",\"text\":\"Balanced-panel mean life expectancy rose from 78.060 years in 2000 to 81.553 years in 2022, a change of +3.493 years.\",\"results_keys\":[\"mean_2000_balanced\",\"mean_2022_balanced\",\"change_2000_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Unweighted mean over a mostly high-income set; not representative globally.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries (78.692) is 2.861 years below the balanced 2022 mean, because of composition differences.\",\"results_keys\":[\"mean_2022_all_available\",\"gap_2022_all_available_minus_balanced\",\"n_countries_2022_available\"],\"status\":\"supported\",\"caveat\":\"Descriptive baseline only; not the estimand.\"},\n {\"id\":\"C4\",\"text\":\"The TUR rows were duplicated under two names with identical values; deduplicating on (iso3, year) removed 23 rows.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"Country name harmonised to 'Turkiye'.\"},\n {\"id\":\"C5\",\"text\":\"Per-country changes within the balanced set range from +0.797 (USA) to +6.770 (KOR) years, with an SD of 1.409.\",\"results_keys\":[\"per_country_change_min\",\"per_country_change_min_iso3\",\"per_country_change_max\",\"per_country_change_max_iso3\",\"per_country_change_sd\"],\"status\":\"supported\",\"caveat\":\"Describes dispersion across countries; not sampling uncertainty.\"}\n]\nEOF\ncat > state/phases.json <<'EOF'\n{\n \"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 recorded\",\"schema/missing/duplicates audited\",\"exclusion ledger sums verified (439-94=345)\",\"no availability-rule violations\"],\"notes\":\"TUR duplicates, CHE 2007 missing, 4 countries start 2011\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"baseline mean_2022_all_available computed = 78.692 over 20 countries\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, no seed needed\"],\"notes\":\"\"},\n \"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical results.json and identical csv hashes\",\"consistency checks in checks.md all PASS\"],\"notes\":\"\"},\n \"P5\":{\"status\":\"passed\",\"checks\":[\"report.md has all 6 sections, quantitative statements cite keys\",\"claims.json keys exist in results.json\"],\"notes\":\"\"}\n}\nEOF\ncd .. && ~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'))\nfor c in json.load(open('out/claims.json')):\n assert all(k in r for k in c['results_keys']),c['id']\nprint('claims ok')\"; ls out","description":"Write state files, report, claims"}}]}}51{"type":"api-request","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","id":"bad80e76-8a28-4965-80a6-953f73e89015","timestamp":"2026-10-30T04:47:31.484Z","version":"2.1.314","querySource":"sdk","shapeHash":"0892a2f21af1cb909ef336153e0be4e6572badc413b435944bb044302b171f19","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":"0f502357-265c-4106-b81e-a6f93c5b47ff","keep":14,"tail":["a5c7f5399b6d45896f9e8777e9e5f792e97a41bebc165851497147ff4dffea81","908c60b269b0f7ce918ae5dc811631746e99f05bbe71ea9688b8e364115b5cad"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"b98099969cf5705df633519528b77c15690b6a02d10da2d324feacb341fb0a90"}52{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"2b93cf31-3f6f-45ab-93fb-dcc75a428264","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5"}53{"type":"atis-latch","atis":"","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5"}54{"parentUuid":"2b93cf31-3f6f-45ab-93fb-dcc75a428264","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpGNgpgHngur3rNSece","type":"message","role":"assistant","content":[{"type":"text","text":"I've finished the analysis: across the 15 countries with a value in every year, average life expectancy at birth rose by **3.493 years** between 2000 and 2022. The script regenerates identical outputs on a clean re-run, and all consistency checks passed.\n\n**Main results:**\n- **Balanced set:** 15 of the 20 countries (345 country-years): AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA.\n- **Mean life expectancy:** 78.060 years in 2000 and 81.553 years in 2022, a change of +3.493 years.\n- **Simple 2022 average over all 20 countries:** 78.692 years. That is 2.861 years lower than the 15-country figure only because it includes four lower-life-expectancy countries (Bangladesh, Ethiopia, India, Nigeria).\n- **By country:** the change ranges from +0.797 years (USA) to +6.770 years (KOR).\n\n**Problems in the data file and how I handled them:**\n- **Türkiye listed twice:** every year appears under both \"Turkey\" and \"Turkiye\" with identical values. I kept one row per country-year (23 rows removed) and used the name \"Turkiye\".\n- **Switzerland, 2007:** the value is empty. It was not filled in, so Switzerland is left out of the 15 countries.\n- **Bangladesh, Ethiopia, India, Nigeria:** the file only has 2011–2022 for these, so they are left out too.\n\nGoing from 439 rows read to 345 analysed is fully accounted for in the exclusion ledger.\n\n**Limitations:** The 15 countries are almost all high-income, so the +3.49-year change is not a global figure. The averages are not weighted by population. No confidence interval is given because these are exact averages over a fixed set of countries, not a sample.\n\nEverything is in `out/`; `report.md` is the place to start, and `analysis.py` regenerates `results.json`, `analytic.csv` and `exclusions.csv` from the data. The rest of `out/` holds the claims list and the working notes (in `out/state/`)."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4559,"cache_read_input_tokens":15730,"output_tokens":720,"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":4559},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpGNQiNGr61AJjTZBfk","requestRef":"bad80e76-8a28-4965-80a6-953f73e89015","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"98d72c7f-eab5-4981-acce-b481df2f4d47","timestamp":"2026-10-30T04:47:38.642Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0156","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","version":"2.1.314","gitBranch":"HEAD"}55{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"98d72c7f-eab5-4981-acce-b481df2f4d47","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5"}56{"type":"cost-state","sessionId":"7cbe8b1e-323c-4daf-9d70-79d91e8127e5","totalCostUSD":0.3149256,"totalAPIDuration":93217,"totalAPIDurationWithoutRetries":93214,"totalToolDuration":1299,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":94626,"startTime":1793335564031,"modelUsage":{"claude-opus-5-5":{"inputTokens":18,"outputTokens":10369,"thinkingTokens":609,"cacheReadInputTokens":67743,"cacheCreationInputTokens":18785,"cacheCreationEphemeral5mInputTokens":18785,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.3149256}},"hasUnknownModelCost":false}57