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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\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. 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The estimands are the **balanced-panel means**: for each year, the simple (unweighted) mean of life expectancy over the countries that have a non-missing value in every year from 2000 to 2022 inclusive, each country counted once; the main quantities are the balanced means for 2000 and 2022 and their difference, together with the composition of the balanced set. A naive mean over whatever countries happen to be available in a year is not the estimand, but it is requested as a descriptive baseline for 2022.\n\n## Inputs\n- `data/life_expectancy_panel.csv` — observed data: life expectancy at birth, total (years), World Bank World Development Indicators indicator `SP.DYN.LE00.IN`, 20 countries, years 2000–2022. Columns `country_name` (string), `iso3` (ISO alpha-3 code), `year` (integer), `life_expectancy` (years). Source URL, retrieval date, license and SHA-256 are in `data/README.md`. The file was assembled from two extractions of the API response and may contain assembly errors; an empty cell means no value is recorded for that country-year.\n\n## Information-availability rules\n- Use only `data/life_expectancy_panel.csv`. Do not impute, interpolate or look up missing values: a country-year without a recorded value is missing.\n- A country is identified by its `iso3` code; each country contributes at most one observation per year.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-02\" |\n| `n_countries_total` | integer | countries | distinct countries (iso3) present in the file |\n| `n_countries_balanced` | integer | countries | countries with a recorded value in every year 2000–2022 |\n| `n_countries_incomplete` | integer | countries | distinct countries not in the balanced set |\n| `balanced_countries` | array of strings | — | iso3 codes of the balanced set, sorted |\n| `n_analysed` | integer | country-years | rows of the balanced analytic dataset (countries × years) |\n| `mean_2000_balanced` | number | years | balanced-panel mean life expectancy in 2000 |\n| `mean_2022_balanced` | number | years | balanced-panel mean life expectancy in 2022 |\n| `change_2000_2022_balanced` | number | years | `mean_2022_balanced − mean_2000_balanced` |\n| `mean_2022_all_available` | number | years | baseline: mean over all distinct countries with a recorded 2022 value, each counted once |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected and how it was handled (empty list if none) |\n| `n_input_rows` | object | rows | {\"life_expectancy_panel.csv\": number of data rows read (excluding the header)} |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — the balanced analytic dataset: one row per country-year of the balanced set, columns `iso3`, `country_name`, `year`, `life_expectancy`; exactly `n_analysed` rows, one row per (iso3, year) pair.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the number of rows read to `n_analysed` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`).\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/analytic.csv` and `out/exclusions.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 15 minutes of work.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\nlife_expectancy_panel.csv\nREADME.md","is_error":true,"tool_use_id":"toolu_0167nsexVo1s3vtt8PKZbHCh"}]},"uuid":"15213247-3ff1-4bc0-bc95-ebc48cff5ff5","timestamp":"2026-10-30T04:43:36.874Z","toolUseResult":"Error: Exit code 1\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Scientific question\nMeasure the change in life expectancy at birth between 2000 and 2022 across a fixed set of countries. The estimands are the **balanced-panel means**: for each year, the simple (unweighted) mean of life expectancy over the countries that have a non-missing value in every year from 2000 to 2022 inclusive, each country counted once; the main quantities are the balanced means for 2000 and 2022 and their difference, together with the composition of the balanced set. A naive mean over whatever countries happen to be available in a year is not the estimand, but it is requested as a descriptive baseline for 2022.\n\n## Inputs\n- `data/life_expectancy_panel.csv` — observed data: life expectancy at birth, total (years), World Bank World Development Indicators indicator `SP.DYN.LE00.IN`, 20 countries, years 2000–2022. Columns `country_name` (string), `iso3` (ISO alpha-3 code), `year` (integer), `life_expectancy` (years). Source URL, retrieval date, license and SHA-256 are in `data/README.md`. The file was assembled from two extractions of the API response and may contain assembly errors; an empty cell means no value is recorded for that country-year.\n\n## Information-availability rules\n- Use only `data/life_expectancy_panel.csv`. Do not impute, interpolate or look up missing values: a country-year without a recorded value is missing.\n- A country is identified by its `iso3` code; each country contributes at most one observation per year.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-02\" |\n| `n_countries_total` | integer | countries | distinct countries (iso3) present in the file |\n| `n_countries_balanced` | integer | countries | countries with a recorded value in every year 2000–2022 |\n| `n_countries_incomplete` | integer | countries | distinct countries not in the balanced set |\n| `balanced_countries` | array of strings | — | iso3 codes of the balanced set, sorted |\n| `n_analysed` | integer | country-years | rows of the balanced analytic dataset (countries × years) |\n| `mean_2000_balanced` | number | years | balanced-panel mean life expectancy in 2000 |\n| `mean_2022_balanced` | number | years | balanced-panel mean life expectancy in 2022 |\n| `change_2000_2022_balanced` | number | years | `mean_2022_balanced − mean_2000_balanced` |\n| `mean_2022_all_available` | number | years | baseline: mean over all distinct countries with a recorded 2022 value, each counted once |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected and how it was handled (empty list if none) |\n| `n_input_rows` | object | rows | {\"life_expectancy_panel.csv\": number of data rows read (excluding the header)} |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — the balanced analytic dataset: one row per country-year of the balanced set, columns `iso3`, `country_name`, `year`, `life_expectancy`; exactly `n_analysed` rows, one row per (iso3, year) pair.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the number of rows read to `n_analysed` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`).\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/analytic.csv` and `out/exclusions.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 15 minutes of work.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\nlife_expectancy_panel.csv\nREADME.md","sandboxed":false,"sourceToolAssistantUUID":"57fc9a7e-daef-4cfb-8a41-219940149c64","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","hash":"25b0cd77ac24b2e81347fa61b691e6f4d3fb9c9ec4fb957761fda141c9e03712","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSlgUKEAgTGAI4AUIIdGhpbmtpbmcSDNYQepoRPaTRChmkxBoMYllU9eWGlj64C3CLIjBZuk1biC6l3zCKfOWmZRB9SZOkdsTv3Z1YFNCNVlFBkjwIpg6mYt0nzquegGSKKO4qswQNpUSz3LXGRLr1d5fHp/5+V2MoLCMj9H+Q9ZrNa+yz9XkMGiKhXHrHeHVgj2E1c/asADn1emeJxKi+wW0458643P2KnsdkiRk1KkrnkBM5ufFGu3aw04IeEs7I2gQ2ebb9aDeCnuacJUXvfdybwWC4JDfsNcFgZOCtKgziiPn4ISS6Lk6jQmti5lyXvFatWXeXbY9AQxtTQVh7ppPdls8RyhEMVkdtolNW6OpZaXDCk5jsBzVU8Myy8/2NiRhdJHtkF794CU6n9fH3DvuUoLhe+E0l4I03voJWaxWP1t+ncLQZ868P3kOg0loeVTCNIs9OI4M7OzSWthXAG6MKqrivYu3cBcElu/iElJC8mcKA33rbeUJLV+y2X7QNThiXTTRYIcZr37Po8YaRzvNigmTiGewIELLjpu6TkdfiSnj1WAQWujNzE4AYsOK+1hTGSm+Wh2Uk+7pPLHkcXk1JrR209tF0UJeG7jjB16sYDxbDSHEKdS27+G4cZaK6MmpiZIGuohRLSi1YrweN4fmB+98UcTrxiUajGnLXvPJmZM6UJiUU5mxwBtuOvJs7Bbt6aWCRMsFl/UiKGzGnS9dGO8PsRJ1deROBM9oY1Nu7jYtnqfJjD9dfyGEiChujuFo3al22Mx0i0KodIsfe/X4qnJNDj9kw9ulUGabwFQI6Am8QNaV66pNLCL/UJ/qQFlos+q5JgiQSuN6IbnZzsYha3i9ri48uGn/FsXvGY/av1ScyyqMujxgB"},{"type":"tool_use","id":"toolu_0167nsexVo1s3vtt8PKZbHCh","name":"Bash","input":{"command":"cat TASK.md; 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cat gate_spec.json | head -50; mkdir -p out/state; cd data; shasum -a 256 *; wc -l life_expectancy_panel.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('life_expectancy_panel.csv',dtype={'iso3':str})\nprint(d.dtypes);print(d.describe(include='all'))\nprint(d.isna().sum())\nprint(d[d.duplicated(['iso3','year'],keep=False)].sort_values(['iso3','year']))\nprint(d[d.duplicated(keep=False)])\nprint(d.groupby('iso3').agg(n=('year','size'),nn=('life_expectancy','count'),names=('country_name','nunique'),ymin=('year','min'),ymax=('year','max')))\nprint(d.groupby('country_name').iso3.unique())\nprint(d.year.value_counts().sort_index())\nprint(d[d.life_expectancy.isna()])\nprint(d.sort_values('life_expectancy').head(5));print(d.sort_values('life_expectancy').dropna().tail(5))\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":1799,"cache_read_input_tokens":4315,"output_tokens":457,"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":1799},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXoy5M4UAhMUiLfqKQZF","requestRef":"4bb0f730-2779-4df4-9e95-774030c66261","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"20a4d8e0-ae34-4b3a-bee4-a363941aeec5","timestamp":"2026-10-30T04:43:41.136Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"20a4d8e0-ae34-4b3a-bee4-a363941aeec5","isSidechain":false,"promptId":"94bd2d06-60f7-4660-973a-1bb37b9a2092","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Kvaat7VsK5sznMeT6EtXZ8","type":"tool_result","content":"# 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 life_expectancy_panel.csv\ne9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0 README.md\n 440 life_expectancy_panel.csv\ncountry_name object\niso3 object\nyear int64\nlife_expectancy float64\ndtype: object\n country_name iso3 year life_expectancy\ncount 439 439 439.000000 438.000000\nunique 21 20 NaN NaN\ntop Australia TUR NaN NaN\nfreq 23 46 NaN NaN\nmean NaN NaN 2011.601367 78.466521\nstd NaN NaN 6.598263 6.073816\nmin NaN NaN 2000.000000 51.490000\n25% NaN NaN 2006.000000 77.738000\n50% NaN NaN 2012.000000 80.517500\n75% NaN NaN 2017.000000 81.803500\nmax NaN NaN 2022.000000 84.560000\ncountry_name 0\niso3 0\nyear 0\nlife_expectancy 1\ndtype: int64\n country_name iso3 year life_expectancy\n347 Turkey TUR 2000 71.931\n370 Turkiye TUR 2000 71.931\n348 Turkey TUR 2001 72.271\n371 Turkiye TUR 2001 72.271\n349 Turkey TUR 2002 72.605\n372 Turkiye TUR 2002 72.605\n350 Turkey TUR 2003 72.906\n373 Turkiye TUR 2003 72.906\n351 Turkey TUR 2004 73.223\n374 Turkiye TUR 2004 73.223\n352 Turkey TUR 2005 73.510\n375 Turkiye TUR 2005 73.510\n353 Turkey TUR 2006 73.804\n376 Turkiye TUR 2006 73.804\n354 Turkey TUR 2007 74.129\n377 Turkiye TUR 2007 74.129\n355 Turkey TUR 2008 74.380\n378 Turkiye TUR 2008 74.380\n356 Turkey TUR 2009 74.679\n379 Turkiye TUR 2009 74.679\n357 Turkey TUR 2010 74.964\n380 Turkiye TUR 2010 74.964\n358 Turkey TUR 2011 74.823\n381 Turkiye TUR 2011 74.823\n359 Turkey TUR 2012 75.552\n382 Turkiye TUR 2012 75.552\n360 Turkey TUR 2013 76.171\n383 Turkiye TUR 2013 76.171\n361 Turkey TUR 2014 76.452\n384 Turkiye TUR 2014 76.452\n362 Turkey TUR 2015 76.539\n385 Turkiye TUR 2015 76.539\n363 Turkey TUR 2016 76.595\n386 Turkiye TUR 2016 76.595\n364 Turkey TUR 2017 77.037\n387 Turkiye TUR 2017 77.037\n365 Turkey TUR 2018 77.454\n388 Turkiye TUR 2018 77.454\n366 Turkey TUR 2019 77.737\n389 Turkiye TUR 2019 77.737\n367 Turkey TUR 2020 76.525\n390 Turkiye TUR 2020 76.525\n368 Turkey TUR 2021 75.722\n391 Turkiye TUR 2021 75.722\n369 Turkey TUR 2022 77.591\n392 Turkiye TUR 2022 77.591\nEmpty DataFrame\nColumns: [country_name, iso3, year, life_expectancy]\nIndex: []\n n nn names ymin ymax\niso3 \nAUS 23 23 1 2000 2022\nAUT 23 23 1 2000 2022\nBEL 23 23 1 2000 2022\nBGD 12 12 1 2011 2022\nCAN 23 23 1 2000 2022\nCHE 23 22 1 2000 2022\nDEU 23 23 1 2000 2022\nESP 23 23 1 2000 2022\nETH 12 12 1 2011 2022\nFRA 23 23 1 2000 2022\nGBR 23 23 1 2000 2022\nIND 12 12 1 2011 2022\nITA 23 23 1 2000 2022\nJPN 23 23 1 2000 2022\nKOR 23 23 1 2000 2022\nNGA 12 12 1 2011 2022\nNLD 23 23 1 2000 2022\nSWE 23 23 1 2000 2022\nTUR 46 46 2 2000 2022\nUSA 23 23 1 2000 2022\ncountry_name\nAustralia [AUS]\nAustria [AUT]\nBangladesh [BGD]\nBelgium [BEL]\nCanada [CAN]\nEthiopia [ETH]\nFrance [FRA]\nGermany [DEU]\nIndia [IND]\nItaly [ITA]\nJapan [JPN]\nKorea, Rep. [KOR]\nNetherlands [NLD]\nNigeria [NGA]\nSpain [ESP]\nSweden [SWE]\nSwitzerland [CHE]\nTurkey [TUR]\nTurkiye [TUR]\nUnited Kingdom [GBR]\nUnited States [USA]\nName: iso3, dtype: object\nyear\n2000 17\n2001 17\n2002 17\n2003 17\n2004 17\n2005 17\n2006 17\n2007 17\n2008 17\n2009 17\n2010 17\n2011 21\n2012 21\n2013 21\n2014 21\n2015 21\n2016 21\n2017 21\n2018 21\n2019 21\n2020 21\n2021 21\n2022 21\nName: count, dtype: int64\n country_name iso3 year life_expectancy\n331 Switzerland CHE 2007 NaN\n country_name iso3 year life_expectancy\n266 Nigeria NGA 2011 51.490\n267 Nigeria NGA 2012 51.688\n268 Nigeria NGA 2013 51.827\n270 Nigeria NGA 2015 51.939\n269 Nigeria NGA 2014 51.940\n country_name iso3 year life_expectancy\n214 Japan JPN 2017 84.100\n215 Japan JPN 2018 84.211\n216 Japan JPN 2019 84.356\n218 Japan JPN 2021 84.446\n217 Japan JPN 2020 84.560","is_error":false}]},"uuid":"3ff789b0-82e1-4407-9bff-03f6ce19fdf8","timestamp":"2026-10-30T04:43:41.395Z","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 life_expectancy_panel.csv\ne9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0 README.md\n 440 life_expectancy_panel.csv\ncountry_name object\niso3 object\nyear int64\nlife_expectancy float64\ndtype: object\n country_name iso3 year life_expectancy\ncount 439 439 439.000000 438.000000\nunique 21 20 NaN NaN\ntop Australia TUR NaN NaN\nfreq 23 46 NaN NaN\nmean NaN NaN 2011.601367 78.466521\nstd NaN NaN 6.598263 6.073816\nmin NaN NaN 2000.000000 51.490000\n25% NaN NaN 2006.000000 77.738000\n50% NaN NaN 2012.000000 80.517500\n75% NaN NaN 2017.000000 81.803500\nmax NaN NaN 2022.000000 84.560000\ncountry_name 0\niso3 0\nyear 0\nlife_expectancy 1\ndtype: int64\n country_name iso3 year life_expectancy\n347 Turkey TUR 2000 71.931\n370 Turkiye TUR 2000 71.931\n348 Turkey TUR 2001 72.271\n371 Turkiye TUR 2001 72.271\n349 Turkey TUR 2002 72.605\n372 Turkiye TUR 2002 72.605\n350 Turkey TUR 2003 72.906\n373 Turkiye TUR 2003 72.906\n351 Turkey TUR 2004 73.223\n374 Turkiye TUR 2004 73.223\n352 Turkey TUR 2005 73.510\n375 Turkiye TUR 2005 73.510\n353 Turkey TUR 2006 73.804\n376 Turkiye TUR 2006 73.804\n354 Turkey TUR 2007 74.129\n377 Turkiye TUR 2007 74.129\n355 Turkey TUR 2008 74.380\n378 Turkiye TUR 2008 74.380\n356 Turkey TUR 2009 74.679\n379 Turkiye TUR 2009 74.679\n357 Turkey TUR 2010 74.964\n380 Turkiye TUR 2010 74.964\n358 Turkey TUR 2011 74.823\n381 Turkiye TUR 2011 74.823\n359 Turkey TUR 2012 75.552\n382 Turkiye TUR 2012 75.552\n360 Turkey TUR 2013 76.171\n383 Turkiye TUR 2013 76.171\n361 Turkey TUR 2014 76.452\n384 Turkiye TUR 2014 76.452\n362 Turkey TUR 2015 76.539\n385 Turkiye TUR 2015 76.539\n363 Turkey TUR 2016 76.595\n386 Turkiye TUR 2016 76.595\n364 Turkey TUR 2017 77.037\n387 Turkiye TUR 2017 77.037\n365 Turkey TUR 2018 77.454\n388 Turkiye TUR 2018 77.454\n366 Turkey TUR 2019 77.737\n389 Turkiye TUR 2019 77.737\n367 Turkey TUR 2020 76.525\n390 Turkiye TUR 2020 76.525\n368 Turkey TUR 2021 75.722\n391 Turkiye TUR 2021 75.722\n369 Turkey TUR 2022 77.591\n392 Turkiye TUR 2022 77.591\nEmpty DataFrame\nColumns: [country_name, iso3, year, life_expectancy]\nIndex: []\n n nn names ymin ymax\niso3 \nAUS 23 23 1 2000 2022\nAUT 23 23 1 2000 2022\nBEL 23 23 1 2000 2022\nBGD 12 12 1 2011 2022\nCAN 23 23 1 2000 2022\nCHE 23 22 1 2000 2022\nDEU 23 23 1 2000 2022\nESP 23 23 1 2000 2022\nETH 12 12 1 2011 2022\nFRA 23 23 1 2000 2022\nGBR 23 23 1 2000 2022\nIND 12 12 1 2011 2022\nITA 23 23 1 2000 2022\nJPN 23 23 1 2000 2022\nKOR 23 23 1 2000 2022\nNGA 12 12 1 2011 2022\nNLD 23 23 1 2000 2022\nSWE 23 23 1 2000 2022\nTUR 46 46 2 2000 2022\nUSA 23 23 1 2000 2022\ncountry_name\nAustralia [AUS]\nAustria [AUT]\nBangladesh [BGD]\nBelgium [BEL]\nCanada [CAN]\nEthiopia [ETH]\nFrance [FRA]\nGermany [DEU]\nIndia [IND]\nItaly [ITA]\nJapan [JPN]\nKorea, Rep. [KOR]\nNetherlands [NLD]\nNigeria [NGA]\nSpain [ESP]\nSweden [SWE]\nSwitzerland [CHE]\nTurkey [TUR]\nTurkiye [TUR]\nUnited Kingdom [GBR]\nUnited States [USA]\nName: iso3, dtype: object\nyear\n2000 17\n2001 17\n2002 17\n2003 17\n2004 17\n2005 17\n2006 17\n2007 17\n2008 17\n2009 17\n2010 17\n2011 21\n2012 21\n2013 21\n2014 21\n2015 21\n2016 21\n2017 21\n2018 21\n2019 21\n2020 21\n2021 21\n2022 21\nName: count, dtype: int64\n country_name iso3 year life_expectancy\n331 Switzerland CHE 2007 NaN\n country_name iso3 year life_expectancy\n266 Nigeria NGA 2011 51.490\n267 Nigeria NGA 2012 51.688\n268 Nigeria NGA 2013 51.827\n270 Nigeria NGA 2015 51.939\n269 Nigeria NGA 2014 51.940\n country_name iso3 year life_expectancy\n214 Japan JPN 2017 84.100\n215 Japan JPN 2018 84.211\n216 Japan JPN 2019 84.356\n218 Japan JPN 2021 84.446\n217 Japan JPN 2020 84.560","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"20a4d8e0-ae34-4b3a-bee4-a363941aeec5","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148/data","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","hash":"debe51946bd440467c864ebc82a030d0e87e9e99dea97334f515caa345e40330","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01Kvaat7VsK5sznMeT6EtXZ8","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json | head -50; mkdir -p out/state; cd data; shasum -a 256 *; wc -l life_expectancy_panel.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('life_expectancy_panel.csv',dtype={'iso3':str})\nprint(d.dtypes);print(d.describe(include='all'))\nprint(d.isna().sum())\nprint(d[d.duplicated(['iso3','year'],keep=False)].sort_values(['iso3','year']))\nprint(d[d.duplicated(keep=False)])\nprint(d.groupby('iso3').agg(n=('year','size'),nn=('life_expectancy','count'),names=('country_name','nunique'),ymin=('year','min'),ymax=('year','max')))\nprint(d.groupby('country_name').iso3.unique())\nprint(d.year.value_counts().sort_index())\nprint(d[d.life_expectancy.isna()])\nprint(d.sort_values('life_expectancy').head(5));print(d.sort_values('life_expectancy').dropna().tail(5))\n\"","description":"Audit data file"}}]}} 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{"parentUuid":"68bdae8e-8077-401a-aad9-9deb2625a297","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoyQyGw3DD3gLFN5asU","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011vsWKLrS6dTb1qtZ8jVKMT","name":"Bash","input":{"command":"mkdir -p out/state && cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-02: balanced-panel mean life expectancy 2000-2022. Reads data/ only.\"\"\"\nimport json, hashlib\nfrom pathlib import Path\nimport pandas as pd\n\nROOT = Path(__file__).resolve().parent.parent\nSRC = ROOT / \"data\" / \"life_expectancy_panel.csv\"\nOUT = ROOT / \"out\"\nYEARS = list(range(2000, 2023))\n\nraw = pd.read_csv(SRC, dtype={\"iso3\": str, \"country_name\": str})\nraw[\"iso3\"] = raw[\"iso3\"].str.strip()\nraw[\"country_name\"] = raw[\"country_name\"].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 ---\ndup_mask = raw.duplicated([\"iso3\", \"year\"], keep=False)\ndups = raw[dup_mask]\nfor iso, g in dups.groupby(\"iso3\"):\n # verify duplicates carry identical values (otherwise conflict -> cannot choose)\n conflict = g.groupby(\"year\")[\"life_expectancy\"].nunique(dropna=False).gt(1).any()\n if conflict:\n raise SystemExit(f\"Conflicting duplicate values for {iso}; not resolvable without outside info\")\n names = sorted(g[\"country_name\"].unique())\n issues.append({\"issue\": f\"iso3 {iso} appears under {len(names)} country_name spellings {names} \"\n f\"with duplicated (iso3, year) rows for {g['year'].nunique()} years \"\n f\"({len(g)} rows); values are identical across duplicates\",\n \"handling\": \"kept one row per (iso3, year) (country identified by iso3); \"\n \"duplicate rows removed; country counted once\"})\n# canonical name: last spelling alphabetically ('Turkiye' over 'Turkey'), current WDI name\ncanon = raw.groupby(\"iso3\")[\"country_name\"].agg(lambda s: sorted(s.unique())[-1])\ndf = raw.drop_duplicates([\"iso3\", \"year\"], keep=\"first\").copy()\ndf[\"country_name\"] = df[\"iso3\"].map(canon)\nstep(\"1_dedup_iso3_year\", \"keep one row per (iso3, year)\", n_raw, len(df),\n \"same country (iso3) assembled twice under two name spellings with identical values\")\n\n# --- 2. incomplete countries ---\nn_total = df[\"iso3\"].nunique()\nobs = df.dropna(subset=[\"life_expectancy\"])\nobs = obs[obs[\"year\"].isin(YEARS)]\ncnt = obs.groupby(\"iso3\")[\"year\"].nunique().reindex(sorted(df[\"iso3\"].unique()), fill_value=0)\nbalanced = sorted(cnt[cnt == len(YEARS)].index)\nincomplete = sorted(set(df[\"iso3\"]) - set(balanced))\nfor iso in incomplete:\n yrs = sorted(set(obs.loc[obs.iso3 == iso, \"year\"]))\n missing = [y for y in YEARS if y not in yrs]\n blank = sorted(df.loc[(df.iso3 == iso) & df.life_expectancy.isna(), \"year\"].tolist())\n desc = (f\"{iso}: no recorded value for {len(missing)} of 23 years \"\n f\"({missing[0]}-{missing[-1]}\" + (\")\" if len(missing) == missing[-1]-missing[0]+1 else f\"; years {missing})\"))\n if blank:\n desc += f\"; empty life_expectancy cell in year(s) {blank}\"\n issues.append({\"issue\": desc, \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"})\n\nbefore = len(df)\npart_missing = df[df.iso3.isin(incomplete) & (df.groupby(\"iso3\")[\"year\"].transform(\"size\") < len(YEARS))]\n# split exclusion into two transparent steps\nshort = [i for i in incomplete if (df.iso3 == i).sum() < len(YEARS)]\ngaps = [i for i in incomplete if i not in short]\ndf2 = df[~df.iso3.isin(short)]\nstep(\"2_drop_countries_missing_rows\", f\"drop countries without a row for every year 2000-2022 ({', '.join(short)})\",\n before, len(df2), \"not observed in every year; balanced-panel estimand requires all 23 years\")\ndf3 = df2[~df2.iso3.isin(gaps)]\nstep(\"3_drop_countries_with_empty_value\", f\"drop countries with an empty life_expectancy cell in some year ({', '.join(gaps)})\",\n len(df2), len(df3), \"missing value may not be imputed; country not in balanced set\")\n\nana = df3[df3.year.isin(YEARS)].sort_values([\"iso3\", \"year\"])[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]]\nassert ana.life_expectancy.notna().all() and len(ana) == len(balanced) * len(YEARS)\nassert not ana.duplicated([\"iso3\", \"year\"]).any()\nif len(ana) != len(df3):\n step(\"4_restrict_years\", \"keep years 2000-2022\", len(df3), len(ana), \"outside study window\")\n\nm2000 = float(ana.loc[ana.year == 2000, \"life_expectancy\"].mean())\nm2022 = float(ana.loc[ana.year == 2022, \"life_expectancy\"].mean())\navail22 = df[(df.year == 2022) & df.life_expectancy.notna()]\nassert not avail22.iso3.duplicated().any()\nm22all = float(avail22[\"life_expectancy\"].mean())\n\nres = {\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": int(n_total),\n \"n_countries_balanced\": len(balanced),\n \"n_countries_incomplete\": len(incomplete),\n \"balanced_countries\": balanced,\n \"n_analysed\": int(len(ana)),\n \"mean_2000_balanced\": round(m2000, 6),\n \"mean_2022_balanced\": round(m2022, 6),\n \"change_2000_2022_balanced\": round(m2022 - m2000, 6),\n \"mean_2022_all_available\": round(m22all, 6),\n \"n_countries_2022_available\": int(avail22.iso3.nunique()),\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 \"input_sha256\": hashlib.sha256(SRC.read_bytes()).hexdigest(),\n}\nOUT.mkdir(exist_ok=True)\nana.to_csv(OUT / \"analytic.csv\", index=False, float_format=\"%.3f\")\npd.DataFrame(excl).to_csv(OUT / \"exclusions.csv\", index=False)\n(OUT / \"results.json\").write_text(json.dumps(res, indent=2, ensure_ascii=False) + \"\\n\")\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\"]}))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/exclusions.csv; head -3 out/analytic.csv","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":4034,"cache_read_input_tokens":6114,"output_tokens":3110,"output_tokens_details":{"thinking_tokens":407},"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":4034},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXoyQgudzi8uag7z5XDj","requestRef":"d444e508-1871-4b19-aa67-b1897465c6a8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"33cc645e-f282-4156-b870-04e07c54cffc","timestamp":"2026-10-30T04:44:06.452Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148/data","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","version":"2.1.314","gitBranch":"HEAD"} {"type":"last-prompt","lastPrompt":"Begin","leafUuid":"33cc645e-f282-4156-b870-04e07c54cffc","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc"} {"type":"atis-latch","atis":"","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc"} {"parentUuid":"33cc645e-f282-4156-b870-04e07c54cffc","isSidechain":false,"promptId":"94bd2d06-60f7-4660-973a-1bb37b9a2092","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nTraceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RC0148/data/out/analysis.py\", line 11, in \n raw = pd.read_csv(SRC, dtype={\"iso3\": str, \"country_name\": str})\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: '/Users/simon-pierreboucher/wp29/runs/RC0148/data/data/life_expectancy_panel.csv'\nhead: out/analytic.csv: No such file or directory","is_error":true,"tool_use_id":"toolu_011vsWKLrS6dTb1qtZ8jVKMT"}]},"uuid":"30dc1390-0a74-421b-907e-a1b5cfba363c","timestamp":"2026-10-30T04:44:06.657Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RC0148/data/out/analysis.py\", line 11, in \n raw = pd.read_csv(SRC, dtype={\"iso3\": str, \"country_name\": str})\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: '/Users/simon-pierreboucher/wp29/runs/RC0148/data/data/life_expectancy_panel.csv'\nhead: out/analytic.csv: No such file or directory","sandboxed":false,"sourceToolAssistantUUID":"33cc645e-f282-4156-b870-04e07c54cffc","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148/data","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","hash":"d7c31ecbd572c64006020d2efba8313b5a8317bc709453bf16ae179abeb45850","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_011vsWKLrS6dTb1qtZ8jVKMT","name":"Bash","input":{"command":"mkdir -p out/state && cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-02: balanced-panel mean life expectancy 2000-2022. Reads data/ only.\"\"\"\nimport json, hashlib\nfrom pathlib import Path\nimport pandas as pd\n\nROOT = Path(__file__).resolve().parent.parent\nSRC = ROOT / \"data\" / \"life_expectancy_panel.csv\"\nOUT = ROOT / \"out\"\nYEARS = list(range(2000, 2023))\n\nraw = pd.read_csv(SRC, dtype={\"iso3\": str, \"country_name\": str})\nraw[\"iso3\"] = raw[\"iso3\"].str.strip()\nraw[\"country_name\"] = raw[\"country_name\"].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 ---\ndup_mask = raw.duplicated([\"iso3\", \"year\"], keep=False)\ndups = raw[dup_mask]\nfor iso, g in dups.groupby(\"iso3\"):\n # verify duplicates carry identical values (otherwise conflict -> cannot choose)\n conflict = g.groupby(\"year\")[\"life_expectancy\"].nunique(dropna=False).gt(1).any()\n if conflict:\n raise SystemExit(f\"Conflicting duplicate values for {iso}; not resolvable without outside info\")\n names = sorted(g[\"country_name\"].unique())\n issues.append({\"issue\": f\"iso3 {iso} appears under {len(names)} country_name spellings {names} \"\n f\"with duplicated (iso3, year) rows for {g['year'].nunique()} years \"\n f\"({len(g)} rows); values are identical across duplicates\",\n \"handling\": \"kept one row per (iso3, year) (country identified by iso3); \"\n \"duplicate rows removed; country counted once\"})\n# canonical name: last spelling alphabetically ('Turkiye' over 'Turkey'), current WDI name\ncanon = raw.groupby(\"iso3\")[\"country_name\"].agg(lambda s: sorted(s.unique())[-1])\ndf = raw.drop_duplicates([\"iso3\", \"year\"], keep=\"first\").copy()\ndf[\"country_name\"] = df[\"iso3\"].map(canon)\nstep(\"1_dedup_iso3_year\", \"keep one row per (iso3, year)\", n_raw, len(df),\n \"same country (iso3) assembled twice under two name spellings with identical values\")\n\n# --- 2. incomplete countries ---\nn_total = df[\"iso3\"].nunique()\nobs = df.dropna(subset=[\"life_expectancy\"])\nobs = obs[obs[\"year\"].isin(YEARS)]\ncnt = obs.groupby(\"iso3\")[\"year\"].nunique().reindex(sorted(df[\"iso3\"].unique()), fill_value=0)\nbalanced = sorted(cnt[cnt == len(YEARS)].index)\nincomplete = sorted(set(df[\"iso3\"]) - set(balanced))\nfor iso in incomplete:\n yrs = sorted(set(obs.loc[obs.iso3 == iso, \"year\"]))\n missing = [y for y in YEARS if y not in yrs]\n blank = sorted(df.loc[(df.iso3 == iso) & df.life_expectancy.isna(), \"year\"].tolist())\n desc = (f\"{iso}: no recorded value for {len(missing)} of 23 years \"\n f\"({missing[0]}-{missing[-1]}\" + (\")\" if len(missing) == missing[-1]-missing[0]+1 else f\"; years {missing})\"))\n if blank:\n desc += f\"; empty life_expectancy cell in year(s) {blank}\"\n issues.append({\"issue\": desc, \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"})\n\nbefore = len(df)\npart_missing = df[df.iso3.isin(incomplete) & (df.groupby(\"iso3\")[\"year\"].transform(\"size\") < len(YEARS))]\n# split exclusion into two transparent steps\nshort = [i for i in incomplete if (df.iso3 == i).sum() < len(YEARS)]\ngaps = [i for i in incomplete if i not in short]\ndf2 = df[~df.iso3.isin(short)]\nstep(\"2_drop_countries_missing_rows\", f\"drop countries without a row for every year 2000-2022 ({', '.join(short)})\",\n before, len(df2), \"not observed in every year; balanced-panel estimand requires all 23 years\")\ndf3 = df2[~df2.iso3.isin(gaps)]\nstep(\"3_drop_countries_with_empty_value\", f\"drop countries with an empty life_expectancy cell in some year ({', '.join(gaps)})\",\n len(df2), len(df3), \"missing value may not be imputed; country not in balanced set\")\n\nana = df3[df3.year.isin(YEARS)].sort_values([\"iso3\", \"year\"])[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]]\nassert ana.life_expectancy.notna().all() and len(ana) == len(balanced) * len(YEARS)\nassert not ana.duplicated([\"iso3\", \"year\"]).any()\nif len(ana) != len(df3):\n step(\"4_restrict_years\", \"keep years 2000-2022\", len(df3), len(ana), \"outside study window\")\n\nm2000 = float(ana.loc[ana.year == 2000, \"life_expectancy\"].mean())\nm2022 = float(ana.loc[ana.year == 2022, \"life_expectancy\"].mean())\navail22 = df[(df.year == 2022) & df.life_expectancy.notna()]\nassert not avail22.iso3.duplicated().any()\nm22all = float(avail22[\"life_expectancy\"].mean())\n\nres = {\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": int(n_total),\n \"n_countries_balanced\": len(balanced),\n \"n_countries_incomplete\": len(incomplete),\n \"balanced_countries\": balanced,\n \"n_analysed\": int(len(ana)),\n \"mean_2000_balanced\": round(m2000, 6),\n \"mean_2022_balanced\": round(m2022, 6),\n \"change_2000_2022_balanced\": round(m2022 - m2000, 6),\n \"mean_2022_all_available\": round(m22all, 6),\n \"n_countries_2022_available\": int(avail22.iso3.nunique()),\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 \"input_sha256\": hashlib.sha256(SRC.read_bytes()).hexdigest(),\n}\nOUT.mkdir(exist_ok=True)\nana.to_csv(OUT / \"analytic.csv\", index=False, float_format=\"%.3f\")\npd.DataFrame(excl).to_csv(OUT / \"exclusions.csv\", index=False)\n(OUT / \"results.json\").write_text(json.dumps(res, indent=2, ensure_ascii=False) + \"\\n\")\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\"]}))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/exclusions.csv; head -3 out/analytic.csv","description":"Write and run analysis script"}}]}} {"type":"api-request","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","id":"769f129d-3aab-46a1-a37a-b8623f9ef6f2","timestamp":"2026-10-30T04:44:06.660Z","version":"2.1.314","querySource":"sdk","shapeHash":"825578ae7cfd7a9c9891f22fab16ddcfe01540f9512990b388be26045148d62d","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":"d444e508-1871-4b19-aa67-b1897465c6a8","keep":6,"tail":["d7c31ecbd572c64006020d2efba8313b5a8317bc709453bf16ae179abeb45850","0334b437630ba8c35808e092a22ec0713b356820cfbc624ed23c154f725ff436"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a08bc469bc78fe4865d78c15d510822a68e046e829a4d8fd68ff849265162d70"} {"parentUuid":"30dc1390-0a74-421b-907e-a1b5cfba363c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp1Gxsf63hNSjLEH7Lv","type":"message","role":"assistant","content":[{"type":"text","text":"I was still in `data/` from the earlier `cd`; the script was written to `data/out/`. Moving it out (and removing the stray directory I created inside `data/`)."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3886,"cache_read_input_tokens":10148,"output_tokens":269,"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":3886},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXp1GhFmscNvmrVpgSvi","requestRef":"769f129d-3aab-46a1-a37a-b8623f9ef6f2","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d6e58815-f3ee-4413-aa4a-5495e496a977","timestamp":"2026-10-30T04:44:08.207Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148/data","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"d6e58815-f3ee-4413-aa4a-5495e496a977","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp1Gxsf63hNSjLEH7Lv","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KrHBVi4XENhGqSczfpFX62","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0148 && ls data data/out; 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values are identical across duplicates\",\n \"handling\": \"kept one row per (iso3, year) (country identified by iso3); duplicate rows removed; country counted once\"\n },\n {\n \"issue\": \"BGD: no recorded value for 11 of 23 years (2000-2010)\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n \"issue\": \"CHE: no recorded value for 1 of 23 years (2007-2007); empty life_expectancy cell in year(s) [2007]\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n \"issue\": \"ETH: no recorded value for 11 of 23 years (2000-2010)\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n \"issue\": \"IND: no recorded value for 11 of 23 years (2000-2010)\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n \"issue\": \"NGA: no recorded value for 11 of 23 years (2000-2010)\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n }\n ],\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null,\n \"input_sha256\": \"b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac\"\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1_dedup_iso3_year,\"keep one row per (iso3, year)\",439,416,23,same country (iso3) assembled twice under two name spellings with identical values\n2_drop_countries_missing_rows,\"drop countries without a row for every year 2000-2022 (BGD, ETH, IND, NGA)\",416,368,48,not observed in every year; balanced-panel estimand requires all 23 years\n3_drop_countries_with_empty_value,drop countries with an empty life_expectancy cell in some year (CHE),368,345,23,missing value may not be imputed; country not in balanced set\niso3,country_name,year,life_expectancy\nAUS,Australia,2000,79.234\nAUS,Australia,2001,79.634","is_error":false}]},"uuid":"538add0c-cb85-498d-98a3-0a302a1d326f","timestamp":"2026-10-30T04:44:09.901Z","toolUseResult":{"stdout":"data:\nlife_expectancy_panel.csv\nout\nREADME.md\n\ndata/out:\nanalysis.py\nstate\nlife_expectancy_panel.csv\nREADME.md\nb3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac data/life_expectancy_panel.csv\ne9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0 data/README.md\n{\"n_countries_balanced\": 15, \"n_analysed\": 345, \"mean_2000_balanced\": 78.060267, \"mean_2022_balanced\": 81.553067, \"change_2000_2022_balanced\": 3.4928, \"mean_2022_all_available\": 78.692}\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 \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"iso3 TUR appears under 2 country_name spellings ['Turkey', 'Turkiye'] with duplicated (iso3, year) rows for 23 years (46 rows); values are identical across duplicates\",\n \"handling\": \"kept one row per (iso3, year) (country identified by iso3); duplicate rows removed; country counted once\"\n },\n {\n \"issue\": \"BGD: no recorded value for 11 of 23 years (2000-2010)\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n \"issue\": \"CHE: no recorded value for 1 of 23 years (2007-2007); empty life_expectancy cell in year(s) [2007]\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n \"issue\": \"ETH: no recorded value for 11 of 23 years (2000-2010)\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n \"issue\": \"IND: no recorded value for 11 of 23 years (2000-2010)\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n \"issue\": \"NGA: no recorded value for 11 of 23 years (2000-2010)\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n }\n ],\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null,\n \"input_sha256\": \"b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac\"\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1_dedup_iso3_year,\"keep one row per (iso3, year)\",439,416,23,same country (iso3) assembled twice under two name spellings with identical values\n2_drop_countries_missing_rows,\"drop countries without a row for every year 2000-2022 (BGD, ETH, IND, NGA)\",416,368,48,not observed in every year; balanced-panel estimand requires all 23 years\n3_drop_countries_with_empty_value,drop countries with an empty life_expectancy cell in some year (CHE),368,345,23,missing value may not be imputed; country not in balanced set\niso3,country_name,year,life_expectancy\nAUS,Australia,2000,79.234\nAUS,Australia,2001,79.634","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f6268a8a-3188-4e18-8444-0d1d02037d92","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","hash":"f246f9cee609add54b136258c5c188a8c811366cf52924f2558cac28b726bd81","message":{"role":"assistant","content":[{"type":"text","text":"I was still in `data/` from the earlier `cd`; the script was written to `data/out/`. Moving it out (and removing the stray directory I created inside `data/`)."},{"type":"tool_use","id":"toolu_01KrHBVi4XENhGqSczfpFX62","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0148 && ls data data/out; mkdir -p out/state && mv data/out/analysis.py out/ && rmdir data/out/state data/out; ls data; shasum -a 256 data/*; ~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/exclusions.csv; head -3 out/analytic.csv","description":"Move script out of data and run"}}]}} 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{"parentUuid":"84545ec1-f4c2-4603-9dbb-df988ae0cd7e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp1WqoRU2RVeyT8LLGE","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013HxQGUKQH6vfdNssyK5Sw5","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json && 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 && echo IDENTICAL; ~/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'], a.duplicated(['iso3','year']).sum(), sorted(a.iso3.unique())==r['balanced_countries'], a.groupby('iso3').size().eq(23).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]==345)\nprint(abs(r['mean_2022_balanced']-r['mean_2000_balanced']-r['change_2000_2022_balanced'])<1e-9, r['n_countries_balanced']+r['n_countries_incomplete']==r['n_countries_total'])\nd=pd.read_csv('data/life_expectancy_panel.csv'); x=d[d.year==2022].life_expectancy; print('naive 2022 with dup TUR rows:',x.mean(), len(x))\nprint('per-country change range', (a[a.year==2022].set_index('iso3').life_expectancy-a[a.year==2000].set_index('iso3').life_expectancy).describe())\n\"","description":"Reproducibility and consistency 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- <<'EOF'\np='out/analysis.py'; s=open(p).read()\ns=s.replace('''m22all = float(avail22[\"life_expectancy\"].mean())''','''m22all = float(avail22[\"life_expectancy\"].mean())\n# descriptive dispersion of per-country changes (fixed set of countries, not a sample)\nw = ana.pivot(index=\"iso3\", columns=\"year\", values=\"life_expectancy\")\nchg = (w[2022] - w[2000])\n# sensitivity: naive 2022 mean if the duplicated TUR rows were NOT removed (wrong; shows impact)\nm22_raw_dups = float(raw.loc[raw.year == 2022, \"life_expectancy\"].dropna().mean())''')\ns=s.replace(''' \"feasibility\": \"complete\",''',''' \"change_per_country_sd\": round(float(chg.std(ddof=1)), 6),\n \"change_per_country_min\": round(float(chg.min()), 6),\n \"change_per_country_min_iso3\": str(chg.idxmin()),\n \"change_per_country_max\": round(float(chg.max()), 6),\n \"change_per_country_max_iso3\": str(chg.idxmax()),\n \"sensitivity_mean_2022_naive_with_duplicate_rows\": round(m22_raw_dups, 6),\n \"feasibility\": \"complete\",''')\nopen(p,'w').write(s)\nEOF\n~/wp29/venv/bin/python out/analysis.py >/dev/null && grep -E \"change_per|sensitivity\" out/results.json","description":"Add descriptive dispersion keys"}}]}} 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{"parentUuid":"8b6c6a4e-ab67-4568-b3f7-96f253839a21","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp2nURTPC4NisAhR4Wc","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DCVCsY9F6YHG5wwUcKLh9J","name":"Bash","input":{"command":"cd out && cat > state/contract.md <<'EOF'\n# Contract — F1-conf-02\n**Question:** How much did life expectancy at birth (WDI SP.DYN.LE00.IN) change from 2000 to 2022 across a fixed set of countries?\n**Estimands:** balanced-panel means — for 2000 and 2022, the unweighted mean of life expectancy over countries (iso3) with a recorded non-missing value in every year 2000–2022, each counted once; their difference; the composition of the balanced set. Meaningful as a description of this fixed country set (not population-weighted, not a sample of a wider population). Baseline: naive mean of all distinct countries with a 2022 value.\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 (6 sections), out/analysis.py, out/claims.json.\n**Information rules:** only the CSV; no imputation/interpolation/lookup; country = iso3; ≤1 obs per country-year.\n**Invalidity:** conflicting duplicate values for an (iso3, year) with no rule to choose; empty balanced set; double-counting a country.\nEOF\ncat > state/data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/life_expectancy_panel.csv | b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac | 439 | 4 |\n| data/README.md | e9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0 | — | — |\n\nSchema: country_name (str, 21 distinct), iso3 (str, 20 distinct), year (int, 2000–2022), life_expectancy (float, years, 3 dp; range 51.49 NGA 2011 – 84.56 JPN 2020; 1 missing).\nAnomalies:\n1. TUR appears twice (country_name \"Turkey\" and \"Turkiye\"), 46 rows = 23 duplicated (iso3, year) pairs with identical values → assembly error from the two extractions; dedupe to one row per (iso3, year), canonical name \"Turkiye\".\n2. BGD, ETH, IND, NGA have rows only for 2011–2022 (12 rows each) → missing 2000–2010; likely from the narrower extraction, but per rules treated as missing; excluded from balanced set.\n3. CHE 2007 has an empty life_expectancy cell → missing; CHE excluded.\nNo exact-duplicate conflicts, no out-of-range years, no implausible values. No input violates the availability rules.\nYear counts: 17 rows/yr 2000–2010 (16 countries + dup TUR), 21 rows/yr 2011–2022.\nEOF\ncat > state/checks.md <<'EOF'\n# Verification checks (P4)\n- Clean re-run (deleted results.json/analytic.csv/exclusions.csv, re-ran): results.json byte-identical — PASS\n- analytic.csv rows = n_analysed = 345 = 15 × 23; no duplicate (iso3, year); every country 23 years; iso3 set = balanced_countries — PASS\n- Exclusion ledger: n_before − n_removed = n_after each row; chained; starts at 439 (n_input_rows), ends at 345 (n_analysed) — PASS\n- change = mean_2022 − mean_2000 (tol 1e-9); balanced + incomplete = total (15+5=20) — PASS\n- 2022 baseline uses 20 distinct iso3 (no duplicate TUR) — PASS (assert in script)\n- Magnitudes: balanced means 78.06 → 81.55 yrs; baseline 2022 (78.69) lower than balanced 2022 because it includes NGA/ETH/IND/BGD — plausible\n- data/ unchanged (SHA-256 identical to audit) — PASS. Note: a stray data/out/ dir was briefly created by mistake (wrong cwd) and removed; original files untouched (hashes verified).\n- No random numbers used; random_seed = null.\nEOF\ncat > state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0: read TASK.md, wrote contract.\n- 2026-10-30 P1: audited data; found TUR duplication, 4 countries 2011+ only, CHE 2007 blank. Decision: dedupe TUR (identical values), exclude incomplete countries, no imputation.\n- 2026-10-30 Incident: analysis.py first written into data/out/ due to wrong cwd; moved to out/, stray dir removed, data hashes verified unchanged.\n- 2026-10-30 P2/P3: baseline (mean_2022_all_available) and balanced means computed in analysis.py.\n- 2026-10-30 P4: reproducibility & consistency checks passed; running gate.\nEOF\nls","description":"Write contract, audit, checks, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":886,"cache_read_input_tokens":16770,"output_tokens":1877,"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":886},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp2nCo2d9mAGafujhsn","requestRef":"471cda11-0dc8-4c7a-b21c-6c00291930fd","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"90a6dba5-322e-43b2-a488-cdbeb6d5c9a5","timestamp":"2026-10-30T04:44:43.691Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"90a6dba5-322e-43b2-a488-cdbeb6d5c9a5","isSidechain":false,"promptId":"94bd2d06-60f7-4660-973a-1bb37b9a2092","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DCVCsY9F6YHG5wwUcKLh9J","type":"tool_result","content":"analysis.py\nanalytic.csv\nexclusions.csv\nresults.json\nstate","is_error":false}]},"uuid":"a21cf85e-7294-44e2-b821-6f57e87a6ccc","timestamp":"2026-10-30T04:44:43.750Z","toolUseResult":{"stdout":"analysis.py\nanalytic.csv\nexclusions.csv\nresults.json\nstate","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"90a6dba5-322e-43b2-a488-cdbeb6d5c9a5","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148/out","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","hash":"21b06ad4d2874d54c272fabf1b766d97d6f12e3628a77b732c3e196a5c7cddd3","message":{"role":"assistant","content":[{"type":"text","text":"Now the state files."},{"type":"tool_use","id":"toolu_01DCVCsY9F6YHG5wwUcKLh9J","name":"Bash","input":{"command":"cd out && cat > state/contract.md <<'EOF'\n# Contract — F1-conf-02\n**Question:** How much did life expectancy at birth (WDI SP.DYN.LE00.IN) change from 2000 to 2022 across a fixed set of countries?\n**Estimands:** balanced-panel means — for 2000 and 2022, the unweighted mean of life expectancy over countries (iso3) with a recorded non-missing value in every year 2000–2022, each counted once; their difference; the composition of the balanced set. Meaningful as a description of this fixed country set (not population-weighted, not a sample of a wider population). Baseline: naive mean of all distinct countries with a 2022 value.\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 (6 sections), out/analysis.py, out/claims.json.\n**Information rules:** only the CSV; no imputation/interpolation/lookup; country = iso3; ≤1 obs per country-year.\n**Invalidity:** conflicting duplicate values for an (iso3, year) with no rule to choose; empty balanced set; double-counting a country.\nEOF\ncat > state/data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/life_expectancy_panel.csv | b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac | 439 | 4 |\n| data/README.md | e9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0 | — | — |\n\nSchema: country_name (str, 21 distinct), iso3 (str, 20 distinct), year (int, 2000–2022), life_expectancy (float, years, 3 dp; range 51.49 NGA 2011 – 84.56 JPN 2020; 1 missing).\nAnomalies:\n1. TUR appears twice (country_name \"Turkey\" and \"Turkiye\"), 46 rows = 23 duplicated (iso3, year) pairs with identical values → assembly error from the two extractions; dedupe to one row per (iso3, year), canonical name \"Turkiye\".\n2. BGD, ETH, IND, NGA have rows only for 2011–2022 (12 rows each) → missing 2000–2010; likely from the narrower extraction, but per rules treated as missing; excluded from balanced set.\n3. CHE 2007 has an empty life_expectancy cell → missing; CHE excluded.\nNo exact-duplicate conflicts, no out-of-range years, no implausible values. No input violates the availability rules.\nYear counts: 17 rows/yr 2000–2010 (16 countries + dup TUR), 21 rows/yr 2011–2022.\nEOF\ncat > state/checks.md <<'EOF'\n# Verification checks (P4)\n- Clean re-run (deleted results.json/analytic.csv/exclusions.csv, re-ran): results.json byte-identical — PASS\n- analytic.csv rows = n_analysed = 345 = 15 × 23; no duplicate (iso3, year); every country 23 years; iso3 set = balanced_countries — PASS\n- Exclusion ledger: n_before − n_removed = n_after each row; chained; starts at 439 (n_input_rows), ends at 345 (n_analysed) — PASS\n- change = mean_2022 − mean_2000 (tol 1e-9); balanced + incomplete = total (15+5=20) — PASS\n- 2022 baseline uses 20 distinct iso3 (no duplicate TUR) — PASS (assert in script)\n- Magnitudes: balanced means 78.06 → 81.55 yrs; baseline 2022 (78.69) lower than balanced 2022 because it includes NGA/ETH/IND/BGD — plausible\n- data/ unchanged (SHA-256 identical to audit) — PASS. Note: a stray data/out/ dir was briefly created by mistake (wrong cwd) and removed; original files untouched (hashes verified).\n- No random numbers used; random_seed = null.\nEOF\ncat > state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0: read TASK.md, wrote contract.\n- 2026-10-30 P1: audited data; found TUR duplication, 4 countries 2011+ only, CHE 2007 blank. Decision: dedupe TUR (identical values), exclude incomplete countries, no imputation.\n- 2026-10-30 Incident: analysis.py first written into data/out/ due to wrong cwd; moved to out/, stray dir removed, data hashes verified unchanged.\n- 2026-10-30 P2/P3: baseline (mean_2022_all_available) and balanced means computed in analysis.py.\n- 2026-10-30 P4: reproducibility & consistency checks passed; running gate.\nEOF\nls","description":"Write contract, audit, checks, log"}}]}} {"type":"api-request","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","id":"0f1c4302-c23d-401d-ac24-f106d968361a","timestamp":"2026-10-30T04:44:43.753Z","version":"2.1.314","querySource":"sdk","shapeHash":"825578ae7cfd7a9c9891f22fab16ddcfe01540f9512990b388be26045148d62d","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":"471cda11-0dc8-4c7a-b21c-6c00291930fd","keep":14,"tail":["21b06ad4d2874d54c272fabf1b766d97d6f12e3628a77b732c3e196a5c7cddd3","2591403c71d2beb9447e3a187760239112bfd0dc64a5ed898ec5bb267838c090"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"6e34d88da03fbcc7d761128452a11a640dd86bbffd7bd43203b609fd9de73a10"} {"parentUuid":"a21cf85e-7294-44e2-b821-6f57e87a6ccc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp41bVL8zAdzNw1JPCU","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017z2C7jrpS2CstLNMhcfdA8","name":"Bash","input":{"command":"cd out && cat > 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 across a fixed set of countries? The estimand is the unweighted mean over countries with a recorded value in every year 2000–2022 (the balanced panel), each country counted once.\n\n## Data and exclusions\nInput: `data/life_expectancy_panel.csv`, 439 data rows (`n_input_rows`), 20 distinct iso3 codes (`n_countries_total`). Problems found (`data_issues`):\n- **TUR duplicated**: Türkiye appears under two spellings (\"Turkey\", \"Turkiye\"), so all 23 (iso3, year) pairs appear twice with identical values. This is an assembly error. I kept one row per (iso3, year); 23 rows removed.\n- **BGD, ETH, IND, NGA** have rows only for 2011–2022. Their 2000–2010 values are treated as missing, not imputed, so these countries are excluded (48 rows).\n- **CHE** has an empty value for 2007, so it is excluded (23 rows).\n\nLedger (`out/exclusions.csv`): 439 → 416 → 368 → 345 = `n_analysed` (15 countries × 23 years). Balanced set (`balanced_countries`, `n_countries_balanced` = 15): AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA. Incomplete: 5 (`n_countries_incomplete`).\n\n## Method\n`out/analysis.py` removes duplicate (iso3, year) rows. It first checks that duplicates agree, and it would stop if they conflicted. It then keeps the countries that have a non-missing value in all 23 years and takes the simple mean across those countries for 2000 and for 2022. The baseline is the simple mean over all distinct countries with a recorded 2022 value (20 countries, `n_countries_2022_available`), after removing the duplicates. The method is deterministic and uses no randomness (`random_seed` = null).\n\n## Results\n- Balanced mean 2000: **78.060 years** (`mean_2000_balanced`).\n- Balanced mean 2022: **81.553 years** (`mean_2022_balanced`).\n- Change 2000→2022: **+3.493 years** (`change_2000_2022_balanced`).\n- Baseline, naive 2022 mean over all available countries: **78.692 years** (`mean_2022_all_available`). It is lower than the balanced 2022 mean because it includes the four lower-life-expectancy countries that are only observed from 2011. It is not comparable with the 2000 balanced mean.\n- Sensitivity: if the duplicate TUR rows had not been removed, the naive 2022 mean would be 78.640 (`sensitivity_mean_2022_naive_with_duplicate_rows`). Every balanced country has exactly one value per year, so the balanced means do not depend on this choice.\n\n## Uncertainty\nThese figures describe a fixed, fully enumerated set of countries. They are not a sample estimate, so I report no confidence interval. Countries differ in how much they changed. The standard deviation of the per-country changes is 1.409 years (`change_per_country_sd`). The smallest change was +0.797 for USA (`change_per_country_min`, `change_per_country_min_iso3`) and the largest +6.770 for KOR (`change_per_country_max`, `change_per_country_max_iso3`). Values are rounded to 3 decimals at the source, so rounding error in the means is negligible (<0.001 years).\n\n## Limitations and open issues\n- The balanced set has 15 mostly high-income countries. The result does not represent the full 20-country list, and the means are not population-weighted.\n- The missing 2000–2010 values for BGD, ETH, IND and NGA probably come from the narrower first extraction and not from real gaps in WDI. The task rules forbid looking them up or imputing them, so those countries are excluded. Including them would probably change the mean levels a lot.\n- Choosing \"Turkiye\" as the single name for TUR is cosmetic; the duplicate values were identical.\n- WDI values are revised between releases. These results apply to the release described in `data/README.md`.\n- Process note: I first wrote `analysis.py` into `data/out/` by mistake because the working directory was wrong. I moved it to `out/` and removed the stray directory. The input file hashes were checked afterwards and are unchanged.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The balanced panel contains 15 of the 20 countries (345 country-years); BGD, ETH, IND, NGA and CHE are excluded for missing years.\",\"results_keys\":[\"n_countries_balanced\",\"n_countries_total\",\"n_countries_incomplete\",\"balanced_countries\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Missingness of 2000-2010 for four countries may be an extraction artifact; not imputed per rules.\"},\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 fixed, mostly high-income set; descriptive, no sampling CI.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries is 78.692 years, lower than the balanced 2022 mean because of composition.\",\"results_keys\":[\"mean_2022_all_available\",\"n_countries_2022_available\",\"mean_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Descriptive baseline only; not comparable with the 2000 balanced mean.\"},\n {\"id\":\"C4\",\"text\":\"TUR was duplicated under two name spellings with identical values; deduplication to one row per (iso3, year) was applied.\",\"results_keys\":[\"data_issues\",\"sensitivity_mean_2022_naive_with_duplicate_rows\"],\"status\":\"supported\",\"caveat\":\"Balanced means are unaffected by the dedup choice since values were identical.\"},\n {\"id\":\"C5\",\"text\":\"Per-country changes vary from +0.797 (USA) to +6.770 (KOR), SD 1.409 years.\",\"results_keys\":[\"change_per_country_sd\",\"change_per_country_min\",\"change_per_country_min_iso3\",\"change_per_country_max\",\"change_per_country_max_iso3\"],\"status\":\"supported\",\"caveat\":\"Descriptive dispersion across countries.\"}\n]\nEOF\ncat > state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"contract.md written covering question, estimands, outputs, rules, invalidity\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 and schema recorded in data_audit.md\",\"duplicates/missing/anomalies identified\",\"exclusion ledger sums 439->345\"],\"notes\":\"\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"mean_2022_all_available computed on deduplicated data\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, no RNG\"],\"notes\":\"\"},\n \"P4\":{\"status\":\"running\",\"checks\":[\"clean re-run byte-identical\",\"consistency checks in checks.md all PASS\"],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\ncd .. && ~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Write report, claims, phases; run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1908,"cache_read_input_tokens":17656,"output_tokens":2732,"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":1908},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXp41JcxRq5M6irvqbDA","requestRef":"0f1c4302-c23d-401d-ac24-f106d968361a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8b88bc21-9824-461b-b988-6ef7b1d142f7","timestamp":"2026-10-30T04:45:06.299Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148/out","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","version":"2.1.314","gitBranch":"HEAD"} {"type":"last-prompt","lastPrompt":"Begin","leafUuid":"8b88bc21-9824-461b-b988-6ef7b1d142f7","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc"} {"type":"atis-latch","atis":"","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc"} {"parentUuid":"8b88bc21-9824-461b-b988-6ef7b1d142f7","isSidechain":false,"promptId":"94bd2d06-60f7-4660-973a-1bb37b9a2092","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017z2C7jrpS2CstLNMhcfdA8","type":"tool_result","content":"(eval):cd:1: no such file or directory: out\nPGAR GATE pgar_gate/1.0 — FAILED (8 of 32 checks failed)\n [ok] file:out/results.json: present\n [FAIL] file:out/report.md: missing or empty\n [ok] file:out/analysis.py: present\n [ok] file:out/claims.json: present\n [ok] file:out/analytic.csv: present\n [ok] file:out/exclusions.csv: present\n [ok] results_json: parsed\n [ok] key:task_id: ok\n [ok] key:n_countries_total: ok\n [ok] key:n_countries_balanced: ok\n [ok] key:n_countries_incomplete: ok\n [ok] key:balanced_countries: ok\n [ok] key:n_analysed: ok\n [ok] key:mean_2000_balanced: ok\n [ok] key:mean_2022_balanced: ok\n [ok] key:change_2000_2022_balanced: ok\n [ok] key:mean_2022_all_available: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [ok] claims_json: 5 claims, all keys resolve\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n [ok] exclusion_ledger: 3 steps consistent\n [ok] reproducibility: regenerated in 0.2s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0148/out/state/gate_receipts/20261030T044506.545641Z.json\n20261030T044506.545641Z.json","is_error":false}]},"uuid":"38275ab8-c16b-4d60-a188-40ee4698bbdd","timestamp":"2026-10-30T04:45:06.553Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: out\nPGAR GATE pgar_gate/1.0 — FAILED (8 of 32 checks failed)\n [ok] file:out/results.json: present\n [FAIL] file:out/report.md: missing or empty\n [ok] file:out/analysis.py: present\n [ok] file:out/claims.json: present\n [ok] file:out/analytic.csv: present\n [ok] file:out/exclusions.csv: present\n [ok] results_json: parsed\n [ok] key:task_id: ok\n [ok] key:n_countries_total: ok\n [ok] key:n_countries_balanced: ok\n [ok] key:n_countries_incomplete: ok\n [ok] key:balanced_countries: ok\n [ok] key:n_analysed: ok\n [ok] key:mean_2000_balanced: ok\n [ok] key:mean_2022_balanced: ok\n [ok] key:change_2000_2022_balanced: ok\n [ok] key:mean_2022_all_available: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [ok] claims_json: 5 claims, all keys resolve\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n [ok] exclusion_ledger: 3 steps consistent\n [ok] reproducibility: regenerated in 0.2s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0148/out/state/gate_receipts/20261030T044506.545641Z.json\n20261030T044506.545641Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"8b88bc21-9824-461b-b988-6ef7b1d142f7","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","hash":"60bbec561aeeccb28ed8098b4ed30f5348e0b75f51793578967be13f1ae0b23a","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_017z2C7jrpS2CstLNMhcfdA8","name":"Bash","input":{"command":"cd out && cat > 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 across a fixed set of countries? The estimand is the unweighted mean over countries with a recorded value in every year 2000–2022 (the balanced panel), each country counted once.\n\n## Data and exclusions\nInput: `data/life_expectancy_panel.csv`, 439 data rows (`n_input_rows`), 20 distinct iso3 codes (`n_countries_total`). Problems found (`data_issues`):\n- **TUR duplicated**: Türkiye appears under two spellings (\"Turkey\", \"Turkiye\"), so all 23 (iso3, year) pairs appear twice with identical values. This is an assembly error. I kept one row per (iso3, year); 23 rows removed.\n- **BGD, ETH, IND, NGA** have rows only for 2011–2022. Their 2000–2010 values are treated as missing, not imputed, so these countries are excluded (48 rows).\n- **CHE** has an empty value for 2007, so it is excluded (23 rows).\n\nLedger (`out/exclusions.csv`): 439 → 416 → 368 → 345 = `n_analysed` (15 countries × 23 years). Balanced set (`balanced_countries`, `n_countries_balanced` = 15): AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA. Incomplete: 5 (`n_countries_incomplete`).\n\n## Method\n`out/analysis.py` removes duplicate (iso3, year) rows. It first checks that duplicates agree, and it would stop if they conflicted. It then keeps the countries that have a non-missing value in all 23 years and takes the simple mean across those countries for 2000 and for 2022. The baseline is the simple mean over all distinct countries with a recorded 2022 value (20 countries, `n_countries_2022_available`), after removing the duplicates. The method is deterministic and uses no randomness (`random_seed` = null).\n\n## Results\n- Balanced mean 2000: **78.060 years** (`mean_2000_balanced`).\n- Balanced mean 2022: **81.553 years** (`mean_2022_balanced`).\n- Change 2000→2022: **+3.493 years** (`change_2000_2022_balanced`).\n- Baseline, naive 2022 mean over all available countries: **78.692 years** (`mean_2022_all_available`). It is lower than the balanced 2022 mean because it includes the four lower-life-expectancy countries that are only observed from 2011. It is not comparable with the 2000 balanced mean.\n- Sensitivity: if the duplicate TUR rows had not been removed, the naive 2022 mean would be 78.640 (`sensitivity_mean_2022_naive_with_duplicate_rows`). Every balanced country has exactly one value per year, so the balanced means do not depend on this choice.\n\n## Uncertainty\nThese figures describe a fixed, fully enumerated set of countries. They are not a sample estimate, so I report no confidence interval. Countries differ in how much they changed. The standard deviation of the per-country changes is 1.409 years (`change_per_country_sd`). The smallest change was +0.797 for USA (`change_per_country_min`, `change_per_country_min_iso3`) and the largest +6.770 for KOR (`change_per_country_max`, `change_per_country_max_iso3`). Values are rounded to 3 decimals at the source, so rounding error in the means is negligible (<0.001 years).\n\n## Limitations and open issues\n- The balanced set has 15 mostly high-income countries. The result does not represent the full 20-country list, and the means are not population-weighted.\n- The missing 2000–2010 values for BGD, ETH, IND and NGA probably come from the narrower first extraction and not from real gaps in WDI. The task rules forbid looking them up or imputing them, so those countries are excluded. Including them would probably change the mean levels a lot.\n- Choosing \"Turkiye\" as the single name for TUR is cosmetic; the duplicate values were identical.\n- WDI values are revised between releases. These results apply to the release described in `data/README.md`.\n- Process note: I first wrote `analysis.py` into `data/out/` by mistake because the working directory was wrong. I moved it to `out/` and removed the stray directory. The input file hashes were checked afterwards and are unchanged.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The balanced panel contains 15 of the 20 countries (345 country-years); BGD, ETH, IND, NGA and CHE are excluded for missing years.\",\"results_keys\":[\"n_countries_balanced\",\"n_countries_total\",\"n_countries_incomplete\",\"balanced_countries\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Missingness of 2000-2010 for four countries may be an extraction artifact; not imputed per rules.\"},\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 fixed, mostly high-income set; descriptive, no sampling CI.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries is 78.692 years, lower than the balanced 2022 mean because of composition.\",\"results_keys\":[\"mean_2022_all_available\",\"n_countries_2022_available\",\"mean_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Descriptive baseline only; not comparable with the 2000 balanced mean.\"},\n {\"id\":\"C4\",\"text\":\"TUR was duplicated under two name spellings with identical values; deduplication to one row per (iso3, year) was applied.\",\"results_keys\":[\"data_issues\",\"sensitivity_mean_2022_naive_with_duplicate_rows\"],\"status\":\"supported\",\"caveat\":\"Balanced means are unaffected by the dedup choice since values were identical.\"},\n {\"id\":\"C5\",\"text\":\"Per-country changes vary from +0.797 (USA) to +6.770 (KOR), SD 1.409 years.\",\"results_keys\":[\"change_per_country_sd\",\"change_per_country_min\",\"change_per_country_min_iso3\",\"change_per_country_max\",\"change_per_country_max_iso3\"],\"status\":\"supported\",\"caveat\":\"Descriptive dispersion across countries.\"}\n]\nEOF\ncat > state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"contract.md written covering question, estimands, outputs, rules, invalidity\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 and schema recorded in data_audit.md\",\"duplicates/missing/anomalies identified\",\"exclusion ledger sums 439->345\"],\"notes\":\"\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"mean_2022_all_available computed on deduplicated data\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, no RNG\"],\"notes\":\"\"},\n \"P4\":{\"status\":\"running\",\"checks\":[\"clean re-run byte-identical\",\"consistency checks in checks.md all PASS\"],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\ncd .. && ~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; 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The estimand is the unweighted mean over countries with a recorded value in every year 2000–2022 (the balanced panel), each country counted once.\n\n## Data and exclusions\nInput: `data/life_expectancy_panel.csv`, 439 data rows (`n_input_rows`), 20 distinct iso3 codes (`n_countries_total`). Problems found (`data_issues`):\n- **TUR duplicated**: Türkiye appears under two spellings (\"Turkey\", \"Turkiye\"), so all 23 (iso3, year) pairs appear twice with identical values. This is an assembly error. I kept one row per (iso3, year); 23 rows removed.\n- **BGD, ETH, IND, NGA** have rows only for 2011–2022. Their 2000–2010 values are treated as missing, not imputed, so these countries are excluded (48 rows).\n- **CHE** has an empty value for 2007, so it is excluded (23 rows).\n\nLedger (`out/exclusions.csv`): 439 → 416 → 368 → 345 = `n_analysed` (15 countries × 23 years). Balanced set (`balanced_countries`, `n_countries_balanced` = 15): AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA. Incomplete: 5 (`n_countries_incomplete`).\n\n## Method\n`out/analysis.py` removes duplicate (iso3, year) rows. It first checks that duplicates agree, and it would stop if they conflicted. It then keeps the countries that have a non-missing value in all 23 years and takes the simple mean across those countries for 2000 and for 2022. The baseline is the simple mean over all distinct countries with a recorded 2022 value (20 countries, `n_countries_2022_available`), after removing the duplicates. The method is deterministic and uses no randomness (`random_seed` = null).\n\n## Results\n- Balanced mean 2000: **78.060 years** (`mean_2000_balanced`).\n- Balanced mean 2022: **81.553 years** (`mean_2022_balanced`).\n- Change 2000→2022: **+3.493 years** (`change_2000_2022_balanced`).\n- Baseline, naive 2022 mean over all available countries: **78.692 years** (`mean_2022_all_available`). It is lower than the balanced 2022 mean because it includes the four lower-life-expectancy countries that are only observed from 2011. It is not comparable with the 2000 balanced mean.\n- Sensitivity: if the duplicate TUR rows had not been removed, the naive 2022 mean would be 78.640 (`sensitivity_mean_2022_naive_with_duplicate_rows`). Every balanced country has exactly one value per year, so the balanced means do not depend on this choice.\n\n## Uncertainty\nThese figures describe a fixed, fully enumerated set of countries. They are not a sample estimate, so I report no confidence interval. Countries differ in how much they changed. The standard deviation of the per-country changes is 1.409 years (`change_per_country_sd`). The smallest change was +0.797 for USA (`change_per_country_min`, `change_per_country_min_iso3`) and the largest +6.770 for KOR (`change_per_country_max`, `change_per_country_max_iso3`). Values are rounded to 3 decimals at the source, so rounding error in the means is negligible (<0.001 years).\n\n## Limitations and open issues\n- The balanced set has 15 mostly high-income countries. The result does not represent the full 20-country list, and the means are not population-weighted.\n- The missing 2000–2010 values for BGD, ETH, IND and NGA probably come from the narrower first extraction and not from real gaps in WDI. The task rules forbid looking them up or imputing them, so those countries are excluded. 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The estimand is the unweighted mean over countries with a recorded value in every year 2000–2022 (the balanced panel), each country counted once.\n\n## Data and exclusions\nInput: `data/life_expectancy_panel.csv`, 439 data rows (`n_input_rows`), 20 distinct iso3 codes (`n_countries_total`). Problems found (`data_issues`):\n- **TUR duplicated**: Türkiye appears under two spellings (\"Turkey\", \"Turkiye\"), so all 23 (iso3, year) pairs appear twice with identical values. This is an assembly error. I kept one row per (iso3, year); 23 rows removed.\n- **BGD, ETH, IND, NGA** have rows only for 2011–2022. Their 2000–2010 values are treated as missing, not imputed, so these countries are excluded (48 rows).\n- **CHE** has an empty value for 2007, so it is excluded (23 rows).\n\nLedger (`out/exclusions.csv`): 439 → 416 → 368 → 345 = `n_analysed` (15 countries × 23 years). Balanced set (`balanced_countries`, `n_countries_balanced` = 15): AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA. Incomplete: 5 (`n_countries_incomplete`).\n\n## Method\n`out/analysis.py` removes duplicate (iso3, year) rows. It first checks that duplicates agree, and it would stop if they conflicted. It then keeps the countries that have a non-missing value in all 23 years and takes the simple mean across those countries for 2000 and for 2022. The baseline is the simple mean over all distinct countries with a recorded 2022 value (20 countries, `n_countries_2022_available`), after removing the duplicates. The method is deterministic and uses no randomness (`random_seed` = null).\n\n## Results\n- Balanced mean 2000: **78.060 years** (`mean_2000_balanced`).\n- Balanced mean 2022: **81.553 years** (`mean_2022_balanced`).\n- Change 2000→2022: **+3.493 years** (`change_2000_2022_balanced`).\n- Baseline, naive 2022 mean over all available countries: **78.692 years** (`mean_2022_all_available`). It is lower than the balanced 2022 mean because it includes the four lower-life-expectancy countries that are only observed from 2011. It is not comparable with the 2000 balanced mean.\n- Sensitivity: if the duplicate TUR rows had not been removed, the naive 2022 mean would be 78.640 (`sensitivity_mean_2022_naive_with_duplicate_rows`). Every balanced country has exactly one value per year, so the balanced means do not depend on this choice.\n\n## Uncertainty\nThese figures describe a fixed, fully enumerated set of countries. They are not a sample estimate, so I report no confidence interval. Countries differ in how much they changed. The standard deviation of the per-country changes is 1.409 years (`change_per_country_sd`). The smallest change was +0.797 for USA (`change_per_country_min`, `change_per_country_min_iso3`) and the largest +6.770 for KOR (`change_per_country_max`, `change_per_country_max_iso3`). Values are rounded to 3 decimals at the source, so rounding error in the means is negligible (<0.001 years).\n\n## Limitations and open issues\n- The balanced set has 15 mostly high-income countries. The result does not represent the full 20-country list, and the means are not population-weighted.\n- The missing 2000–2010 values for BGD, ETH, IND and NGA probably come from the narrower first extraction and not from real gaps in WDI. The task rules forbid looking them up or imputing them, so those countries are excluded. Including them would probably change the mean levels a lot.\n- Choosing \"Turkiye\" as the single name for TUR is cosmetic; the duplicate values were identical.\n- WDI values are revised between releases. These results apply to the release described in `data/README.md`.\n- Process note: I first wrote `analysis.py` into `data/out/` by mistake because the working directory was wrong. I moved it to `out/` and removed the stray directory. The input file hashes were checked afterwards and are unchanged.\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np='out/state/phases.json'; d=json.load(open(p))\nd['P4']['status']='passed'; d['P4']['checks'].append(\"gate run 20261030T044506.545641Z: reproducibility ok; failed only on report (not yet written) and non-terminal P4/P5 - expected at this point\")\nd['P5']['status']='passed'; d['P5']['checks']=[\"report.md with 6 sections, all numbers cite results keys\",\"claims.json 5 claims, keys resolve\"]\njson.dump(d,open(p,'w'),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -vE \"\\[ok\\]\"; ls out/state/gate_receipts","description":"Write report, update phases, rerun gate"}}]}} {"type":"api-request","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","id":"23f2633a-3323-4212-b261-5ee95594f504","timestamp":"2026-10-30T04:45:24.835Z","version":"2.1.314","querySource":"sdk","shapeHash":"825578ae7cfd7a9c9891f22fab16ddcfe01540f9512990b388be26045148d62d","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":"17112d2b-364d-4a4a-8976-a746d5daf765","keep":20,"tail":["034c03552cf067e9b919802dbe3cdebb94c041045891f503f865b3779e16a609","8fbe3400b421390b5ea10d837118dcd86727d24c82f2c7ba3029ab30c8cdc8c0"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"f64341bdeb923b18e130af62c7335ab753b7c64c7bc471176d8e0a9aee78dca6"} {"type":"last-prompt","lastPrompt":"Begin","leafUuid":"dd2b845f-f946-47ea-b4c6-6dbbc5e3b3f6","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc"} {"type":"atis-latch","atis":"","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc"} {"parentUuid":"dd2b845f-f946-47ea-b4c6-6dbbc5e3b3f6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp73GHBMNXqeZ4YT5T3","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BYPq2WPbM7tHorACVETHPb","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np='out/state/phases.json'; d=json.load(open(p))\nd['P5']['checks'].append(\"gate run 20261030T044524.825605Z: PASSED 32/32 (receipt out/state/gate_receipts/20261030T044524.825605Z.json)\")\njson.dump(d,open(p,'w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate 20261030T044506 failed only on missing report/non-terminal phases). 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Task complete.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1; shasum -a 256 data/*","description":"Record final gate receipt and verify"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2041,"cache_read_input_tokens":23474,"output_tokens":349,"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":2041},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXp72yAZ9x7Gv5nQhwvP","requestRef":"23f2633a-3323-4212-b261-5ee95594f504","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"fec35d11-752e-465c-916c-f630fa5e0342","timestamp":"2026-10-30T04:45:28.579Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"fec35d11-752e-465c-916c-f630fa5e0342","isSidechain":false,"promptId":"94bd2d06-60f7-4660-973a-1bb37b9a2092","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BYPq2WPbM7tHorACVETHPb","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 32 checks failed)\nb3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac data/life_expectancy_panel.csv\ne9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0 data/README.md","is_error":false}]},"uuid":"1a5fed0c-ac00-47c4-be6d-8390e773035c","timestamp":"2026-10-30T04:45:28.864Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 32 checks failed)\nb3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac data/life_expectancy_panel.csv\ne9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0 data/README.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"fec35d11-752e-465c-916c-f630fa5e0342","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"73d2a5c7-1039-4b16-9363-20cb2bef0afc","hash":"a7b5ac715e716865a2e273d1c2dc1577f827a4717679fc301dad3704513d4aa5","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01BYPq2WPbM7tHorACVETHPb","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np='out/state/phases.json'; d=json.load(open(p))\nd['P5']['checks'].append(\"gate run 20261030T044524.825605Z: PASSED 32/32 (receipt out/state/gate_receipts/20261030T044524.825605Z.json)\")\njson.dump(d,open(p,'w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate 20261030T044506 failed only on missing report/non-terminal phases). 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Across the 15 countries that have a value in every year, average life expectancy rose from **78.060 years in 2000 to 81.553 in 2022, a gain of 3.493 years**. All phases P0–P5 are marked passed, and the final gate run passed all 32 checks; re-running `out/analysis.py` from scratch reproduced the results exactly.\n\n**Results**\n- **Countries:** 20 in the file, 15 with a value in every year: AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA. That gives 345 country-years.\n- **Comparison mean for 2022:** the simple mean over all 20 countries with a 2022 value is 78.692 years. It is lower than the 15-country figure because it includes four lower-life-expectancy countries.\n- **Spread across countries:** individual gains ranged from +0.797 years (USA) to +6.770 (KOR), with a standard deviation of 1.409. These countries are a fixed list, not a sample, so I report this spread rather than a confidence interval.\n\n**Data problems and how I handled them**\n- **Türkiye listed twice:** it appears as both \"Turkey\" and \"Turkiye\", with identical values. I kept one row per country-year, removing 23 rows.\n- **Four countries start in 2011:** BGD, ETH, IND and NGA have no values for 2000–2010, so they are excluded. The gap probably comes from how the file was put together rather than from the World Bank source, but the task forbids looking up or filling in missing values.\n- **Switzerland missing 2007:** the 2007 value is blank, so Switzerland is excluded.\n\nThe row counts add up from 439 rows read to 345 analysed (439 → 416 → 368 → 345).\n\n**Limitations**\n- The 15 countries are mostly high-income, and the means are not weighted by population.\n- The 2022 comparison mean includes countries that the 2000 figure does not, so the two should not be compared directly.\n\n**Process note:** because I was in the wrong directory, I briefly wrote `analysis.py` into a new folder inside `data/`. I moved it to `out/` and deleted that folder. The input files are unchanged (checksums match). 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