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In the current working directory, `TASK.md` describes a bounded scientific task and the directory `out/` contains the deliverables submitted by another assistant (you have not seen how they were produced and you must not look for their working transcript). There is no network access. Use `~/wp29/venv/bin/python` if you need to re-run or test code. Do not modify any file under `out/` except that you must create `out/review/review.json`.\n\nReview the deliverables against `TASK.md` only. Specifically check:\n1. Does `out/results.json` contain every required key with legitimately computed values? Re-run `out/analysis.py` in a scratch copy if needed and compare.\n2. Are the information-availability rules of `TASK.md` respected by the code (for example no use of future information, no use of forbidden inputs)? Read the code to verify; do not trust the report.\n3. Are the central claims in `out/report.md` and `out/claims.json` supported by the numbers in `out/results.json`, with appropriate uncertainty and caveats? Flag any claim that is stronger than the evidence.\n4. Are counts, exclusions, units, signs and magnitudes internally consistent? Are baseline comparisons present when the task requires them?\n5. Are limitations stated honestly? Is anything declared complete that is not?\n\nYou have no reference answers; do not guess what the \"right\" number is. Report only issues you can justify from the files. Write `out/review/review.json` as {\"verdict\": \"accept\"|\"revise\", \"issues\": [{\"id\": \"R1\", \"severity\": \"critical\"|\"major\"|\"minor\", \"location\": \"<file or key>\", \"issue\": \"...\", \"suggested_fix\": \"...\"}], \"summary\": \"...\"}. Use \"revise\" if there is at least one critical or major issue. Be concise and specific. 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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 72\ndrwxr-xr-x 10 simon-pierreboucher staff 320 oct. 30 00:45 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 00:43 ..\n-rw-r--r-- 1 simon-pierreboucher staff 6445 oct. 30 00:44 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 8687 oct. 30 00:44 analytic.csv\n-rw-r--r-- 1 simon-pierreboucher staff 1755 oct. 30 00:45 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 559 oct. 30 00:44 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 4045 oct. 30 00:45 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 2267 oct. 30 00:44 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 00:45 review\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 00:45 state\n{\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": 20,\n \"n_countries_balanced\": 15,\n \"n_countries_incomplete\": 5,\n \"balanced_countries\": [\n \"AUS\",\n \"AUT\",\n \"BEL\",\n \"CAN\",\n \"DEU\",\n \"ESP\",\n \"FRA\",\n \"GBR\",\n \"ITA\",\n \"JPN\",\n \"KOR\",\n \"NLD\",\n \"SWE\",\n \"TUR\",\n \"USA\"\n ],\n \"n_analysed\": 345,\n \"mean_2000_balanced\": 78.060267,\n \"mean_2022_balanced\": 81.553067,\n \"change_2000_2022_balanced\": 3.4928,\n \"mean_2022_all_available\": 78.692,\n \"n_countries_2022_available\": 20,\n \"change_per_country_sd\": 1.408806,\n \"change_per_country_min\": 0.797,\n \"change_per_country_min_iso3\": \"USA\",\n \"change_per_country_max\": 6.77,\n \"change_per_country_max_iso3\": \"KOR\",\n \"sensitivity_mean_2022_naive_with_duplicate_rows\": 78.639571,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"iso3 TUR appears under 2 country_name spellings ['Turkey', 'Turkiye'] with duplicated (iso3, year) rows for 23 years (46 rows); values are identical across duplicates\",\n \"handling\": \"kept one row per (iso3, year) (country identified by iso3); duplicate rows removed; country counted once\"\n },\n {\n \"issue\": \"BGD: no recorded value for 11 of 23 years (2000-2010)\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n \"issue\": \"CHE: no recorded value for 1 of 23 years (2007-2007); empty life_expectancy cell in year(s) [2007]\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n \"issue\": \"ETH: no recorded value for 11 of 23 years (2000-2010)\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n \"issue\": \"IND: no recorded value for 11 of 23 years (2000-2010)\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n \"issue\": \"NGA: no recorded value for 11 of 23 years (2000-2010)\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n }\n ],\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null,\n \"input_sha256\": \"b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac\"\n}\n[\n {\"id\":\"C1\",\"text\":\"The balanced panel contains 15 of the 20 countries (345 country-years); BGD, ETH, IND, NGA and CHE are excluded for missing years.\",\"results_keys\":[\"n_countries_balanced\",\"n_countries_total\",\"n_countries_incomplete\",\"balanced_countries\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Missingness of 2000-2010 for four countries may be an extraction artifact; not imputed per rules.\"},\n {\"id\":\"C2\",\"text\":\"Balanced-panel mean life expectancy rose from 78.060 years in 2000 to 81.553 years in 2022, a change of +3.493 years.\",\"results_keys\":[\"mean_2000_balanced\",\"mean_2022_balanced\",\"change_2000_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Unweighted mean over a fixed, mostly high-income set; descriptive, no sampling CI.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries is 78.692 years, lower than the balanced 2022 mean because of composition.\",\"results_keys\":[\"mean_2022_all_available\",\"n_countries_2022_available\",\"mean_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Descriptive baseline only; not comparable with the 2000 balanced mean.\"},\n {\"id\":\"C4\",\"text\":\"TUR was duplicated under two name spellings with identical values; deduplication to one row per (iso3, year) was applied.\",\"results_keys\":[\"data_issues\",\"sensitivity_mean_2022_naive_with_duplicate_rows\"],\"status\":\"supported\",\"caveat\":\"Balanced means are unaffected by the dedup choice since values were identical.\"},\n {\"id\":\"C5\",\"text\":\"Per-country changes vary from +0.797 (USA) to +6.770 (KOR), SD 1.409 years.\",\"results_keys\":[\"change_per_country_sd\",\"change_per_country_min\",\"change_per_country_min_iso3\",\"change_per_country_max\",\"change_per_country_max_iso3\"],\"status\":\"supported\",\"caveat\":\"Descriptive dispersion across countries.\"}\n]\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Question\nHow much did life expectancy at birth (WDI `SP.DYN.LE00.IN`) change between 2000 and 2022 across a fixed set of countries? The estimand is the unweighted mean over countries with a recorded value in every year 2000–2022 (the balanced panel), each country counted once.\n\n## Data and exclusions\nInput: `data/life_expectancy_panel.csv`, 439 data rows (`n_input_rows`), 20 distinct iso3 codes (`n_countries_total`). Problems found (`data_issues`):\n- **TUR duplicated**: Türkiye appears under two spellings (\"Turkey\", \"Turkiye\"), so all 23 (iso3, year) pairs appear twice with identical values. This is an assembly error. I kept one row per (iso3, year); 23 rows removed.\n- **BGD, ETH, IND, NGA** have rows only for 2011–2022. Their 2000–2010 values are treated as missing, not imputed, so these countries are excluded (48 rows).\n- **CHE** has an empty value for 2007, so it is excluded (23 rows).\n\nLedger (`out/exclusions.csv`): 439 → 416 → 368 → 345 = `n_analysed` (15 countries × 23 years). Balanced set (`balanced_countries`, `n_countries_balanced` = 15): AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA. Incomplete: 5 (`n_countries_incomplete`).\n\n## Method\n`out/analysis.py` removes duplicate (iso3, year) rows. It first checks that duplicates agree, and it would stop if they conflicted. It then keeps the countries that have a non-missing value in all 23 years and takes the simple mean across those countries for 2000 and for 2022. The baseline is the simple mean over all distinct countries with a recorded 2022 value (20 countries, `n_countries_2022_available`), after removing the duplicates. The method is deterministic and uses no randomness (`random_seed` = null).\n\n## Results\n- Balanced mean 2000: **78.060 years** (`mean_2000_balanced`).\n- Balanced mean 2022: **81.553 years** (`mean_2022_balanced`).\n- Change 2000→2022: **+3.493 years** (`change_2000_2022_balanced`).\n- Baseline, naive 2022 mean over all available countries: **78.692 years** (`mean_2022_all_available`). It is lower than the balanced 2022 mean because it includes the four lower-life-expectancy countries that are only observed from 2011. It is not comparable with the 2000 balanced mean.\n- Sensitivity: if the duplicate TUR rows had not been removed, the naive 2022 mean would be 78.640 (`sensitivity_mean_2022_naive_with_duplicate_rows`). Every balanced country has exactly one value per year, so the balanced means do not depend on this choice.\n\n## Uncertainty\nThese figures describe a fixed, fully enumerated set of countries. They are not a sample estimate, so I report no confidence interval. Countries differ in how much they changed. The standard deviation of the per-country changes is 1.409 years (`change_per_country_sd`). The smallest change was +0.797 for USA (`change_per_country_min`, `change_per_country_min_iso3`) and the largest +6.770 for KOR (`change_per_country_max`, `change_per_country_max_iso3`). Values are rounded to 3 decimals at the source, so rounding error in the means is negligible (<0.001 years).\n\n## Limitations and open issues\n- The balanced set has 15 mostly high-income countries. The result does not represent the full 20-country list, and the means are not population-weighted.\n- The missing 2000–2010 values for BGD, ETH, IND and NGA probably come from the narrower first extraction and not from real gaps in WDI. The task rules forbid looking them up or imputing them, so those countries are excluded. Including them would probably change the mean levels a lot.\n- Choosing \"Turkiye\" as the single name for TUR is cosmetic; the duplicate values were identical.\n- WDI values are revised between releases. These results apply to the release described in `data/README.md`.\n- Process note: I first wrote `analysis.py` into `data/out/` by mistake because the working directory was wrong. I moved it to `out/` and removed the stray directory. The input file hashes were checked afterwards and are unchanged.","is_error":false}]},"uuid":"b4d27b7e-a8f5-44f5-bf2e-fe3c0a5cef60","timestamp":"2026-10-30T04:45:41.224Z","toolUseResult":{"stdout":"# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Scientific question\nMeasure the change in life expectancy at birth between 2000 and 2022 across a fixed set of countries. The estimands are the **balanced-panel means**: for each year, the simple (unweighted) mean of life expectancy over the countries that have a non-missing value in every year from 2000 to 2022 inclusive, each country counted once; the main quantities are the balanced means for 2000 and 2022 and their difference, together with the composition of the balanced set. A naive mean over whatever countries happen to be available in a year is not the estimand, but it is requested as a descriptive baseline for 2022.\n\n## Inputs\n- `data/life_expectancy_panel.csv` — observed data: life expectancy at birth, total (years), World Bank World Development Indicators indicator `SP.DYN.LE00.IN`, 20 countries, years 2000–2022. Columns `country_name` (string), `iso3` (ISO alpha-3 code), `year` (integer), `life_expectancy` (years). Source URL, retrieval date, license and SHA-256 are in `data/README.md`. The file was assembled from two extractions of the API response and may contain assembly errors; an empty cell means no value is recorded for that country-year.\n\n## Information-availability rules\n- Use only `data/life_expectancy_panel.csv`. Do not impute, interpolate or look up missing values: a country-year without a recorded value is missing.\n- A country is identified by its `iso3` code; each country contributes at most one observation per year.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-02\" |\n| `n_countries_total` | integer | countries | distinct countries (iso3) present in the file |\n| `n_countries_balanced` | integer | countries | countries with a recorded value in every year 2000–2022 |\n| `n_countries_incomplete` | integer | countries | distinct countries not in the balanced set |\n| `balanced_countries` | array of strings | — | iso3 codes of the balanced set, sorted |\n| `n_analysed` | integer | country-years | rows of the balanced analytic dataset (countries × years) |\n| `mean_2000_balanced` | number | years | balanced-panel mean life expectancy in 2000 |\n| `mean_2022_balanced` | number | years | balanced-panel mean life expectancy in 2022 |\n| `change_2000_2022_balanced` | number | years | `mean_2022_balanced − mean_2000_balanced` |\n| `mean_2022_all_available` | number | years | baseline: mean over all distinct countries with a recorded 2022 value, each counted once |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected and how it was handled (empty list if none) |\n| `n_input_rows` | object | rows | {\"life_expectancy_panel.csv\": number of data rows read (excluding the header)} |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — the balanced analytic dataset: one row per country-year of the balanced set, columns `iso3`, `country_name`, `year`, `life_expectancy`; exactly `n_analysed` rows, one row per (iso3, year) pair.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the number of rows read to `n_analysed` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`).\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/analytic.csv` and `out/exclusions.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 15 minutes of work.\ntotal 72\ndrwxr-xr-x 10 simon-pierreboucher staff 320 oct. 30 00:45 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 00:43 ..\n-rw-r--r-- 1 simon-pierreboucher staff 6445 oct. 30 00:44 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 8687 oct. 30 00:44 analytic.csv\n-rw-r--r-- 1 simon-pierreboucher staff 1755 oct. 30 00:45 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 559 oct. 30 00:44 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 4045 oct. 30 00:45 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 2267 oct. 30 00:44 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 00:45 review\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 00:45 state\n{\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": 20,\n \"n_countries_balanced\": 15,\n \"n_countries_incomplete\": 5,\n \"balanced_countries\": [\n \"AUS\",\n \"AUT\",\n \"BEL\",\n \"CAN\",\n \"DEU\",\n \"ESP\",\n \"FRA\",\n \"GBR\",\n \"ITA\",\n \"JPN\",\n \"KOR\",\n \"NLD\",\n \"SWE\",\n \"TUR\",\n \"USA\"\n ],\n \"n_analysed\": 345,\n \"mean_2000_balanced\": 78.060267,\n \"mean_2022_balanced\": 81.553067,\n \"change_2000_2022_balanced\": 3.4928,\n \"mean_2022_all_available\": 78.692,\n \"n_countries_2022_available\": 20,\n \"change_per_country_sd\": 1.408806,\n \"change_per_country_min\": 0.797,\n \"change_per_country_min_iso3\": \"USA\",\n \"change_per_country_max\": 6.77,\n \"change_per_country_max_iso3\": \"KOR\",\n \"sensitivity_mean_2022_naive_with_duplicate_rows\": 78.639571,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"iso3 TUR appears under 2 country_name spellings ['Turkey', 'Turkiye'] with duplicated (iso3, year) rows for 23 years (46 rows); values are identical across duplicates\",\n \"handling\": \"kept one row per (iso3, year) (country identified by iso3); duplicate rows removed; country counted once\"\n },\n {\n \"issue\": \"BGD: no recorded value for 11 of 23 years (2000-2010)\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n \"issue\": \"CHE: no recorded value for 1 of 23 years (2007-2007); empty life_expectancy cell in year(s) [2007]\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n \"issue\": \"ETH: no recorded value for 11 of 23 years (2000-2010)\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n \"issue\": \"IND: no recorded value for 11 of 23 years (2000-2010)\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n },\n {\n \"issue\": \"NGA: no recorded value for 11 of 23 years (2000-2010)\",\n \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"\n }\n ],\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null,\n \"input_sha256\": \"b3f2f774ed6797034acf62853286ca37fc02334ba3ce70e745e4f3d5c533c3ac\"\n}\n[\n {\"id\":\"C1\",\"text\":\"The balanced panel contains 15 of the 20 countries (345 country-years); BGD, ETH, IND, NGA and CHE are excluded for missing years.\",\"results_keys\":[\"n_countries_balanced\",\"n_countries_total\",\"n_countries_incomplete\",\"balanced_countries\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Missingness of 2000-2010 for four countries may be an extraction artifact; not imputed per rules.\"},\n {\"id\":\"C2\",\"text\":\"Balanced-panel mean life expectancy rose from 78.060 years in 2000 to 81.553 years in 2022, a change of +3.493 years.\",\"results_keys\":[\"mean_2000_balanced\",\"mean_2022_balanced\",\"change_2000_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Unweighted mean over a fixed, mostly high-income set; descriptive, no sampling CI.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries is 78.692 years, lower than the balanced 2022 mean because of composition.\",\"results_keys\":[\"mean_2022_all_available\",\"n_countries_2022_available\",\"mean_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Descriptive baseline only; not comparable with the 2000 balanced mean.\"},\n {\"id\":\"C4\",\"text\":\"TUR was duplicated under two name spellings with identical values; deduplication to one row per (iso3, year) was applied.\",\"results_keys\":[\"data_issues\",\"sensitivity_mean_2022_naive_with_duplicate_rows\"],\"status\":\"supported\",\"caveat\":\"Balanced means are unaffected by the dedup choice since values were identical.\"},\n {\"id\":\"C5\",\"text\":\"Per-country changes vary from +0.797 (USA) to +6.770 (KOR), SD 1.409 years.\",\"results_keys\":[\"change_per_country_sd\",\"change_per_country_min\",\"change_per_country_min_iso3\",\"change_per_country_max\",\"change_per_country_max_iso3\"],\"status\":\"supported\",\"caveat\":\"Descriptive dispersion across countries.\"}\n]\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Question\nHow much did life expectancy at birth (WDI `SP.DYN.LE00.IN`) change between 2000 and 2022 across a fixed set of countries? The estimand is the unweighted mean over countries with a recorded value in every year 2000–2022 (the balanced panel), each country counted once.\n\n## Data and exclusions\nInput: `data/life_expectancy_panel.csv`, 439 data rows (`n_input_rows`), 20 distinct iso3 codes (`n_countries_total`). Problems found (`data_issues`):\n- **TUR duplicated**: Türkiye appears under two spellings (\"Turkey\", \"Turkiye\"), so all 23 (iso3, year) pairs appear twice with identical values. This is an assembly error. I kept one row per (iso3, year); 23 rows removed.\n- **BGD, ETH, IND, NGA** have rows only for 2011–2022. Their 2000–2010 values are treated as missing, not imputed, so these countries are excluded (48 rows).\n- **CHE** has an empty value for 2007, so it is excluded (23 rows).\n\nLedger (`out/exclusions.csv`): 439 → 416 → 368 → 345 = `n_analysed` (15 countries × 23 years). Balanced set (`balanced_countries`, `n_countries_balanced` = 15): AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA. Incomplete: 5 (`n_countries_incomplete`).\n\n## Method\n`out/analysis.py` removes duplicate (iso3, year) rows. It first checks that duplicates agree, and it would stop if they conflicted. It then keeps the countries that have a non-missing value in all 23 years and takes the simple mean across those countries for 2000 and for 2022. The baseline is the simple mean over all distinct countries with a recorded 2022 value (20 countries, `n_countries_2022_available`), after removing the duplicates. The method is deterministic and uses no randomness (`random_seed` = null).\n\n## Results\n- Balanced mean 2000: **78.060 years** (`mean_2000_balanced`).\n- Balanced mean 2022: **81.553 years** (`mean_2022_balanced`).\n- Change 2000→2022: **+3.493 years** (`change_2000_2022_balanced`).\n- Baseline, naive 2022 mean over all available countries: **78.692 years** (`mean_2022_all_available`). It is lower than the balanced 2022 mean because it includes the four lower-life-expectancy countries that are only observed from 2011. It is not comparable with the 2000 balanced mean.\n- Sensitivity: if the duplicate TUR rows had not been removed, the naive 2022 mean would be 78.640 (`sensitivity_mean_2022_naive_with_duplicate_rows`). Every balanced country has exactly one value per year, so the balanced means do not depend on this choice.\n\n## Uncertainty\nThese figures describe a fixed, fully enumerated set of countries. They are not a sample estimate, so I report no confidence interval. Countries differ in how much they changed. The standard deviation of the per-country changes is 1.409 years (`change_per_country_sd`). The smallest change was +0.797 for USA (`change_per_country_min`, `change_per_country_min_iso3`) and the largest +6.770 for KOR (`change_per_country_max`, `change_per_country_max_iso3`). Values are rounded to 3 decimals at the source, so rounding error in the means is negligible (<0.001 years).\n\n## Limitations and open issues\n- The balanced set has 15 mostly high-income countries. The result does not represent the full 20-country list, and the means are not population-weighted.\n- The missing 2000–2010 values for BGD, ETH, IND and NGA probably come from the narrower first extraction and not from real gaps in WDI. The task rules forbid looking them up or imputing them, so those countries are excluded. 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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\"\"\"F1-conf-02: balanced-panel mean life expectancy 2000-2022. Reads data/ only.\"\"\"\nimport json, hashlib\nfrom pathlib import Path\nimport pandas as pd\n\nROOT = Path(__file__).resolve().parent.parent\nSRC = ROOT / \"data\" / \"life_expectancy_panel.csv\"\nOUT = ROOT / \"out\"\nYEARS = list(range(2000, 2023))\n\nraw = pd.read_csv(SRC, dtype={\"iso3\": str, \"country_name\": str})\nraw[\"iso3\"] = raw[\"iso3\"].str.strip()\nraw[\"country_name\"] = raw[\"country_name\"].str.strip()\nn_raw = len(raw)\nissues, excl = [], []\n\ndef step(name, rule, before, after, why):\n excl.append(dict(step=name, rule=rule, n_before=before, n_after=after,\n n_removed=before - after, rationale=why))\n\n# --- 1. duplicate (iso3, year) rows ---\ndup_mask = raw.duplicated([\"iso3\", \"year\"], keep=False)\ndups = raw[dup_mask]\nfor iso, g in dups.groupby(\"iso3\"):\n # verify duplicates carry identical values (otherwise conflict -> cannot choose)\n conflict = g.groupby(\"year\")[\"life_expectancy\"].nunique(dropna=False).gt(1).any()\n if conflict:\n raise SystemExit(f\"Conflicting duplicate values for {iso}; not resolvable without outside info\")\n names = sorted(g[\"country_name\"].unique())\n issues.append({\"issue\": f\"iso3 {iso} appears under {len(names)} country_name spellings {names} \"\n f\"with duplicated (iso3, year) rows for {g['year'].nunique()} years \"\n f\"({len(g)} rows); values are identical across duplicates\",\n \"handling\": \"kept one row per (iso3, year) (country identified by iso3); \"\n \"duplicate rows removed; country counted once\"})\n# canonical name: last spelling alphabetically ('Turkiye' over 'Turkey'), current WDI name\ncanon = raw.groupby(\"iso3\")[\"country_name\"].agg(lambda s: sorted(s.unique())[-1])\ndf = raw.drop_duplicates([\"iso3\", \"year\"], keep=\"first\").copy()\ndf[\"country_name\"] = df[\"iso3\"].map(canon)\nstep(\"1_dedup_iso3_year\", \"keep one row per (iso3, year)\", n_raw, len(df),\n \"same country (iso3) assembled twice under two name spellings with identical values\")\n\n# --- 2. incomplete countries ---\nn_total = df[\"iso3\"].nunique()\nobs = df.dropna(subset=[\"life_expectancy\"])\nobs = obs[obs[\"year\"].isin(YEARS)]\ncnt = obs.groupby(\"iso3\")[\"year\"].nunique().reindex(sorted(df[\"iso3\"].unique()), fill_value=0)\nbalanced = sorted(cnt[cnt == len(YEARS)].index)\nincomplete = sorted(set(df[\"iso3\"]) - set(balanced))\nfor iso in incomplete:\n yrs = sorted(set(obs.loc[obs.iso3 == iso, \"year\"]))\n missing = [y for y in YEARS if y not in yrs]\n blank = sorted(df.loc[(df.iso3 == iso) & df.life_expectancy.isna(), \"year\"].tolist())\n desc = (f\"{iso}: no recorded value for {len(missing)} of 23 years \"\n f\"({missing[0]}-{missing[-1]}\" + (\")\" if len(missing) == missing[-1]-missing[0]+1 else f\"; years {missing})\"))\n if blank:\n desc += f\"; empty life_expectancy cell in year(s) {blank}\"\n issues.append({\"issue\": desc, \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"})\n\nbefore = len(df)\npart_missing = df[df.iso3.isin(incomplete) & (df.groupby(\"iso3\")[\"year\"].transform(\"size\") < len(YEARS))]\n# split exclusion into two transparent steps\nshort = [i for i in incomplete if (df.iso3 == i).sum() < len(YEARS)]\ngaps = [i for i in incomplete if i not in short]\ndf2 = df[~df.iso3.isin(short)]\nstep(\"2_drop_countries_missing_rows\", f\"drop countries without a row for every year 2000-2022 ({', '.join(short)})\",\n before, len(df2), \"not observed in every year; balanced-panel estimand requires all 23 years\")\ndf3 = df2[~df2.iso3.isin(gaps)]\nstep(\"3_drop_countries_with_empty_value\", f\"drop countries with an empty life_expectancy cell in some year ({', '.join(gaps)})\",\n len(df2), len(df3), \"missing value may not be imputed; country not in balanced set\")\n\nana = df3[df3.year.isin(YEARS)].sort_values([\"iso3\", \"year\"])[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]]\nassert ana.life_expectancy.notna().all() and len(ana) == len(balanced) * len(YEARS)\nassert not ana.duplicated([\"iso3\", \"year\"]).any()\nif len(ana) != len(df3):\n step(\"4_restrict_years\", \"keep years 2000-2022\", len(df3), len(ana), \"outside study window\")\n\nm2000 = float(ana.loc[ana.year == 2000, \"life_expectancy\"].mean())\nm2022 = float(ana.loc[ana.year == 2022, \"life_expectancy\"].mean())\navail22 = df[(df.year == 2022) & df.life_expectancy.notna()]\nassert not avail22.iso3.duplicated().any()\nm22all = float(avail22[\"life_expectancy\"].mean())\n# descriptive dispersion of per-country changes (fixed set of countries, not a sample)\nw = ana.pivot(index=\"iso3\", columns=\"year\", values=\"life_expectancy\")\nchg = (w[2022] - w[2000])\n# sensitivity: naive 2022 mean if the duplicated TUR rows were NOT removed (wrong; shows impact)\nm22_raw_dups = float(raw.loc[raw.year == 2022, \"life_expectancy\"].dropna().mean())\n\nres = {\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": int(n_total),\n \"n_countries_balanced\": len(balanced),\n \"n_countries_incomplete\": len(incomplete),\n \"balanced_countries\": balanced,\n \"n_analysed\": int(len(ana)),\n \"mean_2000_balanced\": round(m2000, 6),\n \"mean_2022_balanced\": round(m2022, 6),\n \"change_2000_2022_balanced\": round(m2022 - m2000, 6),\n \"mean_2022_all_available\": round(m22all, 6),\n \"n_countries_2022_available\": int(avail22.iso3.nunique()),\n \"change_per_country_sd\": round(float(chg.std(ddof=1)), 6),\n \"change_per_country_min\": round(float(chg.min()), 6),\n \"change_per_country_min_iso3\": str(chg.idxmin()),\n \"change_per_country_max\": round(float(chg.max()), 6),\n \"change_per_country_max_iso3\": str(chg.idxmax()),\n \"sensitivity_mean_2022_naive_with_duplicate_rows\": round(m22_raw_dups, 6),\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"life_expectancy_panel.csv\": int(n_raw)},\n \"random_seed\": None,\n \"input_sha256\": hashlib.sha256(SRC.read_bytes()).hexdigest(),\n}\nOUT.mkdir(exist_ok=True)\nana.to_csv(OUT / \"analytic.csv\", index=False, float_format=\"%.3f\")\npd.DataFrame(excl).to_csv(OUT / \"exclusions.csv\", index=False)\n(OUT / \"results.json\").write_text(json.dumps(res, indent=2, ensure_ascii=False) + \"\\n\")\nprint(json.dumps({k: res[k] for k in [\"n_countries_balanced\", \"n_analysed\", \"mean_2000_balanced\",\n \"mean_2022_balanced\", \"change_2000_2022_balanced\",\n \"mean_2022_all_available\"]}))\nstep,rule,n_before,n_after,n_removed,rationale\n1_dedup_iso3_year,\"keep one row per (iso3, year)\",439,416,23,same country (iso3) assembled twice under two name spellings with identical values\n2_drop_countries_missing_rows,\"drop countries without a row for every year 2000-2022 (BGD, ETH, IND, NGA)\",416,368,48,not observed in every year; balanced-panel estimand requires all 23 years\n3_drop_countries_with_empty_value,drop countries with an empty life_expectancy cell in some year (CHE),368,345,23,missing value may not be imputed; country not in balanced set\n{\"n_countries_balanced\": 15, \"n_analysed\": 345, \"mean_2000_balanced\": 78.060267, \"mean_2022_balanced\": 81.553067, \"change_2000_2022_balanced\": 3.4928, \"mean_2022_all_available\": 78.692}\nSAME\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RC0148","is_error":false}]},"uuid":"f0ca8e18-e7db-4960-8095-058da817276f","timestamp":"2026-10-30T04:45:46.094Z","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\"\"\"F1-conf-02: balanced-panel mean life expectancy 2000-2022. Reads data/ only.\"\"\"\nimport json, hashlib\nfrom pathlib import Path\nimport pandas as pd\n\nROOT = Path(__file__).resolve().parent.parent\nSRC = ROOT / \"data\" / \"life_expectancy_panel.csv\"\nOUT = ROOT / \"out\"\nYEARS = list(range(2000, 2023))\n\nraw = pd.read_csv(SRC, dtype={\"iso3\": str, \"country_name\": str})\nraw[\"iso3\"] = raw[\"iso3\"].str.strip()\nraw[\"country_name\"] = raw[\"country_name\"].str.strip()\nn_raw = len(raw)\nissues, excl = [], []\n\ndef step(name, rule, before, after, why):\n excl.append(dict(step=name, rule=rule, n_before=before, n_after=after,\n n_removed=before - after, rationale=why))\n\n# --- 1. duplicate (iso3, year) rows ---\ndup_mask = raw.duplicated([\"iso3\", \"year\"], keep=False)\ndups = raw[dup_mask]\nfor iso, g in dups.groupby(\"iso3\"):\n # verify duplicates carry identical values (otherwise conflict -> cannot choose)\n conflict = g.groupby(\"year\")[\"life_expectancy\"].nunique(dropna=False).gt(1).any()\n if conflict:\n raise SystemExit(f\"Conflicting duplicate values for {iso}; not resolvable without outside info\")\n names = sorted(g[\"country_name\"].unique())\n issues.append({\"issue\": f\"iso3 {iso} appears under {len(names)} country_name spellings {names} \"\n f\"with duplicated (iso3, year) rows for {g['year'].nunique()} years \"\n f\"({len(g)} rows); values are identical across duplicates\",\n \"handling\": \"kept one row per (iso3, year) (country identified by iso3); \"\n \"duplicate rows removed; country counted once\"})\n# canonical name: last spelling alphabetically ('Turkiye' over 'Turkey'), current WDI name\ncanon = raw.groupby(\"iso3\")[\"country_name\"].agg(lambda s: sorted(s.unique())[-1])\ndf = raw.drop_duplicates([\"iso3\", \"year\"], keep=\"first\").copy()\ndf[\"country_name\"] = df[\"iso3\"].map(canon)\nstep(\"1_dedup_iso3_year\", \"keep one row per (iso3, year)\", n_raw, len(df),\n \"same country (iso3) assembled twice under two name spellings with identical values\")\n\n# --- 2. incomplete countries ---\nn_total = df[\"iso3\"].nunique()\nobs = df.dropna(subset=[\"life_expectancy\"])\nobs = obs[obs[\"year\"].isin(YEARS)]\ncnt = obs.groupby(\"iso3\")[\"year\"].nunique().reindex(sorted(df[\"iso3\"].unique()), fill_value=0)\nbalanced = sorted(cnt[cnt == len(YEARS)].index)\nincomplete = sorted(set(df[\"iso3\"]) - set(balanced))\nfor iso in incomplete:\n yrs = sorted(set(obs.loc[obs.iso3 == iso, \"year\"]))\n missing = [y for y in YEARS if y not in yrs]\n blank = sorted(df.loc[(df.iso3 == iso) & df.life_expectancy.isna(), \"year\"].tolist())\n desc = (f\"{iso}: no recorded value for {len(missing)} of 23 years \"\n f\"({missing[0]}-{missing[-1]}\" + (\")\" if len(missing) == missing[-1]-missing[0]+1 else f\"; years {missing})\"))\n if blank:\n desc += f\"; empty life_expectancy cell in year(s) {blank}\"\n issues.append({\"issue\": desc, \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"})\n\nbefore = len(df)\npart_missing = df[df.iso3.isin(incomplete) & (df.groupby(\"iso3\")[\"year\"].transform(\"size\") < len(YEARS))]\n# split exclusion into two transparent steps\nshort = [i for i in incomplete if (df.iso3 == i).sum() < len(YEARS)]\ngaps = [i for i in incomplete if i not in short]\ndf2 = df[~df.iso3.isin(short)]\nstep(\"2_drop_countries_missing_rows\", f\"drop countries without a row for every year 2000-2022 ({', '.join(short)})\",\n before, len(df2), \"not observed in every year; balanced-panel estimand requires all 23 years\")\ndf3 = df2[~df2.iso3.isin(gaps)]\nstep(\"3_drop_countries_with_empty_value\", f\"drop countries with an empty life_expectancy cell in some year ({', '.join(gaps)})\",\n len(df2), len(df3), \"missing value may not be imputed; country not in balanced set\")\n\nana = df3[df3.year.isin(YEARS)].sort_values([\"iso3\", \"year\"])[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]]\nassert ana.life_expectancy.notna().all() and len(ana) == len(balanced) * len(YEARS)\nassert not ana.duplicated([\"iso3\", \"year\"]).any()\nif len(ana) != len(df3):\n step(\"4_restrict_years\", \"keep years 2000-2022\", len(df3), len(ana), \"outside study window\")\n\nm2000 = float(ana.loc[ana.year == 2000, \"life_expectancy\"].mean())\nm2022 = float(ana.loc[ana.year == 2022, \"life_expectancy\"].mean())\navail22 = df[(df.year == 2022) & df.life_expectancy.notna()]\nassert not avail22.iso3.duplicated().any()\nm22all = float(avail22[\"life_expectancy\"].mean())\n# descriptive dispersion of per-country changes (fixed set of countries, not a sample)\nw = ana.pivot(index=\"iso3\", columns=\"year\", values=\"life_expectancy\")\nchg = (w[2022] - w[2000])\n# sensitivity: naive 2022 mean if the duplicated TUR rows were NOT removed (wrong; shows impact)\nm22_raw_dups = float(raw.loc[raw.year == 2022, \"life_expectancy\"].dropna().mean())\n\nres = {\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": int(n_total),\n \"n_countries_balanced\": len(balanced),\n \"n_countries_incomplete\": len(incomplete),\n \"balanced_countries\": balanced,\n \"n_analysed\": int(len(ana)),\n \"mean_2000_balanced\": round(m2000, 6),\n \"mean_2022_balanced\": round(m2022, 6),\n \"change_2000_2022_balanced\": round(m2022 - m2000, 6),\n \"mean_2022_all_available\": round(m22all, 6),\n \"n_countries_2022_available\": int(avail22.iso3.nunique()),\n \"change_per_country_sd\": round(float(chg.std(ddof=1)), 6),\n \"change_per_country_min\": round(float(chg.min()), 6),\n \"change_per_country_min_iso3\": str(chg.idxmin()),\n \"change_per_country_max\": round(float(chg.max()), 6),\n \"change_per_country_max_iso3\": str(chg.idxmax()),\n \"sensitivity_mean_2022_naive_with_duplicate_rows\": round(m22_raw_dups, 6),\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"life_expectancy_panel.csv\": int(n_raw)},\n \"random_seed\": None,\n \"input_sha256\": hashlib.sha256(SRC.read_bytes()).hexdigest(),\n}\nOUT.mkdir(exist_ok=True)\nana.to_csv(OUT / \"analytic.csv\", index=False, float_format=\"%.3f\")\npd.DataFrame(excl).to_csv(OUT / \"exclusions.csv\", index=False)\n(OUT / \"results.json\").write_text(json.dumps(res, indent=2, ensure_ascii=False) + \"\\n\")\nprint(json.dumps({k: res[k] for k in [\"n_countries_balanced\", \"n_analysed\", \"mean_2000_balanced\",\n \"mean_2022_balanced\", \"change_2000_2022_balanced\",\n \"mean_2022_all_available\"]}))\nstep,rule,n_before,n_after,n_removed,rationale\n1_dedup_iso3_year,\"keep one row per (iso3, year)\",439,416,23,same country (iso3) assembled twice under two name spellings with identical values\n2_drop_countries_missing_rows,\"drop countries without a row for every year 2000-2022 (BGD, ETH, IND, NGA)\",416,368,48,not observed in every year; balanced-panel estimand requires all 23 years\n3_drop_countries_with_empty_value,drop countries with an empty life_expectancy cell in some year (CHE),368,345,23,missing value may not be imputed; country not in balanced set\n{\"n_countries_balanced\": 15, \"n_analysed\": 345, \"mean_2000_balanced\": 78.060267, \"mean_2022_balanced\": 81.553067, \"change_2000_2022_balanced\": 3.4928, \"mean_2022_all_available\": 78.692}\nSAME","stderr":"\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RC0148","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"34908d85-b3b0-43bc-bbfa-873378768866","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148","sessionId":"5853cc82-c65b-46dd-9952-3c6ece10287f","version":"2.1.314","gitBranch":"HEAD"}21{"parentUuid":"f0ca8e18-e7db-4960-8095-058da817276f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp8FfgHuon3EtpS4vg3","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0138BHdeDiQ9f5YBAK6sNZbs","name":"Bash","input":{"command":"cd ~/wp29/runs/RC0148; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/life_expectancy_panel.csv',dtype=str,keep_default_na=False)\nprint(d.shape); print(d.groupby(['iso3','country_name']).size())\nx=pd.to_numeric(d.life_expectancy,errors='coerce'); print(x.describe()); print(d[x.isna()])\nprint(d.year.unique())\nd['x']=x\nprint(d.groupby('iso3').x.agg(['min','max']))\np=d.drop_duplicates(['iso3','year']).pivot(index='iso3',columns='year',values='x')\nprint(p.diff(axis=1).abs().max(axis=1).sort_values())\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5381,"cache_read_input_tokens":2220,"output_tokens":708,"output_tokens_details":{"thinking_tokens":147},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":5381},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXp8F1zaFodWvrnQzGCM","requestRef":"bf7a5545-4f13-47b0-ba43-ec97581e3ad8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"72c4053e-f4ff-49c4-a71d-5f4d73d246ca","timestamp":"2026-10-30T04:45:48.031Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148","sessionId":"5853cc82-c65b-46dd-9952-3c6ece10287f","version":"2.1.314","gitBranch":"HEAD"}22{"parentUuid":"72c4053e-f4ff-49c4-a71d-5f4d73d246ca","isSidechain":false,"promptId":"0c4d1ffd-f436-4157-ae83-6932543d2157","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0138BHdeDiQ9f5YBAK6sNZbs","type":"tool_result","content":"(439, 4)\niso3 country_name \nAUS Australia 23\nAUT Austria 23\nBEL Belgium 23\nBGD Bangladesh 12\nCAN Canada 23\nCHE Switzerland 23\nDEU Germany 23\nESP Spain 23\nETH Ethiopia 12\nFRA France 23\nGBR United Kingdom 23\nIND India 12\nITA Italy 23\nJPN Japan 23\nKOR Korea, Rep. 23\nNGA Nigeria 12\nNLD Netherlands 23\nSWE Sweden 23\nTUR Turkey 23\n Turkiye 23\nUSA United States 23\ndtype: int64\ncount 438.000000\nmean 78.466521\nstd 6.073816\nmin 51.490000\n25% 77.738000\n50% 80.517500\n75% 81.803500\nmax 84.560000\nName: life_expectancy, dtype: float64\n country_name iso3 year life_expectancy\n331 Switzerland CHE 2007 \n['2000' '2001' '2002' '2003' '2004' '2005' '2006' '2007' '2008' '2009'\n '2010' '2011' '2012' '2013' '2014' '2015' '2016' '2017' '2018' '2019'\n '2020' '2021' '2022']\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\niso3\nAUS 0.400\nJPN 0.505\nDEU 0.600\nNGA 0.624\nCAN 0.629\nAUT 0.702\nNLD 0.753\nSWE 0.754\nKOR 0.847\nCHE 0.905\nGBR 1.037\nFRA 1.048\nBEL 1.300\nITA 1.303\nETH 1.567\nESP 1.600\nUSA 1.808\nTUR 1.869\nBGD 3.162\nIND 4.416\ndtype: float64","is_error":false}]},"uuid":"9b680a7d-c167-48ab-a9ae-cea73af3ecc6","timestamp":"2026-10-30T04:45:48.247Z","toolUseResult":{"stdout":"(439, 4)\niso3 country_name \nAUS Australia 23\nAUT Austria 23\nBEL Belgium 23\nBGD Bangladesh 12\nCAN Canada 23\nCHE Switzerland 23\nDEU Germany 23\nESP Spain 23\nETH Ethiopia 12\nFRA France 23\nGBR United Kingdom 23\nIND India 12\nITA Italy 23\nJPN Japan 23\nKOR Korea, Rep. 23\nNGA Nigeria 12\nNLD Netherlands 23\nSWE Sweden 23\nTUR Turkey 23\n Turkiye 23\nUSA United States 23\ndtype: int64\ncount 438.000000\nmean 78.466521\nstd 6.073816\nmin 51.490000\n25% 77.738000\n50% 80.517500\n75% 81.803500\nmax 84.560000\nName: life_expectancy, dtype: float64\n country_name iso3 year life_expectancy\n331 Switzerland CHE 2007 \n['2000' '2001' '2002' '2003' '2004' '2005' '2006' '2007' '2008' '2009'\n '2010' '2011' '2012' '2013' '2014' '2015' '2016' '2017' '2018' '2019'\n '2020' '2021' '2022']\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\niso3\nAUS 0.400\nJPN 0.505\nDEU 0.600\nNGA 0.624\nCAN 0.629\nAUT 0.702\nNLD 0.753\nSWE 0.754\nKOR 0.847\nCHE 0.905\nGBR 1.037\nFRA 1.048\nBEL 1.300\nITA 1.303\nETH 1.567\nESP 1.600\nUSA 1.808\nTUR 1.869\nBGD 3.162\nIND 4.416\ndtype: float64","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"72c4053e-f4ff-49c4-a71d-5f4d73d246ca","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148","sessionId":"5853cc82-c65b-46dd-9952-3c6ece10287f","version":"2.1.314","gitBranch":"HEAD"}23{"type":"api-request-blob","sessionId":"5853cc82-c65b-46dd-9952-3c6ece10287f","hash":"0e1df28a0120cac0df19ed25e54b8fba27a34c6e16b24c6239c91b6561d96508","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01RGXf8PMT4ChSX7uiGdFBvH","name":"Bash","input":{"command":"cat data/README.md; cat out/analysis.py; cat out/exclusions.csv; cd /tmp && rm -rf rc148 && mkdir rc148 && cp -r ~/wp29/runs/RC0148/data ~/wp29/runs/RC0148/out rc148/ 2>/dev/null; cd rc148 && ~/wp29/venv/bin/python out/analysis.py && diff out/results.json ~/wp29/runs/RC0148/out/results.json && diff out/analytic.csv ~/wp29/runs/RC0148/out/analytic.csv && echo SAME"}},{"type":"tool_use","id":"toolu_0138BHdeDiQ9f5YBAK6sNZbs","name":"Bash","input":{"command":"cd ~/wp29/runs/RC0148; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/life_expectancy_panel.csv',dtype=str,keep_default_na=False)\nprint(d.shape); print(d.groupby(['iso3','country_name']).size())\nx=pd.to_numeric(d.life_expectancy,errors='coerce'); print(x.describe()); print(d[x.isna()])\nprint(d.year.unique())\nd['x']=x\nprint(d.groupby('iso3').x.agg(['min','max']))\np=d.drop_duplicates(['iso3','year']).pivot(index='iso3',columns='year',values='x')\nprint(p.diff(axis=1).abs().max(axis=1).sort_values())\n\""}}]}}24{"type":"api-request-blob","sessionId":"5853cc82-c65b-46dd-9952-3c6ece10287f","hash":"6f29abcf8c2008e601279138702bd032dadbfbdd13592408c651c8a148b1ea08","message":{"role":"user","content":[{"tool_use_id":"toolu_01RGXf8PMT4ChSX7uiGdFBvH","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\"\"\"F1-conf-02: balanced-panel mean life expectancy 2000-2022. Reads data/ only.\"\"\"\nimport json, hashlib\nfrom pathlib import Path\nimport pandas as pd\n\nROOT = Path(__file__).resolve().parent.parent\nSRC = ROOT / \"data\" / \"life_expectancy_panel.csv\"\nOUT = ROOT / \"out\"\nYEARS = list(range(2000, 2023))\n\nraw = pd.read_csv(SRC, dtype={\"iso3\": str, \"country_name\": str})\nraw[\"iso3\"] = raw[\"iso3\"].str.strip()\nraw[\"country_name\"] = raw[\"country_name\"].str.strip()\nn_raw = len(raw)\nissues, excl = [], []\n\ndef step(name, rule, before, after, why):\n excl.append(dict(step=name, rule=rule, n_before=before, n_after=after,\n n_removed=before - after, rationale=why))\n\n# --- 1. duplicate (iso3, year) rows ---\ndup_mask = raw.duplicated([\"iso3\", \"year\"], keep=False)\ndups = raw[dup_mask]\nfor iso, g in dups.groupby(\"iso3\"):\n # verify duplicates carry identical values (otherwise conflict -> cannot choose)\n conflict = g.groupby(\"year\")[\"life_expectancy\"].nunique(dropna=False).gt(1).any()\n if conflict:\n raise SystemExit(f\"Conflicting duplicate values for {iso}; not resolvable without outside info\")\n names = sorted(g[\"country_name\"].unique())\n issues.append({\"issue\": f\"iso3 {iso} appears under {len(names)} country_name spellings {names} \"\n f\"with duplicated (iso3, year) rows for {g['year'].nunique()} years \"\n f\"({len(g)} rows); values are identical across duplicates\",\n \"handling\": \"kept one row per (iso3, year) (country identified by iso3); \"\n \"duplicate rows removed; country counted once\"})\n# canonical name: last spelling alphabetically ('Turkiye' over 'Turkey'), current WDI name\ncanon = raw.groupby(\"iso3\")[\"country_name\"].agg(lambda s: sorted(s.unique())[-1])\ndf = raw.drop_duplicates([\"iso3\", \"year\"], keep=\"first\").copy()\ndf[\"country_name\"] = df[\"iso3\"].map(canon)\nstep(\"1_dedup_iso3_year\", \"keep one row per (iso3, year)\", n_raw, len(df),\n \"same country (iso3) assembled twice under two name spellings with identical values\")\n\n# --- 2. incomplete countries ---\nn_total = df[\"iso3\"].nunique()\nobs = df.dropna(subset=[\"life_expectancy\"])\nobs = obs[obs[\"year\"].isin(YEARS)]\ncnt = obs.groupby(\"iso3\")[\"year\"].nunique().reindex(sorted(df[\"iso3\"].unique()), fill_value=0)\nbalanced = sorted(cnt[cnt == len(YEARS)].index)\nincomplete = sorted(set(df[\"iso3\"]) - set(balanced))\nfor iso in incomplete:\n yrs = sorted(set(obs.loc[obs.iso3 == iso, \"year\"]))\n missing = [y for y in YEARS if y not in yrs]\n blank = sorted(df.loc[(df.iso3 == iso) & df.life_expectancy.isna(), \"year\"].tolist())\n desc = (f\"{iso}: no recorded value for {len(missing)} of 23 years \"\n f\"({missing[0]}-{missing[-1]}\" + (\")\" if len(missing) == missing[-1]-missing[0]+1 else f\"; years {missing})\"))\n if blank:\n desc += f\"; empty life_expectancy cell in year(s) {blank}\"\n issues.append({\"issue\": desc, \"handling\": \"treated as missing (no imputation); country excluded from balanced set\"})\n\nbefore = len(df)\npart_missing = df[df.iso3.isin(incomplete) & (df.groupby(\"iso3\")[\"year\"].transform(\"size\") < len(YEARS))]\n# split exclusion into two transparent steps\nshort = [i for i in incomplete if (df.iso3 == i).sum() < len(YEARS)]\ngaps = [i for i in incomplete if i not in short]\ndf2 = df[~df.iso3.isin(short)]\nstep(\"2_drop_countries_missing_rows\", f\"drop countries without a row for every year 2000-2022 ({', '.join(short)})\",\n before, len(df2), \"not observed in every year; balanced-panel estimand requires all 23 years\")\ndf3 = df2[~df2.iso3.isin(gaps)]\nstep(\"3_drop_countries_with_empty_value\", f\"drop countries with an empty life_expectancy cell in some year ({', '.join(gaps)})\",\n len(df2), len(df3), \"missing value may not be imputed; country not in balanced set\")\n\nana = df3[df3.year.isin(YEARS)].sort_values([\"iso3\", \"year\"])[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]]\nassert ana.life_expectancy.notna().all() and len(ana) == len(balanced) * len(YEARS)\nassert not ana.duplicated([\"iso3\", \"year\"]).any()\nif len(ana) != len(df3):\n step(\"4_restrict_years\", \"keep years 2000-2022\", len(df3), len(ana), \"outside study window\")\n\nm2000 = float(ana.loc[ana.year == 2000, \"life_expectancy\"].mean())\nm2022 = float(ana.loc[ana.year == 2022, \"life_expectancy\"].mean())\navail22 = df[(df.year == 2022) & df.life_expectancy.notna()]\nassert not avail22.iso3.duplicated().any()\nm22all = float(avail22[\"life_expectancy\"].mean())\n# descriptive dispersion of per-country changes (fixed set of countries, not a sample)\nw = ana.pivot(index=\"iso3\", columns=\"year\", values=\"life_expectancy\")\nchg = (w[2022] - w[2000])\n# sensitivity: naive 2022 mean if the duplicated TUR rows were NOT removed (wrong; shows impact)\nm22_raw_dups = float(raw.loc[raw.year == 2022, \"life_expectancy\"].dropna().mean())\n\nres = {\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": int(n_total),\n \"n_countries_balanced\": len(balanced),\n \"n_countries_incomplete\": len(incomplete),\n \"balanced_countries\": balanced,\n \"n_analysed\": int(len(ana)),\n \"mean_2000_balanced\": round(m2000, 6),\n \"mean_2022_balanced\": round(m2022, 6),\n \"change_2000_2022_balanced\": round(m2022 - m2000, 6),\n \"mean_2022_all_available\": round(m22all, 6),\n \"n_countries_2022_available\": int(avail22.iso3.nunique()),\n \"change_per_country_sd\": round(float(chg.std(ddof=1)), 6),\n \"change_per_country_min\": round(float(chg.min()), 6),\n \"change_per_country_min_iso3\": str(chg.idxmin()),\n \"change_per_country_max\": round(float(chg.max()), 6),\n \"change_per_country_max_iso3\": str(chg.idxmax()),\n \"sensitivity_mean_2022_naive_with_duplicate_rows\": round(m22_raw_dups, 6),\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"life_expectancy_panel.csv\": int(n_raw)},\n \"random_seed\": None,\n \"input_sha256\": hashlib.sha256(SRC.read_bytes()).hexdigest(),\n}\nOUT.mkdir(exist_ok=True)\nana.to_csv(OUT / \"analytic.csv\", index=False, float_format=\"%.3f\")\npd.DataFrame(excl).to_csv(OUT / \"exclusions.csv\", index=False)\n(OUT / \"results.json\").write_text(json.dumps(res, indent=2, ensure_ascii=False) + \"\\n\")\nprint(json.dumps({k: res[k] for k in [\"n_countries_balanced\", \"n_analysed\", \"mean_2000_balanced\",\n \"mean_2022_balanced\", \"change_2000_2022_balanced\",\n \"mean_2022_all_available\"]}))\nstep,rule,n_before,n_after,n_removed,rationale\n1_dedup_iso3_year,\"keep one row per (iso3, year)\",439,416,23,same country (iso3) assembled twice under two name spellings with identical values\n2_drop_countries_missing_rows,\"drop countries without a row for every year 2000-2022 (BGD, ETH, IND, NGA)\",416,368,48,not observed in every year; balanced-panel estimand requires all 23 years\n3_drop_countries_with_empty_value,drop countries with an empty life_expectancy cell in some year (CHE),368,345,23,missing value may not be imputed; country not in balanced set\n{\"n_countries_balanced\": 15, \"n_analysed\": 345, \"mean_2000_balanced\": 78.060267, \"mean_2022_balanced\": 81.553067, \"change_2000_2022_balanced\": 3.4928, \"mean_2022_all_available\": 78.692}\nSAME\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RC0148","is_error":false},{"tool_use_id":"toolu_0138BHdeDiQ9f5YBAK6sNZbs","type":"tool_result","content":"(439, 4)\niso3 country_name \nAUS Australia 23\nAUT Austria 23\nBEL Belgium 23\nBGD Bangladesh 12\nCAN Canada 23\nCHE Switzerland 23\nDEU Germany 23\nESP Spain 23\nETH Ethiopia 12\nFRA France 23\nGBR United Kingdom 23\nIND India 12\nITA Italy 23\nJPN Japan 23\nKOR Korea, Rep. 23\nNGA Nigeria 12\nNLD Netherlands 23\nSWE Sweden 23\nTUR Turkey 23\n Turkiye 23\nUSA United States 23\ndtype: int64\ncount 438.000000\nmean 78.466521\nstd 6.073816\nmin 51.490000\n25% 77.738000\n50% 80.517500\n75% 81.803500\nmax 84.560000\nName: life_expectancy, dtype: float64\n country_name iso3 year life_expectancy\n331 Switzerland CHE 2007 \n['2000' '2001' '2002' '2003' '2004' '2005' '2006' '2007' '2008' '2009'\n '2010' '2011' '2012' '2013' '2014' '2015' '2016' '2017' '2018' '2019'\n '2020' '2021' '2022']\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\niso3\nAUS 0.400\nJPN 0.505\nDEU 0.600\nNGA 0.624\nCAN 0.629\nAUT 0.702\nNLD 0.753\nSWE 0.754\nKOR 0.847\nCHE 0.905\nGBR 1.037\nFRA 1.048\nBEL 1.300\nITA 1.303\nETH 1.567\nESP 1.600\nUSA 1.808\nTUR 1.869\nBGD 3.162\nIND 4.416\ndtype: float64","is_error":false}]}}25{"type":"api-request","sessionId":"5853cc82-c65b-46dd-9952-3c6ece10287f","id":"1156feee-fa55-48c9-b88e-2e38296337d3","timestamp":"2026-10-30T04:45:48.250Z","version":"2.1.314","querySource":"sdk","shapeHash":"f0e8f933ff6ce9ab4f017f98ba1bba64a5bab9632065a8616cba5c4cfdbf3749","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":"bf7a5545-4f13-47b0-ba43-ec97581e3ad8","keep":4,"tail":["0e1df28a0120cac0df19ed25e54b8fba27a34c6e16b24c6239c91b6561d96508","6f29abcf8c2008e601279138702bd032dadbfbdd13592408c651c8a148b1ea08"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",1],"value":{"type":"ephemeral"}}],"requestDigest":"f2f0f62bb0a15cdcd519a7608f114a8f48026c6390a888d947f26151e5388965"}26{"type":"last-prompt","lastPrompt":"Review","leafUuid":"9b680a7d-c167-48ab-a9ae-cea73af3ecc6","sessionId":"5853cc82-c65b-46dd-9952-3c6ece10287f"}27{"type":"atis-latch","atis":"","sessionId":"5853cc82-c65b-46dd-9952-3c6ece10287f"}28{"parentUuid":"9b680a7d-c167-48ab-a9ae-cea73af3ecc6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp8mJe7B9hNygBqhhSq","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":5520,"cache_read_input_tokens":7601,"output_tokens":366,"output_tokens_details":{"thinking_tokens":106},"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":5520},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":706,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXp8m31j5Rxvf9bx37e3","requestRef":"1156feee-fa55-48c9-b88e-2e38296337d3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c077ed69-d770-42ae-8028-17e112453cfd","timestamp":"2026-10-30T04:45:50.416Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148","sessionId":"5853cc82-c65b-46dd-9952-3c6ece10287f","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"c077ed69-d770-42ae-8028-17e112453cfd","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp8mJe7B9hNygBqhhSq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TFwXEe47Q6CE7xLK47PoSi","name":"Bash","input":{"command":"cd ~/wp29/runs/RC0148; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/life_expectancy_panel.csv')\nd=d.drop_duplicates(['iso3','year'])\np=d.pivot(index='iso3',columns='year',values='life_expectancy')\npd.set_option('display.width',250)\nprint(p.loc[['IND','BGD','ETH','NGA','TUR','USA','CHE']].T)\n\"; grep -c Turk data/life_expectancy_panel.csv; head -3 data/life_expectancy_panel.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5520,"cache_read_input_tokens":7601,"output_tokens":366,"output_tokens_details":{"thinking_tokens":106},"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":5520},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp8m31j5Rxvf9bx37e3","requestRef":"1156feee-fa55-48c9-b88e-2e38296337d3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f86f8386-85db-4c80-87eb-ff1d6824cb60","timestamp":"2026-10-30T04:45:52.315Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148","sessionId":"5853cc82-c65b-46dd-9952-3c6ece10287f","version":"2.1.314","gitBranch":"HEAD"}30{"parentUuid":"f86f8386-85db-4c80-87eb-ff1d6824cb60","isSidechain":false,"promptId":"0c4d1ffd-f436-4157-ae83-6932543d2157","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TFwXEe47Q6CE7xLK47PoSi","type":"tool_result","content":"iso3 IND BGD ETH NGA TUR USA CHE\nyear \n2000 NaN NaN NaN NaN 71.931 76.637 79.680\n2001 NaN NaN NaN NaN 72.271 76.837 80.180\n2002 NaN NaN NaN NaN 72.605 76.937 80.385\n2003 NaN NaN NaN NaN 72.906 77.037 80.537\n2004 NaN NaN NaN NaN 73.223 77.488 81.088\n2005 NaN NaN NaN NaN 73.510 77.488 81.237\n2006 NaN NaN NaN NaN 73.804 77.688 81.490\n2007 NaN NaN NaN NaN 74.129 77.988 NaN\n2008 NaN NaN NaN NaN 74.380 78.039 81.993\n2009 NaN NaN NaN NaN 74.679 78.390 82.044\n2010 NaN NaN NaN NaN 74.964 78.541 82.246\n2011 67.623 68.499 60.595 51.490 74.823 78.641 82.695\n2012 68.076 68.989 61.443 51.688 75.552 78.741 82.698\n2013 68.499 69.487 62.161 51.827 76.171 78.741 82.798\n2014 68.932 70.016 62.927 51.940 76.452 78.841 83.198\n2015 69.326 70.543 63.592 51.939 76.539 78.690 82.898\n2016 69.709 71.075 64.162 52.189 76.595 78.539 83.602\n2017 70.068 71.606 64.766 52.397 77.037 78.539 83.551\n2018 70.415 72.122 65.257 52.669 77.454 78.639 83.754\n2019 70.746 72.625 65.741 53.009 77.737 78.788 83.905\n2020 70.156 71.421 65.969 53.072 76.525 76.980 83.000\n2021 67.282 71.103 65.330 53.455 75.722 76.329 83.751\n2022 71.698 74.265 66.897 54.079 77.591 77.434 83.605\n46\ncountry_name,iso3,year,life_expectancy\nAustralia,AUS,2000,79.234\nAustralia,AUS,2001,79.634","is_error":false}]},"uuid":"1e874663-aef3-49c8-a59b-279cc0d15792","timestamp":"2026-10-30T04:45:52.539Z","toolUseResult":{"stdout":"iso3 IND BGD ETH NGA TUR USA CHE\nyear \n2000 NaN NaN NaN NaN 71.931 76.637 79.680\n2001 NaN NaN NaN NaN 72.271 76.837 80.180\n2002 NaN NaN NaN NaN 72.605 76.937 80.385\n2003 NaN NaN NaN NaN 72.906 77.037 80.537\n2004 NaN NaN NaN NaN 73.223 77.488 81.088\n2005 NaN NaN NaN NaN 73.510 77.488 81.237\n2006 NaN NaN NaN NaN 73.804 77.688 81.490\n2007 NaN NaN NaN NaN 74.129 77.988 NaN\n2008 NaN NaN NaN NaN 74.380 78.039 81.993\n2009 NaN NaN NaN NaN 74.679 78.390 82.044\n2010 NaN NaN NaN NaN 74.964 78.541 82.246\n2011 67.623 68.499 60.595 51.490 74.823 78.641 82.695\n2012 68.076 68.989 61.443 51.688 75.552 78.741 82.698\n2013 68.499 69.487 62.161 51.827 76.171 78.741 82.798\n2014 68.932 70.016 62.927 51.940 76.452 78.841 83.198\n2015 69.326 70.543 63.592 51.939 76.539 78.690 82.898\n2016 69.709 71.075 64.162 52.189 76.595 78.539 83.602\n2017 70.068 71.606 64.766 52.397 77.037 78.539 83.551\n2018 70.415 72.122 65.257 52.669 77.454 78.639 83.754\n2019 70.746 72.625 65.741 53.009 77.737 78.788 83.905\n2020 70.156 71.421 65.969 53.072 76.525 76.980 83.000\n2021 67.282 71.103 65.330 53.455 75.722 76.329 83.751\n2022 71.698 74.265 66.897 54.079 77.591 77.434 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~/wp29/runs/RC0148; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/life_expectancy_panel.csv')\nd=d.drop_duplicates(['iso3','year'])\np=d.pivot(index='iso3',columns='year',values='life_expectancy')\npd.set_option('display.width',250)\nprint(p.loc[['IND','BGD','ETH','NGA','TUR','USA','CHE']].T)\n\"; grep -c Turk data/life_expectancy_panel.csv; head -3 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The canonical name 'Turkiye' differs from the README spelling 'Türkiye'. None of these affect any number.\",\n \"suggested_fix\": \"Print single years as one year, delete the dead code, and optionally use the README spelling.\"\n },\n {\n \"id\": \"R3\",\n \"severity\": \"minor\",\n \"location\": \"out/report.md (Limitations)\",\n \"issue\": \"'Including them would probably change the mean levels a lot' is a guess about the missing 2000–2010 values. It is hedged and not used in any result, but it goes beyond what the evidence shows.\",\n \"suggested_fix\": \"Limit the statement to the observed 2011–2022 levels of BGD, ETH, IND and NGA, which are far below the balanced mean.\"\n }\n ],\n \"summary\": \"I re-ran analysis.py in a scratch copy and got identical results.json and analytic.csv. I checked the raw data independently. It has 439 rows. TUR appears under two names, 'Turkey' and 'Turkiye', with identical values in all 23 years. BGD, ETH, IND and NGA have rows only for 2011–2022. CHE has a single empty cell, in 2007. I found no other anomalies: there are no out-of-window years and no implausible values or jumps. The deliverables report all of these issues and handle them correctly: one row per (iso3, year) is kept, nothing is imputed, and only data/ is used. The counts reconcile (20 countries, 15 balanced, 5 incomplete, 345 = 15×23), and the exclusion ledger chains 439→416→368→345. The 2022 baseline uses 20 distinct countries, each counted once. All required keys are present. The claims match results.json, and the caveats about uncertainty and representativeness are appropriate. Only minor presentation issues remain.\"\n}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok"}}]}}42{"type":"api-request","sessionId":"5853cc82-c65b-46dd-9952-3c6ece10287f","id":"c513f228-cf95-4e09-83f5-01220b629315","timestamp":"2026-10-30T04:46:08.515Z","version":"2.1.314","querySource":"sdk","shapeHash":"f0e8f933ff6ce9ab4f017f98ba1bba64a5bab9632065a8616cba5c4cfdbf3749","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":"d30da6bc-7977-4cbe-ba6a-29cd13b2fcab","keep":10,"tail":["c864234d840581d9423c76f29bf8c5b543e38716d1877e83d85f4463261e7064","d463fc7d845ff4ba125050c832cf801171f19f7163af3c0995fbe851e4e09d39"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5ef01e534589b6a6a159e69b089208fc082fef24540678aa17f36f9f61b3056b"}43{"type":"last-prompt","lastPrompt":"Review","leafUuid":"21ffabd1-c5d1-4022-af72-cddd55a854bf","sessionId":"5853cc82-c65b-46dd-9952-3c6ece10287f"}44{"type":"atis-latch","atis":"","sessionId":"5853cc82-c65b-46dd-9952-3c6ece10287f"}45{"parentUuid":"21ffabd1-c5d1-4022-af72-cddd55a854bf","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpAFx6QjMJXyGRcEpku","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1212,"cache_read_input_tokens":14937,"output_tokens":655,"output_tokens_details":{"thinking_tokens":5},"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":1212},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":5497,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpAFgisgAwdPTLEyvsu","requestRef":"c513f228-cf95-4e09-83f5-01220b629315","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d68f536d-25cd-46f6-acf6-4849fc6768f6","timestamp":"2026-10-30T04:46:15.091Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148","sessionId":"5853cc82-c65b-46dd-9952-3c6ece10287f","version":"2.1.314","gitBranch":"HEAD"}46{"parentUuid":"d68f536d-25cd-46f6-acf6-4849fc6768f6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpAFx6QjMJXyGRcEpku","type":"message","role":"assistant","content":[{"type":"text","text":"My verdict is **accept**: I found no critical or major problems, only three minor ones. I wrote the review to `out/review/review.json`.\n\nI re-ran `out/analysis.py` in a scratch copy and it reproduced `results.json` and `analytic.csv` exactly. I also checked the raw data myself. The submission found and handled every problem I found:\n- **Turkey duplicated:** it appears twice, under \"Turkey\" and \"Turkiye\", with identical values in all 23 years. The code keeps one row per country and year, and stops if duplicates ever disagree.\n- **Four countries with gaps:** Bangladesh, Ethiopia, India and Nigeria have no rows for 2000–2010.\n- **Switzerland:** one empty value, in 2007.\n\nThe code reads only the input file and fills in no missing values. The counts add up: 20 countries in total, 15 complete, 5 dropped, and 345 rows (15 × 23). The exclusion ledger runs 439 → 416 → 368 → 345 rows. The 2022 baseline counts each of the 20 countries once. Every required key is present, the claims match the numbers in `results.json`, and the stated caveats are reasonable.\n\nThe three minor issues:\n1. **Unverifiable hash claim:** the report says the script was briefly written into `data/` and that the input file's hash was checked afterwards and is unchanged. `data/README.md` only gives the hash of the original download, not of the CSV, so that check can't be confirmed. The stray folder is gone and `data/` holds only the original two files.\n2. **Cosmetic code and text issues:** an issue entry reads \"(2007-2007)\", the script has one unused variable, and it spells the country \"Turkiye\" where the README has \"Türkiye\". None of these change any number.\n3. **Speculative sentence:** the report guesses that including the four excluded countries \"would probably change the mean levels a lot\". It is hedged and feeds no result, but it goes beyond the evidence."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1212,"cache_read_input_tokens":14937,"output_tokens":655,"output_tokens_details":{"thinking_tokens":5},"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":1212},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpAFgisgAwdPTLEyvsu","requestRef":"c513f228-cf95-4e09-83f5-01220b629315","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"63173d5e-2218-4638-861d-b91b9c5c6078","timestamp":"2026-10-30T04:46:15.101Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0148","sessionId":"5853cc82-c65b-46dd-9952-3c6ece10287f","version":"2.1.314","gitBranch":"HEAD"}47{"type":"last-prompt","lastPrompt":"Review","leafUuid":"63173d5e-2218-4638-861d-b91b9c5c6078","sessionId":"5853cc82-c65b-46dd-9952-3c6ece10287f"}48{"type":"cost-state","sessionId":"5853cc82-c65b-46dd-9952-3c6ece10287f","totalCostUSD":0.1528754,"totalAPIDuration":35657,"totalAPIDurationWithoutRetries":35653,"totalToolDuration":755,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":36319,"startTime":1793335538806,"modelUsage":{"claude-opus-5-5":{"inputTokens":14,"outputTokens":3443,"thinkingTokens":697,"cacheReadInputTokens":53672,"cacheCreationInputTokens":14645,"cacheCreationEphemeral5mInputTokens":14645,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.1528754}},"hasUnknownModelCost":false}49