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1{"type":"atis-latch","atis":"","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T04:43:27.814Z","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T04:43:27.820Z","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d"}4{"parentUuid":null,"isSidechain":false,"promptId":"6d9633a6-419b-4786-8d68-b31e015f4286","type":"user","message":{"role":"user","content":"Begin"},"uuid":"cdce84de-6f89-44cc-b7c0-64c7cd6324bd","timestamp":"2026-10-30T04:43:27.825Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0150","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","shapeHash":"eeb19a4c232b3a4a5700999f6d7939186b1a0f6934547979b35794975d4faf55","timestamp":"2026-10-30T04:43:27.839Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RC0150\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0150","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d"}12{"parentUuid":"d21622e5-6d15-48c6-ab1b-e210b0c0bd66","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoxTXXt2zetwEGVqCSw","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2603,"cache_read_input_tokens":1504,"output_tokens":117,"output_tokens_details":{"thinking_tokens":15},"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":2603},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":484,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXoxTCwbNbuaZDC8hBfn","requestRef":"4c418ac2-bd9a-412f-abef-4bacf2a830da","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"46679e4b-930f-4c1c-9c10-ff928e155a03","timestamp":"2026-10-30T04:43:29.326Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0150","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"46679e4b-930f-4c1c-9c10-ff928e155a03","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoxTXXt2zetwEGVqCSw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014DipVHbZ4KKFqDr6NS5eEW","name":"Bash","input":{"command":"cat TASK.md; 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The estimands are the **balanced-panel means**: for each year, the simple (unweighted) mean of life expectancy over the countries that have a non-missing value in every year from 2000 to 2022 inclusive, each country counted once; the main quantities are the balanced means for 2000 and 2022 and their difference, together with the composition of the balanced set. A naive mean over whatever countries happen to be available in a year is not the estimand, but it is requested as a descriptive baseline for 2022.\n\n## Inputs\n- `data/life_expectancy_panel.csv` — observed data: life expectancy at birth, total (years), World Bank World Development Indicators indicator `SP.DYN.LE00.IN`, 20 countries, years 2000–2022. Columns `country_name` (string), `iso3` (ISO alpha-3 code), `year` (integer), `life_expectancy` (years). Source URL, retrieval date, license and SHA-256 are in `data/README.md`. The file was assembled from two extractions of the API response and may contain assembly errors; an empty cell means no value is recorded for that country-year.\n\n## Information-availability rules\n- Use only `data/life_expectancy_panel.csv`. Do not impute, interpolate or look up missing values: a country-year without a recorded value is missing.\n- A country is identified by its `iso3` code; each country contributes at most one observation per year.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-02\" |\n| `n_countries_total` | integer | countries | distinct countries (iso3) present in the file |\n| `n_countries_balanced` | integer | countries | countries with a recorded value in every year 2000–2022 |\n| `n_countries_incomplete` | integer | countries | distinct countries not in the balanced set |\n| `balanced_countries` | array of strings | — | iso3 codes of the balanced set, sorted |\n| `n_analysed` | integer | country-years | rows of the balanced analytic dataset (countries × years) |\n| `mean_2000_balanced` | number | years | balanced-panel mean life expectancy in 2000 |\n| `mean_2022_balanced` | number | years | balanced-panel mean life expectancy in 2022 |\n| `change_2000_2022_balanced` | number | years | `mean_2022_balanced − mean_2000_balanced` |\n| `mean_2022_all_available` | number | years | baseline: mean over all distinct countries with a recorded 2022 value, each counted once |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected and how it was handled (empty list if none) |\n| `n_input_rows` | object | rows | {\"life_expectancy_panel.csv\": number of data rows read (excluding the header)} |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — the balanced analytic dataset: one row per country-year of the balanced set, columns `iso3`, `country_name`, `year`, `life_expectancy`; exactly `n_analysed` rows, one row per (iso3, year) pair.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the number of rows read to `n_analysed` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`).\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/analytic.csv` and `out/exclusions.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 15 minutes of work.\ntotal 32\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:41 .\ndrwxr-xr-x  93 simon-pierreboucher  staff  2976 oct.  30 00:43 ..\n-rw-r--r--   1 simon-pierreboucher  staff   525 oct.  29 23:41 bundle.json\ndrwxr-xr-x   4 simon-pierreboucher  staff   128 oct.  29 23:35 data\n-rw-r--r--   1 simon-pierreboucher  staff  1509 oct.  29 23:35 gate_spec.json\n-rw-r--r--   1 simon-pierreboucher  staff  4324 oct.  29 23:41 TASK.md\ndata:\ntotal 32\ndrwxr-xr-x  4 simon-pierreboucher  staff    128 oct.  29 23:35 .\ndrwxr-xr-x  6 simon-pierreboucher  staff    192 oct.  29 23:41 ..\n-rw-r--r--  1 simon-pierreboucher  staff  11030 oct.  29 23:33 life_expectancy_panel.csv\n-rw-r--r--  1 simon-pierreboucher  staff   1416 oct.  29 23:35 README.md","is_error":true,"tool_use_id":"toolu_014DipVHbZ4KKFqDr6NS5eEW"}]},"uuid":"1326fe15-4193-419d-9cb9-8fd7d4709ac3","timestamp":"2026-10-30T04:43:29.895Z","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. 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SHA-256 of the raw API response: `ead88ee94d5b6cc6f8a055c9f2ec79f232c37faf8baca0ea122dbfc0550ff27e`\n- License: World Bank open data, Creative Commons Attribution 4.0 (CC BY-4.0).\n- Columns: `country_name` (string), `iso3` (ISO-3166 alpha-3 code as returned by the API), `year` (integer), `life_expectancy` (years, 3 decimals). Values were rounded to 3 decimals; nothing else was changed in the values.\n- The panel was assembled by the task author from two extractions of the API response (one earlier, narrower extraction and one later, wider one) and may contain assembly errors. An empty `life_expectancy` cell means no value was recorded for that country-year in the file.\n{\n  \"task_id\": \"F1-conf-02\",\n  \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/analytic.csv\", \"out/exclusions.csv\"],\n  \"results_keys\": {\n    \"task_id\": {\"type\": \"string\", \"nullable\": false},\n    \"n_countries_total\": {\"type\": \"integer\", \"nullable\": false},\n    \"n_countries_balanced\": {\"type\": \"integer\", \"nullable\": false},\n    \"n_countries_incomplete\": {\"type\": \"integer\", \"nullable\": false},\n    \"balanced_countries\": {\"type\": \"array\", \"nullable\": false},\n    \"n_analysed\": {\"type\": \"integer\", \"nullable\": false},\n    \"mean_2000_balanced\": {\"type\": \"number\", \"nullable\": false},\n    \"mean_2022_balanced\": {\"type\": \"number\", \"nullable\": false},\n    \"change_2000_2022_balanced\": {\"type\": \"number\", \"nullable\": false},\n    \"mean_2022_all_available\": {\"type\": \"number\", \"nullable\": false},\n    \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n    \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n    \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n    \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n    \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n  },\n  \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n  \"family_checks\": [\"exclusion_ledger_sums\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\"],\n  \"ci_pairs\": [],\n  \"positive_keys\": [],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-6\n}\n{\n  \"task_id\": \"F1-conf-02\",\n  \"family\": \"F1\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:41:11.640136+00:00\",\n  \"files\": {\n    \"TASK.md\": \"979bd92bb25ae3dda2a47ef6fce427a1db4a9c3f848a86bf09c4f424fcfe6dcb\",\n    \"data/README.md\": \"e9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0\",\n    \"data/life_expectancy_panel.csv\": \"b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac\",\n    \"gate_spec.json\": \"5e2280ad93e5a7edb7d300c499d0e2a12c442513edfe1f25b85c24c282bc01bc\"\n  }\n}b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac  data/life_expectancy_panel.csv\ne9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0  data/README.md\ncountry_name,iso3,year,life_expectancy\nAustralia,AUS,2000,79.234\nAustralia,AUS,2001,79.634\nAustralia,AUS,2002,79.937\nAustralia,AUS,2003,80.239\n     440 data/life_expectancy_panel.csv","is_error":false}]},"uuid":"aabac4a1-b9bf-481e-bf47-e2f7285fdf04","timestamp":"2026-10-30T04:43:31.710Z","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}\n{\n  \"task_id\": \"F1-conf-02\",\n  \"family\": \"F1\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:41:11.640136+00:00\",\n  \"files\": {\n    \"TASK.md\": \"979bd92bb25ae3dda2a47ef6fce427a1db4a9c3f848a86bf09c4f424fcfe6dcb\",\n    \"data/README.md\": \"e9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0\",\n    \"data/life_expectancy_panel.csv\": \"b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac\",\n    \"gate_spec.json\": \"5e2280ad93e5a7edb7d300c499d0e2a12c442513edfe1f25b85c24c282bc01bc\"\n  }\n}b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac  data/life_expectancy_panel.csv\ne9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0  data/README.md\ncountry_name,iso3,year,life_expectancy\nAustralia,AUS,2000,79.234\nAustralia,AUS,2001,79.634\nAustralia,AUS,2002,79.937\nAustralia,AUS,2003,80.239\n     440 data/life_expectancy_panel.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"d4b4a5b7-09a5-4cdd-b591-ce0a27a0e39b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0150","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","version":"2.1.314","gitBranch":"HEAD"}21{"parentUuid":"aabac4a1-b9bf-481e-bf47-e2f7285fdf04","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoxaCNuXzeAXhjxodY1","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0175SQyVHJ8NM52aoWGHZyx2","name":"Bash","input":{"command":"cd data && ~/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.groupby(['iso3']).agg(n=('year','size'),names=('country_name',lambda s: sorted(set(s))),miss=('life_expectancy',lambda s:s.isna().sum()),ymin=('year','min'),ymax=('year','max')))\ndup=d[d.duplicated(['iso3','year'],keep=False)].sort_values(['iso3','year']); print(dup)\nprint(d[d.life_expectancy.isna()])\nprint(d[(d.life_expectancy<30)|(d.life_expectancy>90)])\nprint(d.iso3.str.len().value_counts(), d.iso3.unique())\n\"","description":"Audit data contents"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2169,"cache_read_input_tokens":4107,"output_tokens":520,"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":2169},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXoxZtWjPBL7KSkE54x6","requestRef":"ee93205d-ca51-4b7c-892f-d3076edac1d8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"361a8f7d-7b82-4aee-b524-18292725924d","timestamp":"2026-10-30T04:43:34.349Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0150","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","version":"2.1.314","gitBranch":"HEAD"}22{"parentUuid":"361a8f7d-7b82-4aee-b524-18292725924d","isSidechain":false,"promptId":"6d9633a6-419b-4786-8d68-b31e015f4286","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0175SQyVHJ8NM52aoWGHZyx2","type":"tool_result","content":"country_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\n       n              names  miss  ymin  ymax\niso3                                         \nAUS   23        [Australia]     0  2000  2022\nAUT   23          [Austria]     0  2000  2022\nBEL   23          [Belgium]     0  2000  2022\nBGD   12       [Bangladesh]     0  2011  2022\nCAN   23           [Canada]     0  2000  2022\nCHE   23      [Switzerland]     1  2000  2022\nDEU   23          [Germany]     0  2000  2022\nESP   23            [Spain]     0  2000  2022\nETH   12         [Ethiopia]     0  2011  2022\nFRA   23           [France]     0  2000  2022\nGBR   23   [United Kingdom]     0  2000  2022\nIND   12            [India]     0  2011  2022\nITA   23            [Italy]     0  2000  2022\nJPN   23            [Japan]     0  2000  2022\nKOR   23      [Korea, Rep.]     0  2000  2022\nNGA   12          [Nigeria]     0  2011  2022\nNLD   23      [Netherlands]     0  2000  2022\nSWE   23           [Sweden]     0  2000  2022\nTUR   46  [Turkey, Turkiye]     0  2000  2022\nUSA   23    [United States]     0  2000  2022\n    country_name iso3  year  life_expectancy\n347       Turkey  TUR  2000           71.931\n370      Turkiye  TUR  2000           71.931\n348       Turkey  TUR  2001           72.271\n371      Turkiye  TUR  2001           72.271\n349       Turkey  TUR  2002           72.605\n372      Turkiye  TUR  2002           72.605\n350       Turkey  TUR  2003           72.906\n373      Turkiye  TUR  2003           72.906\n351       Turkey  TUR  2004           73.223\n374      Turkiye  TUR  2004           73.223\n352       Turkey  TUR  2005           73.510\n375      Turkiye  TUR  2005           73.510\n353       Turkey  TUR  2006           73.804\n376      Turkiye  TUR  2006           73.804\n354       Turkey  TUR  2007           74.129\n377      Turkiye  TUR  2007           74.129\n355       Turkey  TUR  2008           74.380\n378      Turkiye  TUR  2008           74.380\n356       Turkey  TUR  2009           74.679\n379      Turkiye  TUR  2009           74.679\n357       Turkey  TUR  2010           74.964\n380      Turkiye  TUR  2010           74.964\n358       Turkey  TUR  2011           74.823\n381      Turkiye  TUR  2011           74.823\n359       Turkey  TUR  2012           75.552\n382      Turkiye  TUR  2012           75.552\n360       Turkey  TUR  2013           76.171\n383      Turkiye  TUR  2013           76.171\n361       Turkey  TUR  2014           76.452\n384      Turkiye  TUR  2014           76.452\n362       Turkey  TUR  2015           76.539\n385      Turkiye  TUR  2015           76.539\n363       Turkey  TUR  2016           76.595\n386      Turkiye  TUR  2016           76.595\n364       Turkey  TUR  2017           77.037\n387      Turkiye  TUR  2017           77.037\n365       Turkey  TUR  2018           77.454\n388      Turkiye  TUR  2018           77.454\n366       Turkey  TUR  2019           77.737\n389      Turkiye  TUR  2019           77.737\n367       Turkey  TUR  2020           76.525\n390      Turkiye  TUR  2020           76.525\n368       Turkey  TUR  2021           75.722\n391      Turkiye  TUR  2021           75.722\n369       Turkey  TUR  2022           77.591\n392      Turkiye  TUR  2022           77.591\n    country_name iso3  year  life_expectancy\n331  Switzerland  CHE  2007              NaN\nEmpty DataFrame\nColumns: [country_name, iso3, year, life_expectancy]\nIndex: []\niso3\n3    439\nName: count, dtype: int64 ['AUS' 'AUT' 'BGD' 'BEL' 'CAN' 'ETH' 'FRA' 'DEU' 'IND' 'ITA' 'JPN' 'KOR'\n 'NLD' 'NGA' 'ESP' 'SWE' 'CHE' 'TUR' 'GBR' 'USA']","is_error":false}]},"uuid":"721f1d81-6a12-4211-a15a-8cee06bb57b4","timestamp":"2026-10-30T04:43:34.642Z","toolUseResult":{"stdout":"country_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\n       n              names  miss  ymin  ymax\niso3                                         \nAUS   23        [Australia]     0  2000  2022\nAUT   23          [Austria]     0  2000  2022\nBEL   23          [Belgium]     0  2000  2022\nBGD   12       [Bangladesh]     0  2011  2022\nCAN   23           [Canada]     0  2000  2022\nCHE   23      [Switzerland]     1  2000  2022\nDEU   23          [Germany]     0  2000  2022\nESP   23            [Spain]     0  2000  2022\nETH   12         [Ethiopia]     0  2011  2022\nFRA   23           [France]     0  2000  2022\nGBR   23   [United Kingdom]     0  2000  2022\nIND   12            [India]     0  2011  2022\nITA   23            [Italy]     0  2000  2022\nJPN   23            [Japan]     0  2000  2022\nKOR   23      [Korea, Rep.]     0  2000  2022\nNGA   12          [Nigeria]     0  2011  2022\nNLD   23      [Netherlands]     0  2000  2022\nSWE   23           [Sweden]     0  2000  2022\nTUR   46  [Turkey, Turkiye]     0  2000  2022\nUSA   23    [United States]     0  2000  2022\n    country_name iso3  year  life_expectancy\n347       Turkey  TUR  2000           71.931\n370      Turkiye  TUR  2000           71.931\n348       Turkey  TUR  2001           72.271\n371      Turkiye  TUR  2001           72.271\n349       Turkey  TUR  2002           72.605\n372      Turkiye  TUR  2002           72.605\n350       Turkey  TUR  2003           72.906\n373      Turkiye  TUR  2003           72.906\n351       Turkey  TUR  2004           73.223\n374      Turkiye  TUR  2004           73.223\n352       Turkey  TUR  2005           73.510\n375      Turkiye  TUR  2005           73.510\n353       Turkey  TUR  2006           73.804\n376      Turkiye  TUR  2006           73.804\n354       Turkey  TUR  2007           74.129\n377      Turkiye  TUR  2007           74.129\n355       Turkey  TUR  2008           74.380\n378      Turkiye  TUR  2008           74.380\n356       Turkey  TUR  2009           74.679\n379      Turkiye  TUR  2009           74.679\n357       Turkey  TUR  2010           74.964\n380      Turkiye  TUR  2010           74.964\n358       Turkey  TUR  2011           74.823\n381      Turkiye  TUR  2011           74.823\n359       Turkey  TUR  2012           75.552\n382      Turkiye  TUR  2012           75.552\n360       Turkey  TUR  2013           76.171\n383      Turkiye  TUR  2013           76.171\n361       Turkey  TUR  2014           76.452\n384      Turkiye  TUR  2014           76.452\n362       Turkey  TUR  2015           76.539\n385      Turkiye  TUR  2015           76.539\n363       Turkey  TUR  2016           76.595\n386      Turkiye  TUR  2016           76.595\n364       Turkey  TUR  2017           77.037\n387      Turkiye  TUR  2017           77.037\n365       Turkey  TUR  2018           77.454\n388      Turkiye  TUR  2018           77.454\n366       Turkey  TUR  2019           77.737\n389      Turkiye  TUR  2019           77.737\n367       Turkey  TUR  2020           76.525\n390      Turkiye  TUR  2020           76.525\n368       Turkey  TUR  2021           75.722\n391      Turkiye  TUR  2021           75.722\n369       Turkey  TUR  2022           77.591\n392      Turkiye  TUR  2022           77.591\n    country_name iso3  year  life_expectancy\n331  Switzerland  CHE  2007              NaN\nEmpty DataFrame\nColumns: [country_name, iso3, year, life_expectancy]\nIndex: []\niso3\n3    439\nName: count, dtype: int64 ['AUS' 'AUT' 'BGD' 'BEL' 'CAN' 'ETH' 'FRA' 'DEU' 'IND' 'ITA' 'JPN' 'KOR'\n 'NLD' 'NGA' 'ESP' 'SWE' 'CHE' 'TUR' 'GBR' 'USA']","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"361a8f7d-7b82-4aee-b524-18292725924d","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0150/data","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","version":"2.1.314","gitBranch":"HEAD"}23{"type":"api-request-blob","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","hash":"cb42da6731aeb0971182c132499c16662b3cad0e17181d90cb5748238c013998","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01UkJ9L6rkXtLURFPuYvJWkz","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json bundle.json; shasum -a 256 data/*; head -5 data/life_expectancy_panel.csv; wc -l data/life_expectancy_panel.csv","description":"Inspect README, specs, data head"}},{"type":"tool_use","id":"toolu_0175SQyVHJ8NM52aoWGHZyx2","name":"Bash","input":{"command":"cd data && ~/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.groupby(['iso3']).agg(n=('year','size'),names=('country_name',lambda s: sorted(set(s))),miss=('life_expectancy',lambda s:s.isna().sum()),ymin=('year','min'),ymax=('year','max')))\ndup=d[d.duplicated(['iso3','year'],keep=False)].sort_values(['iso3','year']); print(dup)\nprint(d[d.life_expectancy.isna()])\nprint(d[(d.life_expectancy<30)|(d.life_expectancy>90)])\nprint(d.iso3.str.len().value_counts(), d.iso3.unique())\n\"","description":"Audit data contents"}}]}}24{"type":"api-request-blob","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","hash":"40b22281052f4f8e5932ff87eaeabc13650729d9bd7dd427ebff0eea705525f8","message":{"role":"user","content":[{"tool_use_id":"toolu_01UkJ9L6rkXtLURFPuYvJWkz","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}\n{\n  \"task_id\": \"F1-conf-02\",\n  \"family\": \"F1\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:41:11.640136+00:00\",\n  \"files\": {\n    \"TASK.md\": \"979bd92bb25ae3dda2a47ef6fce427a1db4a9c3f848a86bf09c4f424fcfe6dcb\",\n    \"data/README.md\": \"e9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0\",\n    \"data/life_expectancy_panel.csv\": \"b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac\",\n    \"gate_spec.json\": \"5e2280ad93e5a7edb7d300c499d0e2a12c442513edfe1f25b85c24c282bc01bc\"\n  }\n}b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac  data/life_expectancy_panel.csv\ne9099d209a7642b2b2151161277222719045c3c1094e9490a5d02faa316353d0  data/README.md\ncountry_name,iso3,year,life_expectancy\nAustralia,AUS,2000,79.234\nAustralia,AUS,2001,79.634\nAustralia,AUS,2002,79.937\nAustralia,AUS,2003,80.239\n     440 data/life_expectancy_panel.csv","is_error":false},{"tool_use_id":"toolu_0175SQyVHJ8NM52aoWGHZyx2","type":"tool_result","content":"country_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\n       n              names  miss  ymin  ymax\niso3                                         \nAUS   23        [Australia]     0  2000  2022\nAUT   23          [Austria]     0  2000  2022\nBEL   23          [Belgium]     0  2000  2022\nBGD   12       [Bangladesh]     0  2011  2022\nCAN   23           [Canada]     0  2000  2022\nCHE   23      [Switzerland]     1  2000  2022\nDEU   23          [Germany]     0  2000  2022\nESP   23            [Spain]     0  2000  2022\nETH   12         [Ethiopia]     0  2011  2022\nFRA   23           [France]     0  2000  2022\nGBR   23   [United Kingdom]     0  2000  2022\nIND   12            [India]     0  2011  2022\nITA   23            [Italy]     0  2000  2022\nJPN   23            [Japan]     0  2000  2022\nKOR   23      [Korea, Rep.]     0  2000  2022\nNGA   12          [Nigeria]     0  2011  2022\nNLD   23      [Netherlands]     0  2000  2022\nSWE   23           [Sweden]     0  2000  2022\nTUR   46  [Turkey, Turkiye]     0  2000  2022\nUSA   23    [United States]     0  2000  2022\n    country_name iso3  year  life_expectancy\n347       Turkey  TUR  2000           71.931\n370      Turkiye  TUR  2000           71.931\n348       Turkey  TUR  2001           72.271\n371      Turkiye  TUR  2001           72.271\n349       Turkey  TUR  2002           72.605\n372      Turkiye  TUR  2002           72.605\n350       Turkey  TUR  2003           72.906\n373      Turkiye  TUR  2003           72.906\n351       Turkey  TUR  2004           73.223\n374      Turkiye  TUR  2004           73.223\n352       Turkey  TUR  2005           73.510\n375      Turkiye  TUR  2005           73.510\n353       Turkey  TUR  2006           73.804\n376      Turkiye  TUR  2006           73.804\n354       Turkey  TUR  2007           74.129\n377      Turkiye  TUR  2007           74.129\n355       Turkey  TUR  2008           74.380\n378      Turkiye  TUR  2008           74.380\n356       Turkey  TUR  2009           74.679\n379      Turkiye  TUR  2009           74.679\n357       Turkey  TUR  2010           74.964\n380      Turkiye  TUR  2010           74.964\n358       Turkey  TUR  2011           74.823\n381      Turkiye  TUR  2011           74.823\n359       Turkey  TUR  2012           75.552\n382      Turkiye  TUR  2012           75.552\n360       Turkey  TUR  2013           76.171\n383      Turkiye  TUR  2013           76.171\n361       Turkey  TUR  2014           76.452\n384      Turkiye  TUR  2014           76.452\n362       Turkey  TUR  2015           76.539\n385      Turkiye  TUR  2015           76.539\n363       Turkey  TUR  2016           76.595\n386      Turkiye  TUR  2016           76.595\n364       Turkey  TUR  2017           77.037\n387      Turkiye  TUR  2017           77.037\n365       Turkey  TUR  2018           77.454\n388      Turkiye  TUR  2018           77.454\n366       Turkey  TUR  2019           77.737\n389      Turkiye  TUR  2019           77.737\n367       Turkey  TUR  2020           76.525\n390      Turkiye  TUR  2020           76.525\n368       Turkey  TUR  2021           75.722\n391      Turkiye  TUR  2021           75.722\n369       Turkey  TUR  2022           77.591\n392      Turkiye  TUR  2022           77.591\n    country_name iso3  year  life_expectancy\n331  Switzerland  CHE  2007              NaN\nEmpty DataFrame\nColumns: [country_name, iso3, year, life_expectancy]\nIndex: []\niso3\n3    439\nName: count, dtype: int64 ['AUS' 'AUT' 'BGD' 'BEL' 'CAN' 'ETH' 'FRA' 'DEU' 'IND' 'ITA' 'JPN' 'KOR'\n 'NLD' 'NGA' 'ESP' 'SWE' 'CHE' 'TUR' 'GBR' 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data && grep -n '  \\|\\s$\\|\"' life_expectancy_panel.csv | head; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('life_expectancy_panel.csv').drop_duplicates(['iso3','year']).sort_values(['iso3','year'])\nd['dif']=d.groupby('iso3').life_expectancy.diff()\nprint(d[d.dif.abs()>1.5])\nprint(d.groupby('iso3').life_expectancy.agg(['min','max']))\n\"","description":"Check whitespace and implausible jumps"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4165,"cache_read_input_tokens":6276,"output_tokens":406,"output_tokens_details":{"thinking_tokens":138},"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":4165},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXoxv7BsdAJksgFKQ7U9","requestRef":"0c9075a1-142a-40ea-a57d-85ad768be6d0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4ada8663-466f-454d-8bbf-3088db9e2b60","timestamp":"2026-10-30T04:43:38.315Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0150/data","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","version":"2.1.314","gitBranch":"HEAD"}30{"parentUuid":"4ada8663-466f-454d-8bbf-3088db9e2b60","isSidechain":false,"promptId":"6d9633a6-419b-4786-8d68-b31e015f4286","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UVNSXE3oHcsDob6MWyNWvc","type":"tool_result","content":"(eval):cd:1: no such file or directory: data\n      country_name iso3  year  life_expectancy    dif\n57      Bangladesh  BGD  2022           74.265  3.162\n298          Spain  ESP  2020           82.232 -1.600\n115       Ethiopia  ETH  2022           66.897  1.567\n172          India  IND  2021           67.282 -2.874\n173          India  IND  2022           71.698  4.416\n369         Turkey  TUR  2022           77.591  1.869\n436  United States  USA  2020           76.980 -1.808\n         min     max\niso3                \nAUS   79.234  83.300\nAUT   78.127  81.895\nBEL   77.722  81.995\nBGD   68.499  74.265\nCAN   79.167  82.159\nCHE   79.680  83.905\nDEU   77.927  81.293\nESP   78.966  83.832\nETH   60.595  66.897\nFRA   79.056  82.827\nGBR   77.741  81.369\nIND   67.282  71.698\nITA   79.778  83.498\nJPN   81.076  84.560\nKOR   75.910  83.527\nNGA   51.490  54.079\nNLD   77.988  82.112\nSWE   79.644  83.110\nTUR   71.931  77.737\nUSA   76.329  78.841","is_error":false}]},"uuid":"aba72299-77e4-4125-bd8b-6e3596c04ad0","timestamp":"2026-10-30T04:43:38.581Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: data\n      country_name iso3  year  life_expectancy    dif\n57      Bangladesh  BGD  2022           74.265  3.162\n298          Spain  ESP  2020           82.232 -1.600\n115       Ethiopia  ETH  2022           66.897  1.567\n172          India  IND  2021           67.282 -2.874\n173          India  IND  2022           71.698  4.416\n369         Turkey  TUR  2022           77.591  1.869\n436  United States  USA  2020           76.980 -1.808\n         min     max\niso3                \nAUS   79.234  83.300\nAUT   78.127  81.895\nBEL   77.722  81.995\nBGD   68.499  74.265\nCAN   79.167  82.159\nCHE   79.680  83.905\nDEU   77.927  81.293\nESP   78.966  83.832\nETH   60.595  66.897\nFRA   79.056  82.827\nGBR   77.741  81.369\nIND   67.282  71.698\nITA   79.778  83.498\nJPN   81.076  84.560\nKOR   75.910  83.527\nNGA   51.490  54.079\nNLD   77.988  82.112\nSWE   79.644  83.110\nTUR   71.931  77.737\nUSA   76.329  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data && grep -n '  \\|\\s$\\|\"' life_expectancy_panel.csv | head; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('life_expectancy_panel.csv').drop_duplicates(['iso3','year']).sort_values(['iso3','year'])\nd['dif']=d.groupby('iso3').life_expectancy.diff()\nprint(d[d.dif.abs()>1.5])\nprint(d.groupby('iso3').life_expectancy.agg(['min','max']))\n\"","description":"Check whitespace and implausible jumps"}}]}}32{"type":"api-request","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","id":"e3502d08-1aa7-45c3-8cad-55a6cede7dcb","timestamp":"2026-10-30T04:43:38.585Z","version":"2.1.314","querySource":"sdk","shapeHash":"eeb19a4c232b3a4a5700999f6d7939186b1a0f6934547979b35794975d4faf55","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":"0c9075a1-142a-40ea-a57d-85ad768be6d0","keep":6,"tail":["ad7ea073e568b48a28af7695dd6aa6cda545a70f94ec4619e0bad0df6ea89e92","a0bdbcce6ef7d09fd5d811b61626a4f60d706b54f70dcbad7d8969d041b6e12e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5ad38ac183a43deea191eaccf867865a6ba0570facf726c2b084f12f0b120f1f"}33{"parentUuid":"aba72299-77e4-4125-bd8b-6e3596c04ad0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoyDFaaSqhM35idSqyK","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":845,"cache_read_input_tokens":10441,"output_tokens":2944,"output_tokens_details":{"thinking_tokens":283},"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":845},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2727,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXoyCxTMpZoeHTLE822R","requestRef":"e3502d08-1aa7-45c3-8cad-55a6cede7dcb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e5b6d844-b306-430e-a132-fd546343129e","timestamp":"2026-10-30T04:43:42.757Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0150/data","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","version":"2.1.314","gitBranch":"HEAD"}34{"parentUuid":"e5b6d844-b306-430e-a132-fd546343129e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoyDFaaSqhM35idSqyK","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SxeTm3BK9Qn3Uk7VRn9mFE","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0150 && mkdir -p out/state && cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-02: balanced-panel mean life expectancy 2000-2022.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/analytic.csv, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport pandas as pd\n\nDATA = \"data/life_expectancy_panel.csv\"\nOUT = \"out\"\nYEARS = list(range(2000, 2023))\nos.makedirs(OUT, exist_ok=True)\n\nraw = pd.read_csv(DATA, dtype={\"iso3\": str, \"country_name\": str})\nn_raw = len(raw)\nfor c in [\"iso3\", \"country_name\"]:\n    raw[c] = raw[c].str.strip()\nexcl = []\nissues = []\n\ndef step(name, rule, before, after, why):\n    excl.append(dict(step=name, rule=rule, n_before=len(before), n_after=len(after),\n                     n_removed=len(before) - len(after), rationale=why))\n\n# Step 1: restrict to study window\nd0 = raw\nd1 = d0[d0[\"year\"].between(2000, 2022)].copy()\nstep(\"1_year_window\", \"keep 2000 <= year <= 2022\", d0, d1, \"study window defined by the task\")\n\n# Step 2: duplicated (iso3, year) rows\ndup_mask = d1.duplicated([\"iso3\", \"year\"], keep=False)\ndups = d1[dup_mask]\nconflicting = dups.groupby([\"iso3\", \"year\"])[\"life_expectancy\"].nunique(dropna=False)\nconflicting = conflicting[conflicting > 1]\nif len(conflicting):\n    raise SystemExit(f\"Conflicting duplicate values found: {conflicting}\")\nfor iso, g in dups.groupby(\"iso3\"):\n    names = sorted(g[\"country_name\"].unique())\n    issues.append({\n        \"issue\": f\"{iso}: {g[['iso3','year']].drop_duplicates().shape[0]} country-years appear \"\n                 f\"{len(g)} times in total under country names {names} (two extractions; API renamed the \"\n                 f\"country); duplicate values are identical\",\n        \"handling\": \"country identified by iso3; kept one row per (iso3, year) (first occurrence); \"\n                    \"values identical so choice does not affect results\"})\nd2 = d1.drop_duplicates([\"iso3\", \"year\"], keep=\"first\").copy()\nstep(\"2_dedup_iso3_year\", \"keep one row per (iso3, year); duplicates have identical values\", d1, d2,\n     \"each country contributes at most one observation per year (assembly duplicates)\")\n# harmonise display name to the most recent spelling (last occurrence in file) per iso3\nlatest_name = d1.groupby(\"iso3\")[\"country_name\"].last()\nd2[\"country_name\"] = d2[\"iso3\"].map(latest_name)\n\n# Completeness per country\nmiss_rows = d2[d2[\"life_expectancy\"].isna()]\nfor _, r in miss_rows.iterrows():\n    issues.append({\"issue\": f\"{r.iso3} {int(r.year)}: empty life_expectancy cell\",\n                   \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"})\nobs = d2.dropna(subset=[\"life_expectancy\"])\nyears_by = obs.groupby(\"iso3\")[\"year\"].apply(set)\nall_iso = sorted(d2[\"iso3\"].unique())\nbalanced = sorted(i for i in all_iso if set(YEARS) <= years_by.get(i, set()))\nincomplete = [i for i in all_iso if i not in balanced]\nfor i in incomplete:\n    present = years_by.get(i, set())\n    missing_years = sorted(set(YEARS) - present)\n    if len(missing_years) > 1 and missing_years == list(range(missing_years[0], missing_years[-1] + 1)):\n        desc = f\"{missing_years[0]}-{missing_years[-1]}\"\n    else:\n        desc = \",\".join(map(str, missing_years))\n    if not (len(missing_years) == 1 and i in set(miss_rows.iso3)):\n        issues.append({\"issue\": f\"{i}: no rows/values for years {desc} ({len(missing_years)} of 23 years missing)\",\n                       \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"})\n\n# Step 3: drop rows with empty value\nd3 = d2.dropna(subset=[\"life_expectancy\"])\nstep(\"3_drop_missing_value\", \"drop rows with empty life_expectancy\", d2, d3,\n     \"a country-year without a recorded value is missing; no imputation\")\n# Step 4: drop countries not observed in all 23 years\nd4 = d3[d3[\"iso3\"].isin(balanced)]\nstep(\"4_balanced_panel\", \"keep only iso3 with a recorded value in every year 2000-2022\", d3, d4,\n     f\"balanced panel; excluded countries: {', '.join(incomplete)}\")\n\nanalytic = d4[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]].sort_values([\"iso3\", \"year\"]).reset_index(drop=True)\nassert len(analytic) == len(balanced) * len(YEARS)\nassert not analytic.duplicated([\"iso3\", \"year\"]).any()\nanalytic.to_csv(f\"{OUT}/analytic.csv\", index=False)\npd.DataFrame(excl).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nm = analytic.groupby(\"year\")[\"life_expectancy\"].mean()\nm2000, m2022 = float(m[2000]), float(m[2022])\navail22 = d3[d3[\"year\"] == 2022]  # already one row per iso3\nresults = {\n    \"task_id\": \"F1-conf-02\",\n    \"n_countries_total\": len(all_iso),\n    \"n_countries_balanced\": len(balanced),\n    \"n_countries_incomplete\": len(incomplete),\n    \"balanced_countries\": balanced,\n    \"n_analysed\": int(len(analytic)),\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(float(avail22[\"life_expectancy\"].mean()), 6),\n    \"n_countries_2022_available\": int(avail22[\"iso3\"].nunique()),\n    \"incomplete_countries\": incomplete,\n    \"balanced_means_by_year\": {str(y): round(float(v), 6) for y, v in m.items()},\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"life_expectancy_panel.csv\": int(n_raw)},\n    \"random_seed\": None,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(results, f, indent=2)\nprint(json.dumps({k: results[k] for k in [\"n_countries_total\", \"n_countries_balanced\", \"n_analysed\",\n      \"mean_2000_balanced\", \"mean_2022_balanced\", \"change_2000_2022_balanced\", \"mean_2022_all_available\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && python3 -c \"import json;print(json.dumps(json.load(open('out/results.json'))['data_issues'],indent=1))\"","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":845,"cache_read_input_tokens":10441,"output_tokens":2944,"output_tokens_details":{"thinking_tokens":283},"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":845},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXoyCxTMpZoeHTLE822R","requestRef":"e3502d08-1aa7-45c3-8cad-55a6cede7dcb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1e515573-9df1-4fcc-b23c-0909a9fef39b","timestamp":"2026-10-30T04:44:02.636Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0150/data","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"1e515573-9df1-4fcc-b23c-0909a9fef39b","isSidechain":false,"promptId":"6d9633a6-419b-4786-8d68-b31e015f4286","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SxeTm3BK9Qn3Uk7VRn9mFE","type":"tool_result","content":"{\n \"n_countries_total\": 20,\n \"n_countries_balanced\": 15,\n \"n_analysed\": 345,\n \"mean_2000_balanced\": 78.060267,\n \"mean_2022_balanced\": 81.553067,\n \"change_2000_2022_balanced\": 3.4928,\n \"mean_2022_all_available\": 78.692\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1_year_window,keep 2000 <= year <= 2022,439,439,0,study window defined by the task\n2_dedup_iso3_year,\"keep one row per (iso3, year); duplicates have identical values\",439,416,23,each country contributes at most one observation per year (assembly duplicates)\n3_drop_missing_value,drop rows with empty life_expectancy,416,415,1,a country-year without a recorded value is missing; no imputation\n4_balanced_panel,keep only iso3 with a recorded value in every year 2000-2022,415,345,70,\"balanced panel; excluded countries: BGD, CHE, ETH, IND, NGA\"\n[\n {\n  \"issue\": \"TUR: 23 country-years appear 46 times in total under country names ['Turkey', 'Turkiye'] (two extractions; API renamed the country); duplicate values are identical\",\n  \"handling\": \"country identified by iso3; kept one row per (iso3, year) (first occurrence); values identical so choice does not affect results\"\n },\n {\n  \"issue\": \"CHE 2007: empty life_expectancy cell\",\n  \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n  \"issue\": \"BGD: no rows/values for years 2000-2010 (11 of 23 years missing)\",\n  \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n  \"issue\": \"ETH: no rows/values for years 2000-2010 (11 of 23 years missing)\",\n  \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n  \"issue\": \"IND: no rows/values for years 2000-2010 (11 of 23 years missing)\",\n  \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n  \"issue\": \"NGA: no rows/values for years 2000-2010 (11 of 23 years missing)\",\n  \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n }\n]","is_error":false}]},"uuid":"f3ed111c-fc8e-4a28-a9e7-d6f360b355b1","timestamp":"2026-10-30T04:44:02.936Z","toolUseResult":{"stdout":"{\n \"n_countries_total\": 20,\n \"n_countries_balanced\": 15,\n \"n_analysed\": 345,\n \"mean_2000_balanced\": 78.060267,\n \"mean_2022_balanced\": 81.553067,\n \"change_2000_2022_balanced\": 3.4928,\n \"mean_2022_all_available\": 78.692\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1_year_window,keep 2000 <= year <= 2022,439,439,0,study window defined by the task\n2_dedup_iso3_year,\"keep one row per (iso3, year); duplicates have identical values\",439,416,23,each country contributes at most one observation per year (assembly duplicates)\n3_drop_missing_value,drop rows with empty life_expectancy,416,415,1,a country-year without a recorded value is missing; no imputation\n4_balanced_panel,keep only iso3 with a recorded value in every year 2000-2022,415,345,70,\"balanced panel; excluded countries: BGD, CHE, ETH, IND, NGA\"\n[\n {\n  \"issue\": \"TUR: 23 country-years appear 46 times in total under country names ['Turkey', 'Turkiye'] (two extractions; API renamed the country); duplicate values are identical\",\n  \"handling\": \"country identified by iso3; kept one row per (iso3, year) (first occurrence); values identical so choice does not affect results\"\n },\n {\n  \"issue\": \"CHE 2007: empty life_expectancy cell\",\n  \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n  \"issue\": \"BGD: no rows/values for years 2000-2010 (11 of 23 years missing)\",\n  \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n  \"issue\": \"ETH: no rows/values for years 2000-2010 (11 of 23 years missing)\",\n  \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n  \"issue\": \"IND: no rows/values for years 2000-2010 (11 of 23 years missing)\",\n  \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n  \"issue\": \"NGA: no rows/values for years 2000-2010 (11 of 23 years missing)\",\n  \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n 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/Users/simon-pierreboucher/wp29/runs/RC0150 && mkdir -p out/state && cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-02: balanced-panel mean life expectancy 2000-2022.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/analytic.csv, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport pandas as pd\n\nDATA = \"data/life_expectancy_panel.csv\"\nOUT = \"out\"\nYEARS = list(range(2000, 2023))\nos.makedirs(OUT, exist_ok=True)\n\nraw = pd.read_csv(DATA, dtype={\"iso3\": str, \"country_name\": str})\nn_raw = len(raw)\nfor c in [\"iso3\", \"country_name\"]:\n    raw[c] = raw[c].str.strip()\nexcl = []\nissues = []\n\ndef step(name, rule, before, after, why):\n    excl.append(dict(step=name, rule=rule, n_before=len(before), n_after=len(after),\n                     n_removed=len(before) - len(after), rationale=why))\n\n# Step 1: restrict to study window\nd0 = raw\nd1 = d0[d0[\"year\"].between(2000, 2022)].copy()\nstep(\"1_year_window\", \"keep 2000 <= year <= 2022\", d0, d1, \"study window defined by the task\")\n\n# Step 2: duplicated (iso3, year) rows\ndup_mask = d1.duplicated([\"iso3\", \"year\"], keep=False)\ndups = d1[dup_mask]\nconflicting = dups.groupby([\"iso3\", \"year\"])[\"life_expectancy\"].nunique(dropna=False)\nconflicting = conflicting[conflicting > 1]\nif len(conflicting):\n    raise SystemExit(f\"Conflicting duplicate values found: {conflicting}\")\nfor iso, g in dups.groupby(\"iso3\"):\n    names = sorted(g[\"country_name\"].unique())\n    issues.append({\n        \"issue\": f\"{iso}: {g[['iso3','year']].drop_duplicates().shape[0]} country-years appear \"\n                 f\"{len(g)} times in total under country names {names} (two extractions; API renamed the \"\n                 f\"country); duplicate values are identical\",\n        \"handling\": \"country identified by iso3; kept one row per (iso3, year) (first occurrence); \"\n                    \"values identical so choice does not affect results\"})\nd2 = d1.drop_duplicates([\"iso3\", \"year\"], keep=\"first\").copy()\nstep(\"2_dedup_iso3_year\", \"keep one row per (iso3, year); duplicates have identical values\", d1, d2,\n     \"each country contributes at most one observation per year (assembly duplicates)\")\n# harmonise display name to the most recent spelling (last occurrence in file) per iso3\nlatest_name = d1.groupby(\"iso3\")[\"country_name\"].last()\nd2[\"country_name\"] = d2[\"iso3\"].map(latest_name)\n\n# Completeness per country\nmiss_rows = d2[d2[\"life_expectancy\"].isna()]\nfor _, r in miss_rows.iterrows():\n    issues.append({\"issue\": f\"{r.iso3} {int(r.year)}: empty life_expectancy cell\",\n                   \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"})\nobs = d2.dropna(subset=[\"life_expectancy\"])\nyears_by = obs.groupby(\"iso3\")[\"year\"].apply(set)\nall_iso = sorted(d2[\"iso3\"].unique())\nbalanced = sorted(i for i in all_iso if set(YEARS) <= years_by.get(i, set()))\nincomplete = [i for i in all_iso if i not in balanced]\nfor i in incomplete:\n    present = years_by.get(i, set())\n    missing_years = sorted(set(YEARS) - present)\n    if len(missing_years) > 1 and missing_years == list(range(missing_years[0], missing_years[-1] + 1)):\n        desc = f\"{missing_years[0]}-{missing_years[-1]}\"\n    else:\n        desc = \",\".join(map(str, missing_years))\n    if not (len(missing_years) == 1 and i in set(miss_rows.iso3)):\n        issues.append({\"issue\": f\"{i}: no rows/values for years {desc} ({len(missing_years)} of 23 years missing)\",\n                       \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"})\n\n# Step 3: drop rows with empty value\nd3 = d2.dropna(subset=[\"life_expectancy\"])\nstep(\"3_drop_missing_value\", \"drop rows with empty life_expectancy\", d2, d3,\n     \"a country-year without a recorded value is missing; no imputation\")\n# Step 4: drop countries not observed in all 23 years\nd4 = d3[d3[\"iso3\"].isin(balanced)]\nstep(\"4_balanced_panel\", \"keep only iso3 with a recorded value in every year 2000-2022\", d3, d4,\n     f\"balanced panel; excluded countries: {', '.join(incomplete)}\")\n\nanalytic = d4[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]].sort_values([\"iso3\", \"year\"]).reset_index(drop=True)\nassert len(analytic) == len(balanced) * len(YEARS)\nassert not analytic.duplicated([\"iso3\", \"year\"]).any()\nanalytic.to_csv(f\"{OUT}/analytic.csv\", index=False)\npd.DataFrame(excl).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nm = analytic.groupby(\"year\")[\"life_expectancy\"].mean()\nm2000, m2022 = float(m[2000]), float(m[2022])\navail22 = d3[d3[\"year\"] == 2022]  # already one row per iso3\nresults = {\n    \"task_id\": \"F1-conf-02\",\n    \"n_countries_total\": len(all_iso),\n    \"n_countries_balanced\": len(balanced),\n    \"n_countries_incomplete\": len(incomplete),\n    \"balanced_countries\": balanced,\n    \"n_analysed\": int(len(analytic)),\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(float(avail22[\"life_expectancy\"].mean()), 6),\n    \"n_countries_2022_available\": int(avail22[\"iso3\"].nunique()),\n    \"incomplete_countries\": incomplete,\n    \"balanced_means_by_year\": {str(y): round(float(v), 6) for y, v in m.items()},\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"life_expectancy_panel.csv\": int(n_raw)},\n    \"random_seed\": None,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(results, f, indent=2)\nprint(json.dumps({k: results[k] for k in [\"n_countries_total\", \"n_countries_balanced\", \"n_analysed\",\n      \"mean_2000_balanced\", \"mean_2022_balanced\", \"change_2000_2022_balanced\", \"mean_2022_all_available\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && python3 -c \"import json;print(json.dumps(json.load(open('out/results.json'))['data_issues'],indent=1))\"","description":"Write and run analysis 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the state files, checks, report and claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3771,"cache_read_input_tokens":11286,"output_tokens":1381,"output_tokens_details":{"thinking_tokens":74},"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":3771},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp116tHAAD1ZMcrE9Py","requestRef":"3f75b30c-9b10-4d67-ba0f-a4aeec96c15b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"be65e379-676e-4f32-845d-52d7f8fe48ce","timestamp":"2026-10-30T04:44:04.419Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0150","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","version":"2.1.314","gitBranch":"HEAD"}42{"parentUuid":"be65e379-676e-4f32-845d-52d7f8fe48ce","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp11QjhJw9MFoiQJRrG","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014t5Dcffqwao9qGJprMcrkM","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 /tmp/r1.json out/results.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json,pandas as pd\nr=json.load(open('out/results.json')); a=pd.read_csv('out/analytic.csv'); e=pd.read_csv('out/exclusions.csv')\nd=pd.read_csv('data/life_expectancy_panel.csv')\nchk=[]\nchk.append((\"n_analysed == rows analytic.csv\", len(a)==r['n_analysed']))\nchk.append((\"n_analysed == n_balanced*23\", r['n_analysed']==r['n_countries_balanced']*23))\nchk.append((\"balanced+incomplete==total\", r['n_countries_balanced']+r['n_countries_incomplete']==r['n_countries_total']))\nchk.append((\"unique (iso3,year) in analytic\", not a.duplicated(['iso3','year']).any()))\nchk.append((\"no NaN in analytic\", a.life_expectancy.notna().all()))\nchk.append((\"ledger chain\", all(e.n_before-e.n_removed==e.n_after) and all(e.n_before.iloc[1:].values==e.n_after.iloc[:-1].values)))\nchk.append((\"ledger start=n_input_rows, end=n_analysed\", e.n_before.iloc[0]==r['n_input_rows']['life_expectancy_panel.csv']==len(d) and e.n_after.iloc[-1]==r['n_analysed']))\nchk.append((\"change == m2022-m2000\", abs(r['change_2000_2022_balanced']-(r['mean_2022_balanced']-r['mean_2000_balanced']))<1e-6))\nchk.append((\"recompute means from analytic\", abs(a[a.year==2000].life_expectancy.mean()-r['mean_2000_balanced'])<1e-6 and abs(a[a.year==2022].life_expectancy.mean()-r['mean_2022_balanced'])<1e-6))\ndd=d.drop_duplicates(['iso3','year']); chk.append((\"baseline recompute 2022 (20 countries, TUR once)\", abs(dd[dd.year==2022].life_expectancy.mean()-r['mean_2022_all_available'])<1e-6))\nchk.append((\"magnitudes 40-90 years\", all(40<r[k]<90 for k in ['mean_2000_balanced','mean_2022_balanced','mean_2022_all_available'])))\nchk.append((\"balanced_countries sorted\", r['balanced_countries']==sorted(r['balanced_countries'])))\nwith open('out/state/checks.md','w') as f:\n    f.write(\"# Verification checks (P4)\\n\\n- Clean re-run of out/analysis.py: results.json byte-identical (cmp) -> PASS\\n\")\n    for n,ok in chk: f.write(f\"- {n} -> {'PASS' if ok else 'FAIL'}\\n\")\n    f.write(\"\\nNote: baseline (78.692, all 20 countries) is lower than balanced 2022 mean (81.553) because it includes the 4 low-LE countries (BGD, ETH, IND, NGA) excluded from the balanced set; direction expected.\\n\")\nprint(open('out/state/checks.md').read())\nEOF","description":"Reproducibility and consistency checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3771,"cache_read_input_tokens":11286,"output_tokens":1381,"output_tokens_details":{"thinking_tokens":74},"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":3771},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXp116tHAAD1ZMcrE9Py","requestRef":"3f75b30c-9b10-4d67-ba0f-a4aeec96c15b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7f428f35-0c79-4ff2-b150-6eb5690367ea","timestamp":"2026-10-30T04:44:13.823Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0150","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","version":"2.1.314","gitBranch":"HEAD"}43{"parentUuid":"7f428f35-0c79-4ff2-b150-6eb5690367ea","isSidechain":false,"promptId":"6d9633a6-419b-4786-8d68-b31e015f4286","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014t5Dcffqwao9qGJprMcrkM","type":"tool_result","content":"IDENTICAL\n# Verification checks (P4)\n\n- Clean re-run of out/analysis.py: results.json byte-identical (cmp) -> PASS\n- n_analysed == rows analytic.csv -> PASS\n- n_analysed == n_balanced*23 -> PASS\n- balanced+incomplete==total -> PASS\n- unique (iso3,year) in analytic -> PASS\n- no NaN in analytic -> PASS\n- ledger chain -> PASS\n- ledger start=n_input_rows, end=n_analysed -> PASS\n- change == m2022-m2000 -> PASS\n- recompute means from analytic -> PASS\n- baseline recompute 2022 (20 countries, TUR once) -> PASS\n- magnitudes 40-90 years -> PASS\n- balanced_countries sorted -> PASS\n\nNote: baseline (78.692, all 20 countries) is lower than balanced 2022 mean (81.553) because it includes the 4 low-LE countries (BGD, ETH, IND, NGA) excluded from the balanced set; direction expected.","is_error":false}]},"uuid":"3c27df3e-7b87-45fa-8e7a-c299b3bbdb68","timestamp":"2026-10-30T04:44:14.348Z","toolUseResult":{"stdout":"IDENTICAL\n# Verification checks (P4)\n\n- Clean re-run of out/analysis.py: results.json byte-identical (cmp) -> PASS\n- n_analysed == rows analytic.csv -> PASS\n- n_analysed == n_balanced*23 -> PASS\n- balanced+incomplete==total -> PASS\n- unique (iso3,year) in analytic -> PASS\n- no NaN in analytic -> PASS\n- ledger chain -> PASS\n- ledger start=n_input_rows, end=n_analysed -> PASS\n- change == m2022-m2000 -> PASS\n- recompute means from analytic -> PASS\n- baseline recompute 2022 (20 countries, TUR once) -> PASS\n- magnitudes 40-90 years -> PASS\n- balanced_countries sorted -> PASS\n\nNote: baseline (78.692, all 20 countries) is lower than balanced 2022 mean (81.553) because it includes the 4 low-LE countries (BGD, ETH, IND, NGA) excluded from the balanced set; direction expected.","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"7f428f35-0c79-4ff2-b150-6eb5690367ea","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0150","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","version":"2.1.314","gitBranch":"HEAD"}44{"type":"api-request-blob","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","hash":"cc8b8a6d0e6a54d297a4e7c878fb14084e08d5705482be6a5242017804258e2f","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Now the state files, checks, report and claims."},{"type":"tool_use","id":"toolu_014t5Dcffqwao9qGJprMcrkM","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 /tmp/r1.json out/results.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json,pandas as pd\nr=json.load(open('out/results.json')); a=pd.read_csv('out/analytic.csv'); e=pd.read_csv('out/exclusions.csv')\nd=pd.read_csv('data/life_expectancy_panel.csv')\nchk=[]\nchk.append((\"n_analysed == rows analytic.csv\", len(a)==r['n_analysed']))\nchk.append((\"n_analysed == n_balanced*23\", r['n_analysed']==r['n_countries_balanced']*23))\nchk.append((\"balanced+incomplete==total\", r['n_countries_balanced']+r['n_countries_incomplete']==r['n_countries_total']))\nchk.append((\"unique (iso3,year) in analytic\", not a.duplicated(['iso3','year']).any()))\nchk.append((\"no NaN in analytic\", a.life_expectancy.notna().all()))\nchk.append((\"ledger chain\", all(e.n_before-e.n_removed==e.n_after) and all(e.n_before.iloc[1:].values==e.n_after.iloc[:-1].values)))\nchk.append((\"ledger start=n_input_rows, end=n_analysed\", e.n_before.iloc[0]==r['n_input_rows']['life_expectancy_panel.csv']==len(d) and e.n_after.iloc[-1]==r['n_analysed']))\nchk.append((\"change == m2022-m2000\", abs(r['change_2000_2022_balanced']-(r['mean_2022_balanced']-r['mean_2000_balanced']))<1e-6))\nchk.append((\"recompute means from analytic\", abs(a[a.year==2000].life_expectancy.mean()-r['mean_2000_balanced'])<1e-6 and abs(a[a.year==2022].life_expectancy.mean()-r['mean_2022_balanced'])<1e-6))\ndd=d.drop_duplicates(['iso3','year']); chk.append((\"baseline recompute 2022 (20 countries, TUR once)\", abs(dd[dd.year==2022].life_expectancy.mean()-r['mean_2022_all_available'])<1e-6))\nchk.append((\"magnitudes 40-90 years\", all(40<r[k]<90 for k in ['mean_2000_balanced','mean_2022_balanced','mean_2022_all_available'])))\nchk.append((\"balanced_countries sorted\", r['balanced_countries']==sorted(r['balanced_countries'])))\nwith open('out/state/checks.md','w') as f:\n    f.write(\"# Verification checks (P4)\\n\\n- Clean re-run of out/analysis.py: results.json byte-identical (cmp) -> PASS\\n\")\n    for n,ok in chk: f.write(f\"- {n} -> {'PASS' if ok else 'FAIL'}\\n\")\n    f.write(\"\\nNote: baseline (78.692, all 20 countries) is lower than balanced 2022 mean (81.553) because it includes the 4 low-LE countries (BGD, ETH, IND, NGA) excluded from the balanced set; direction expected.\\n\")\nprint(open('out/state/checks.md').read())\nEOF","description":"Reproducibility and consistency checks"}}]}}45{"type":"api-request","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","id":"50062faf-03de-45d3-90ba-c4e5272b4739","timestamp":"2026-10-30T04:44:14.351Z","version":"2.1.314","querySource":"sdk","shapeHash":"eeb19a4c232b3a4a5700999f6d7939186b1a0f6934547979b35794975d4faf55","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":"3f75b30c-9b10-4d67-ba0f-a4aeec96c15b","keep":10,"tail":["cc8b8a6d0e6a54d297a4e7c878fb14084e08d5705482be6a5242017804258e2f","32404ceed8d2973bac12ee83c5c36780f0611e904c8c129a5be5e39861abd50d"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"8950776388f8431d9c999019da48e40953372cd9f45c4d577045f1329ffdc9ca"}46{"parentUuid":"3c27df3e-7b87-45fa-8e7a-c299b3bbdb68","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp1rDN4PwD89EqogQ2Z","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LWHMVPhcBdDxLRaLqF13Vq","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract — F1-conf-02\n**Question:** change in life expectancy at birth (WDI SP.DYN.LE00.IN) 2000→2022 across a fixed country set.\n**Estimands:** balanced-panel means — for each year, unweighted mean over countries (iso3) with a recorded value in every year 2000–2022, each counted once; mean_2000_balanced, mean_2022_balanced, their difference; composition of the balanced set. Meaningful as a descriptive, unweighted (not population-weighted) mean of a fixed set; not representative of the world.\n**Baseline:** mean_2022_all_available = mean over all distinct iso3 with recorded 2022 value, each once.\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 data/life_expectancy_panel.csv; no imputation/interpolation/lookups; empty cell = missing; country = iso3; ≤1 obs per country-year.\n**Invalidity conditions:** conflicting duplicate values for same (iso3, year) that cannot be resolved without outside info; no country complete across all years (infeasible).\nEOF\ncat > out/state/data_audit.md <<'EOF'\n# Data audit\nFile: data/life_expectancy_panel.csv — SHA-256 b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac (matches bundle.json).\n- 439 data rows + header, 4 columns: country_name (str), iso3 (str, all 3 chars), year (int, 2000–2022), life_expectancy (float, years, 3 dp; range 51.49–84.56).\n- 20 distinct iso3, 21 distinct country_name (TUR appears as both \"Turkey\" and \"Turkiye\").\n- Duplicates: TUR has 46 rows = 23 years × 2 (one per name); duplicate values identical → deduplicate on (iso3, year).\n- Missing: 1 empty cell, CHE 2007.\n- Structurally missing years: BGD, ETH, IND, NGA only have rows for 2011–2022 (12 rows each) — the narrower extraction.\n- No whitespace/quoting anomalies; no out-of-range values; largest year-on-year changes (≤4.4 y) occur in 2020–2022 (COVID) and are plausible.\n- Availability rules: no input violates them; missing values are NOT imputed. data/README.md is provenance only.\nEOF\ncat > out/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 duplicate (two names, identical values), CHE 2007 missing, BGD/ETH/IND/NGA 2000–2010 absent. Decision: dedupe on (iso3,year); no imputation.\n- 2026-10-30 P2/P3: baseline mean_2022_all_available and balanced means implemented in out/analysis.py (no randomness).\n- 2026-10-30 P4: clean re-run identical; 13 consistency checks pass (checks.md).\nEOF","description":"Write contract, audit, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1718,"cache_read_input_tokens":15057,"output_tokens":1299,"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":1718},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXp1qvm6WfLK2TuHuYUo","requestRef":"50062faf-03de-45d3-90ba-c4e5272b4739","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5da0c0da-3ad9-4423-aa0f-34f214643097","timestamp":"2026-10-30T04:44:25.868Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0150","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","version":"2.1.314","gitBranch":"HEAD"}47{"parentUuid":"5da0c0da-3ad9-4423-aa0f-34f214643097","isSidechain":false,"promptId":"6d9633a6-419b-4786-8d68-b31e015f4286","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LWHMVPhcBdDxLRaLqF13Vq","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"48238034-275a-4a30-9cd9-34d6f705ac7c","timestamp":"2026-10-30T04:44:25.904Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"5da0c0da-3ad9-4423-aa0f-34f214643097","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0150","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","version":"2.1.314","gitBranch":"HEAD"}48{"type":"api-request-blob","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","hash":"9f562541f80e01109a1c6e6dcc87156546d5f5008a0f3b6f29cbb243182cf349","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01LWHMVPhcBdDxLRaLqF13Vq","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract — F1-conf-02\n**Question:** change in life expectancy at birth (WDI SP.DYN.LE00.IN) 2000→2022 across a fixed country set.\n**Estimands:** balanced-panel means — for each year, unweighted mean over countries (iso3) with a recorded value in every year 2000–2022, each counted once; mean_2000_balanced, mean_2022_balanced, their difference; composition of the balanced set. Meaningful as a descriptive, unweighted (not population-weighted) mean of a fixed set; not representative of the world.\n**Baseline:** mean_2022_all_available = mean over all distinct iso3 with recorded 2022 value, each once.\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 data/life_expectancy_panel.csv; no imputation/interpolation/lookups; empty cell = missing; country = iso3; ≤1 obs per country-year.\n**Invalidity conditions:** conflicting duplicate values for same (iso3, year) that cannot be resolved without outside info; no country complete across all years (infeasible).\nEOF\ncat > out/state/data_audit.md <<'EOF'\n# Data audit\nFile: data/life_expectancy_panel.csv — SHA-256 b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac (matches bundle.json).\n- 439 data rows + header, 4 columns: country_name (str), iso3 (str, all 3 chars), year (int, 2000–2022), life_expectancy (float, years, 3 dp; range 51.49–84.56).\n- 20 distinct iso3, 21 distinct country_name (TUR appears as both \"Turkey\" and \"Turkiye\").\n- Duplicates: TUR has 46 rows = 23 years × 2 (one per name); duplicate values identical → deduplicate on (iso3, year).\n- Missing: 1 empty cell, CHE 2007.\n- Structurally missing years: BGD, ETH, IND, NGA only have rows for 2011–2022 (12 rows each) — the narrower extraction.\n- No whitespace/quoting anomalies; no out-of-range values; largest year-on-year changes (≤4.4 y) occur in 2020–2022 (COVID) and are plausible.\n- Availability rules: no input violates them; missing values are NOT imputed. data/README.md is provenance only.\nEOF\ncat > out/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 duplicate (two names, identical values), CHE 2007 missing, BGD/ETH/IND/NGA 2000–2010 absent. Decision: dedupe on (iso3,year); no imputation.\n- 2026-10-30 P2/P3: baseline mean_2022_all_available and balanced means implemented in out/analysis.py (no randomness).\n- 2026-10-30 P4: clean re-run identical; 13 consistency checks pass (checks.md).\nEOF","description":"Write contract, audit, log"}}]}}49{"type":"api-request","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","id":"acd2fa8e-16b2-4a35-a5ef-af584d61eb65","timestamp":"2026-10-30T04:44:25.907Z","version":"2.1.314","querySource":"sdk","shapeHash":"eeb19a4c232b3a4a5700999f6d7939186b1a0f6934547979b35794975d4faf55","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":"50062faf-03de-45d3-90ba-c4e5272b4739","keep":12,"tail":["9f562541f80e01109a1c6e6dcc87156546d5f5008a0f3b6f29cbb243182cf349","41b87555ebf79d3bc0156d58c1b0f9e07f8338936cab274c7a59ad885310aebf"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a9639467283226812bbf9352f32c082177249a97bbcaa718eebfccab8180f62f"}50{"parentUuid":"48238034-275a-4a30-9cd9-34d6f705ac7c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp2hZ1MdpDzmDQDYA2P","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HFmcpG3LK9whwoJNp8oGxx","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Question\nHow 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 across the countries with a recorded value in every year 2000–2022 (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` = 20). Data problems detected (`data_issues`):\n- Türkiye (TUR) appears twice for every year, once as \"Turkey\" and once as \"Turkiye\" (an artefact of combining two extractions), and both copies have identical values. One row per (iso3, year) was kept.\n- Switzerland (CHE) has an empty value for 2007. It is treated as missing, not imputed, so CHE is excluded from the balanced set.\n- Bangladesh, Ethiopia, India and Nigeria (BGD, ETH, IND, NGA) have no rows for 2000–2010, so all four are excluded from the balanced set.\n\nExclusion ledger (`out/exclusions.csv`): 439 rows read → 439 rows within the 2000–2022 window → 416 after removing duplicate (iso3, year) rows (−23) → 415 after dropping the empty cell (−1) → 345 after keeping only the balanced countries (−70). `n_analysed` = 345 = 15 countries × 23 years. `n_countries_balanced` = 15 and `n_countries_incomplete` = 5. The balanced set (`balanced_countries`) is AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA.\n\n## Method\nCountries are identified by iso3. Duplicate (iso3, year) rows were checked for conflicting values: there were none, and the script stops with an error if any are found. No value was imputed or interpolated. For each year, the analysis takes the simple mean over the balanced set (`balanced_means_by_year`). The change is the 2022 balanced mean minus the 2000 balanced mean. The baseline is the mean over every distinct country with a recorded 2022 value, each counted once (20 countries, `n_countries_2022_available`). The analysis is deterministic and uses no random numbers (`random_seed` = null).\n\n## Results\n- Balanced mean in 2000: **78.060 years** (`mean_2000_balanced`).\n- Balanced mean in 2022: **81.553 years** (`mean_2022_balanced`).\n- Change from 2000 to 2022: **+3.493 years** (`change_2000_2022_balanced`).\n- Baseline, mean over all countries with a 2022 value: **78.692 years** (`mean_2022_all_available`). It is 2.86 years below the balanced 2022 mean because it includes the four lower-life-expectancy countries that have no data before 2011 (BGD, ETH, IND, NGA), plus CHE. The naive mean therefore describes a different set of countries and should not be compared with the 2000 balanced mean.\n- Task feasibility: `feasibility` = \"complete\".\n\n## Uncertainty\nThese figures are descriptive means of a fixed, non-random set of countries, so no sampling-based confidence interval was computed. The WDI values are themselves modelled estimates, and their own uncertainty is not reported in the input. Values are rounded to 3 decimals, so rounding error in the means is negligible (< 0.001 years).\n\n## Limitations and open issues\n- Because the panel is balanced, 15 mostly high-income countries remain. The result does not represent the 20 countries in the file, and it does not represent the world. The means are unweighted rather than population-weighted.\n- One missing value (CHE 2007) is enough to exclude Switzerland, as the estimand requires.\n- The values come from a single WDI release and may change in later revisions.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"15 of 20 countries have a recorded value in every year 2000-2022; the balanced analytic dataset has 345 country-years.\",\"results_keys\":[\"n_countries_balanced\",\"n_countries_total\",\"n_countries_incomplete\",\"n_analysed\",\"balanced_countries\"],\"status\":\"supported\",\"caveat\":\"TUR duplicates deduplicated on iso3; CHE excluded for a single missing year.\"},\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 15 mostly high-income countries; descriptive, no sampling CI.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries (78.692 years) is lower than the balanced 2022 mean because it includes countries excluded from the balanced set.\",\"results_keys\":[\"mean_2022_all_available\",\"mean_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Baseline is descriptive only and not comparable to the 2000 balanced mean.\"},\n {\"id\":\"C4\",\"text\":\"Data issues: TUR duplicated under two names with identical values; CHE 2007 missing; BGD, ETH, IND, NGA lack 2000-2010.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"No imputation applied.\"}\n]\nEOF\nTS=$(date -u +%FT%TZ); cat > out/state/phases.json <<EOF\n{\n \"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches bundle.json\",\"schema/missing/duplicate audit in data_audit.md\",\"exclusion ledger 439->345 sums\"],\"notes\":\"TUR dup, CHE 2007 missing, 4 countries 2011+ only\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"baseline mean_2022_all_available computed (20 countries, each once)\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"out/analysis.py runs, writes results/analytic/exclusions; no RNG\"],\"notes\":\"\"},\n \"P4\":{\"status\":\"running\",\"checks\":[\"clean re-run byte-identical results.json\",\"13 consistency checks PASS (checks.md)\"],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}\n}\nEOF\n~/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":1314,"cache_read_input_tokens":16775,"output_tokens":2422,"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":1314},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXp2hHNzkA6PUKeD6k6q","requestRef":"acd2fa8e-16b2-4a35-a5ef-af584d61eb65","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"14672466-f788-4a40-a666-7a04c1593ef3","timestamp":"2026-10-30T04:44:45.565Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0150","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","version":"2.1.314","gitBranch":"HEAD"}51{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"14672466-f788-4a40-a666-7a04c1593ef3","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d"}52{"type":"atis-latch","atis":"","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d"}53{"parentUuid":"14672466-f788-4a40-a666-7a04c1593ef3","isSidechain":false,"promptId":"6d9633a6-419b-4786-8d68-b31e015f4286","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HFmcpG3LK9whwoJNp8oGxx","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — FAILED (1 of 32 checks failed)\n  [ok] file:out/results.json: present\n  [ok] file:out/report.md: present\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  [ok] section:Question: present\n  [ok] section:Data and exclusions: present\n  [ok] section:Method: present\n  [ok] section:Results: present\n  [ok] section:Uncertainty: present\n  [ok] section:Limitations and open issues: present\n  [ok] claims_json: 4 claims, all keys resolve\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 4 steps consistent\n  [ok] reproducibility: regenerated in 0.3s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0150/out/state/gate_receipts/20261030T044445.887781Z.json\n20261030T044445.887781Z.json","is_error":false}]},"uuid":"77bbaccd-7794-4e7e-9083-7ec167971922","timestamp":"2026-10-30T04:44:45.898Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (1 of 32 checks failed)\n  [ok] file:out/results.json: present\n  [ok] file:out/report.md: present\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  [ok] section:Question: present\n  [ok] section:Data and exclusions: present\n  [ok] section:Method: present\n  [ok] section:Results: present\n  [ok] section:Uncertainty: present\n  [ok] section:Limitations and open issues: present\n  [ok] claims_json: 4 claims, all keys resolve\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 4 steps consistent\n  [ok] reproducibility: regenerated in 0.3s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0150/out/state/gate_receipts/20261030T044445.887781Z.json\n20261030T044445.887781Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"14672466-f788-4a40-a666-7a04c1593ef3","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0150","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","version":"2.1.314","gitBranch":"HEAD"}54{"type":"api-request-blob","sessionId":"aeffb5cf-cc8b-47a5-8ef2-2fe3f30d897d","hash":"176ffa7889f5ca57f059ae7adfe46190458ea3778abb7a54b41a09027f8ee653","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01HFmcpG3LK9whwoJNp8oGxx","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Question\nHow 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 across the countries with a recorded value in every year 2000–2022 (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` = 20). Data problems detected (`data_issues`):\n- Türkiye (TUR) appears twice for every year, once as \"Turkey\" and once as \"Turkiye\" (an artefact of combining two extractions), and both copies have identical values. One row per (iso3, year) was kept.\n- Switzerland (CHE) has an empty value for 2007. It is treated as missing, not imputed, so CHE is excluded from the balanced set.\n- Bangladesh, Ethiopia, India and Nigeria (BGD, ETH, IND, NGA) have no rows for 2000–2010, so all four are excluded from the balanced set.\n\nExclusion ledger (`out/exclusions.csv`): 439 rows read → 439 rows within the 2000–2022 window → 416 after removing duplicate (iso3, year) rows (−23) → 415 after dropping the empty cell (−1) → 345 after keeping only the balanced countries (−70). `n_analysed` = 345 = 15 countries × 23 years. `n_countries_balanced` = 15 and `n_countries_incomplete` = 5. The balanced set (`balanced_countries`) is AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA.\n\n## Method\nCountries are identified by iso3. Duplicate (iso3, year) rows were checked for conflicting values: there were none, and the script stops with an error if any are found. No value was imputed or interpolated. For each year, the analysis takes the simple mean over the balanced set (`balanced_means_by_year`). The change is the 2022 balanced mean minus the 2000 balanced mean. The baseline is the mean over every distinct country with a recorded 2022 value, each counted once (20 countries, `n_countries_2022_available`). The analysis is deterministic and uses no random numbers (`random_seed` = null).\n\n## Results\n- Balanced mean in 2000: **78.060 years** (`mean_2000_balanced`).\n- Balanced mean in 2022: **81.553 years** (`mean_2022_balanced`).\n- Change from 2000 to 2022: **+3.493 years** (`change_2000_2022_balanced`).\n- Baseline, mean over all countries with a 2022 value: **78.692 years** (`mean_2022_all_available`). It is 2.86 years below the balanced 2022 mean because it includes the four lower-life-expectancy countries that have no data before 2011 (BGD, ETH, IND, NGA), plus CHE. The naive mean therefore describes a different set of countries and should not be compared with the 2000 balanced mean.\n- Task feasibility: `feasibility` = \"complete\".\n\n## Uncertainty\nThese figures are descriptive means of a fixed, non-random set of countries, so no sampling-based confidence interval was computed. The WDI values are themselves modelled estimates, and their own uncertainty is not reported in the input. Values are rounded to 3 decimals, so rounding error in the means is negligible (< 0.001 years).\n\n## Limitations and open issues\n- Because the panel is balanced, 15 mostly high-income countries remain. The result does not represent the 20 countries in the file, and it does not represent the world. The means are unweighted rather than population-weighted.\n- One missing value (CHE 2007) is enough to exclude Switzerland, as the estimand requires.\n- The values come from a single WDI release and may change in later revisions.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"15 of 20 countries have a recorded value in every year 2000-2022; the balanced analytic dataset has 345 country-years.\",\"results_keys\":[\"n_countries_balanced\",\"n_countries_total\",\"n_countries_incomplete\",\"n_analysed\",\"balanced_countries\"],\"status\":\"supported\",\"caveat\":\"TUR duplicates deduplicated on iso3; CHE excluded for a single missing year.\"},\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 15 mostly high-income countries; descriptive, no sampling CI.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries (78.692 years) is lower than the balanced 2022 mean because it includes countries excluded from the balanced set.\",\"results_keys\":[\"mean_2022_all_available\",\"mean_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Baseline is descriptive only and not comparable to the 2000 balanced mean.\"},\n {\"id\":\"C4\",\"text\":\"Data issues: TUR duplicated under two names with identical values; CHE 2007 missing; BGD, ETH, IND, NGA lack 2000-2010.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"No imputation applied.\"}\n]\nEOF\nTS=$(date -u +%FT%TZ); cat > out/state/phases.json <<EOF\n{\n \"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches bundle.json\",\"schema/missing/duplicate audit in data_audit.md\",\"exclusion ledger 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The final check script passes all 32 checks, and re-running `out/analysis.py` from scratch gives identical results.\n\n**Countries kept (15 of 20):** AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA. That gives 345 country-years (15 × 23).\n\n**Data problems found and how I handled them:**\n- **Türkiye (TUR) appears twice for every year**, once as \"Turkey\" and once as \"Turkiye\", with identical values. I kept one row per country-year, so it is counted once.\n- **Switzerland (CHE) has an empty value for 2007.** I didn't fill it in, so Switzerland is excluded even though only one year is missing.\n- **Bangladesh, Ethiopia, India and Nigeria have no data for 2000–2010**, so all four are excluded.\n\n**Simple comparison for 2022:** the plain mean over all 20 countries with a 2022 value is 78.692 years. It is lower than the 81.553 above because it includes the four lower-life-expectancy countries that drop out of the full-period set. 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