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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. 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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 00:44 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 00:43 ..\n-rw-r--r-- 1 simon-pierreboucher staff 5910 oct. 30 00:43 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 8620 oct. 30 00:43 analytic.csv\n-rw-r--r-- 1 simon-pierreboucher staff 1232 oct. 30 00:44 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 640 oct. 30 00:43 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 3401 oct. 30 00:44 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 2502 oct. 30 00:43 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 00:44 review\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 00:44 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_all_available\": 20,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"TUR appears under two country names ['Turkey', 'Turkiye'] (two extractions), giving 23 duplicated (iso3, year) rows with identical life_expectancy values\",\n \"handling\": \"Kept one row per (iso3, year) (values identical, so no conflict); country_name harmonised to 'Turkiye'; country counted once\"\n },\n {\n \"issue\": \"BGD has rows only for 2011-2022 (12 of 23 years)\",\n \"handling\": \"Excluded from the balanced set; no imputation. Included in the 2022 all-available baseline\"\n },\n {\n \"issue\": \"ETH has rows only for 2011-2022 (12 of 23 years)\",\n \"handling\": \"Excluded from the balanced set; no imputation. Included in the 2022 all-available baseline\"\n },\n {\n \"issue\": \"IND has rows only for 2011-2022 (12 of 23 years)\",\n \"handling\": \"Excluded from the balanced set; no imputation. Included in the 2022 all-available baseline\"\n },\n {\n \"issue\": \"NGA has rows only for 2011-2022 (12 of 23 years)\",\n \"handling\": \"Excluded from the balanced set; no imputation. Included in the 2022 all-available baseline\"\n },\n {\n \"issue\": \"CHE has an empty life_expectancy cell for year(s) [2007]\",\n \"handling\": \"Treated as missing (no interpolation); country excluded from the balanced set\"\n }\n ],\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null,\n \"balanced_yearly_means\": {\n \"2000\": 78.060267,\n \"2001\": 78.3652,\n \"2002\": 78.5194,\n \"2003\": 78.651,\n \"2004\": 79.118667,\n \"2005\": 79.2918,\n \"2006\": 79.671467,\n \"2007\": 79.9296,\n \"2008\": 80.088,\n \"2009\": 80.350667,\n \"2010\": 80.541,\n \"2011\": 80.849533,\n \"2012\": 80.941933,\n \"2013\": 81.191867,\n \"2014\": 81.524,\n \"2015\": 81.3644,\n \"2016\": 81.6016,\n \"2017\": 81.664867,\n \"2018\": 81.783067,\n \"2019\": 82.0732,\n \"2020\": 81.320067,\n \"2021\": 81.447067,\n \"2022\": 81.553067\n }\n}[\n {\"id\":\"C1\",\"text\":\"The balanced panel contains 15 of 20 countries (345 country-years); BGD, ETH, IND, NGA and CHE are incomplete.\",\"results_keys\":[\"n_countries_balanced\",\"n_countries_total\",\"n_countries_incomplete\",\"balanced_countries\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"CHE excluded for a single empty cell (2007).\"},\n {\"id\":\"C2\",\"text\":\"Balanced-panel mean life expectancy rose from 78.060 years in 2000 to 81.553 years in 2022, a change of +3.493 years.\",\"results_keys\":[\"mean_2000_balanced\",\"mean_2022_balanced\",\"change_2000_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Unweighted mean over a mostly high-income set; descriptive, no sampling uncertainty.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries (78.692) is about 2.9 years below the balanced 2022 mean because it includes lower-life-expectancy countries.\",\"results_keys\":[\"mean_2022_all_available\",\"mean_2022_balanced\",\"n_countries_2022_all_available\"],\"status\":\"supported\",\"caveat\":\"Composition difference, not a time trend.\"},\n {\"id\":\"C4\",\"text\":\"TUR appears twice (Turkey/Turkiye) with identical values; it was deduplicated and counted once.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Question\nChange in unweighted mean life expectancy at birth (WDI `SP.DYN.LE00.IN`) between 2000 and 2022 over the fixed set of countries with a recorded value in every year 2000–2022 (balanced panel). The 2022 mean over all available countries is reported only as a descriptive baseline.\n\n## Data and exclusions\n`data/life_expectancy_panel.csv`: 439 data rows (`n_input_rows`), 20 distinct iso3 codes (`n_countries_total`). Issues found (`data_issues`):\n- TUR appears under two names (\"Turkey\", \"Turkiye\") from the two extractions, 23 duplicated (iso3, year) rows; values identical in all 23 pairs, so one row per country-year was kept and the name harmonised to \"Turkiye\".\n- BGD, ETH, IND, NGA have rows only for 2011–2022 → excluded from the balanced set.\n- CHE has an empty value for 2007 → treated as missing (no interpolation) → excluded.\n\nExclusion ledger (`out/exclusions.csv`): 439 → (year scope, 0 removed) 439 → (dedupe, −23) 416 → (countries lacking year rows, −48) 368 → (countries with an empty cell, −23) 345 = `n_analysed`. Balanced set: 15 countries (`n_countries_balanced`, `balanced_countries`: AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA); 5 incomplete (`n_countries_incomplete`).\n\n## Method\nSimple unweighted mean of life expectancy across the 15 balanced countries in each year; change = 2022 mean − 2000 mean. Baseline = mean over all distinct iso3 with a recorded 2022 value (after deduplication, so TUR counts once). Deterministic; no randomness (`random_seed` = null). Results were cross-checked by an independent recomputation without pandas (see `out/state/checks.md`).\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, all 20 countries with a 2022 value: 78.692 years (`mean_2022_all_available`, `n_countries_2022_all_available`). It is about 2.9 years below the balanced 2022 mean because it includes the lower-life-expectancy countries (BGD, ETH, IND, NGA) left out of the balanced set; this shows why the baseline is not the estimand.\n- Yearly balanced means are in `balanced_yearly_means`. They peak in 2019 (82.073) and dip in 2020 (81.320).\n\n## Uncertainty\nThese are descriptive figures computed exactly for a fixed set of countries. There is no sampling design, so no sampling standard error or confidence interval is reported. The only uncertainty is in the source estimates themselves: WDI values are modelled and get revised between releases, and this file does not quantify that.\n\n## Limitations and open issues\n- The balanced set has 15 countries and is mostly high-income (TUR is the only middle-income member). It leaves out all four lower-income countries and Switzerland. So the change describes this group of countries, not the 20-country sample or the world.\n- Switzerland is excluded only because of one empty cell (2007). This follows the rule as written (no interpolation).\n- The means are unweighted, not weighted by population.\n- The two TUR extractions agreed exactly, so the deduplication did not depend on which copy was kept. The README gives a SHA-256 for the raw API response, not for the CSV, so I could not check the CSV against it.\n- Feasibility: complete.","is_error":false}]},"uuid":"3ec3d692-7e8f-4ab9-8082-eca3843134f7","timestamp":"2026-10-30T04:44:35.537Z","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 00:44 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 00:43 ..\n-rw-r--r-- 1 simon-pierreboucher staff 5910 oct. 30 00:43 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 8620 oct. 30 00:43 analytic.csv\n-rw-r--r-- 1 simon-pierreboucher staff 1232 oct. 30 00:44 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 640 oct. 30 00:43 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 3401 oct. 30 00:44 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 2502 oct. 30 00:43 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 00:44 review\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 00:44 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_all_available\": 20,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"TUR appears under two country names ['Turkey', 'Turkiye'] (two extractions), giving 23 duplicated (iso3, year) rows with identical life_expectancy values\",\n \"handling\": \"Kept one row per (iso3, year) (values identical, so no conflict); country_name harmonised to 'Turkiye'; country counted once\"\n },\n {\n \"issue\": \"BGD has rows only for 2011-2022 (12 of 23 years)\",\n \"handling\": \"Excluded from the balanced set; no imputation. Included in the 2022 all-available baseline\"\n },\n {\n \"issue\": \"ETH has rows only for 2011-2022 (12 of 23 years)\",\n \"handling\": \"Excluded from the balanced set; no imputation. Included in the 2022 all-available baseline\"\n },\n {\n \"issue\": \"IND has rows only for 2011-2022 (12 of 23 years)\",\n \"handling\": \"Excluded from the balanced set; no imputation. Included in the 2022 all-available baseline\"\n },\n {\n \"issue\": \"NGA has rows only for 2011-2022 (12 of 23 years)\",\n \"handling\": \"Excluded from the balanced set; no imputation. Included in the 2022 all-available baseline\"\n },\n {\n \"issue\": \"CHE has an empty life_expectancy cell for year(s) [2007]\",\n \"handling\": \"Treated as missing (no interpolation); country excluded from the balanced set\"\n }\n ],\n \"n_input_rows\": {\n \"life_expectancy_panel.csv\": 439\n },\n \"random_seed\": null,\n \"balanced_yearly_means\": {\n \"2000\": 78.060267,\n \"2001\": 78.3652,\n \"2002\": 78.5194,\n \"2003\": 78.651,\n \"2004\": 79.118667,\n \"2005\": 79.2918,\n \"2006\": 79.671467,\n \"2007\": 79.9296,\n \"2008\": 80.088,\n \"2009\": 80.350667,\n \"2010\": 80.541,\n \"2011\": 80.849533,\n \"2012\": 80.941933,\n \"2013\": 81.191867,\n \"2014\": 81.524,\n \"2015\": 81.3644,\n \"2016\": 81.6016,\n \"2017\": 81.664867,\n \"2018\": 81.783067,\n \"2019\": 82.0732,\n \"2020\": 81.320067,\n \"2021\": 81.447067,\n \"2022\": 81.553067\n }\n}[\n {\"id\":\"C1\",\"text\":\"The balanced panel contains 15 of 20 countries (345 country-years); BGD, ETH, IND, NGA and CHE are incomplete.\",\"results_keys\":[\"n_countries_balanced\",\"n_countries_total\",\"n_countries_incomplete\",\"balanced_countries\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"CHE excluded for a single empty cell (2007).\"},\n {\"id\":\"C2\",\"text\":\"Balanced-panel mean life expectancy rose from 78.060 years in 2000 to 81.553 years in 2022, a change of +3.493 years.\",\"results_keys\":[\"mean_2000_balanced\",\"mean_2022_balanced\",\"change_2000_2022_balanced\"],\"status\":\"supported\",\"caveat\":\"Unweighted mean over a mostly high-income set; descriptive, no sampling uncertainty.\"},\n {\"id\":\"C3\",\"text\":\"The naive 2022 mean over all 20 available countries (78.692) is about 2.9 years below the balanced 2022 mean because it includes lower-life-expectancy countries.\",\"results_keys\":[\"mean_2022_all_available\",\"mean_2022_balanced\",\"n_countries_2022_all_available\"],\"status\":\"supported\",\"caveat\":\"Composition difference, not a time trend.\"},\n {\"id\":\"C4\",\"text\":\"TUR appears twice (Turkey/Turkiye) with identical values; it was deduplicated and counted once.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\n# F1-conf-02 — Balanced-panel mean life expectancy, 2000–2022\n\n## Question\nChange in unweighted mean life expectancy at birth (WDI `SP.DYN.LE00.IN`) between 2000 and 2022 over the fixed set of countries with a recorded value in every year 2000–2022 (balanced panel). The 2022 mean over all available countries is reported only as a descriptive baseline.\n\n## Data and exclusions\n`data/life_expectancy_panel.csv`: 439 data rows (`n_input_rows`), 20 distinct iso3 codes (`n_countries_total`). Issues found (`data_issues`):\n- TUR appears under two names (\"Turkey\", \"Turkiye\") from the two extractions, 23 duplicated (iso3, year) rows; values identical in all 23 pairs, so one row per country-year was kept and the name harmonised to \"Turkiye\".\n- BGD, ETH, IND, NGA have rows only for 2011–2022 → excluded from the balanced set.\n- CHE has an empty value for 2007 → treated as missing (no interpolation) → excluded.\n\nExclusion ledger (`out/exclusions.csv`): 439 → (year scope, 0 removed) 439 → (dedupe, −23) 416 → (countries lacking year rows, −48) 368 → (countries with an empty cell, −23) 345 = `n_analysed`. Balanced set: 15 countries (`n_countries_balanced`, `balanced_countries`: AUS, AUT, BEL, CAN, DEU, ESP, FRA, GBR, ITA, JPN, KOR, NLD, SWE, TUR, USA); 5 incomplete (`n_countries_incomplete`).\n\n## Method\nSimple unweighted mean of life expectancy across the 15 balanced countries in each year; change = 2022 mean − 2000 mean. Baseline = mean over all distinct iso3 with a recorded 2022 value (after deduplication, so TUR counts once). Deterministic; no randomness (`random_seed` = null). Results were cross-checked by an independent recomputation without pandas (see `out/state/checks.md`).\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, all 20 countries with a 2022 value: 78.692 years (`mean_2022_all_available`, `n_countries_2022_all_available`). It is about 2.9 years below the balanced 2022 mean because it includes the lower-life-expectancy countries (BGD, ETH, IND, NGA) left out of the balanced set; this shows why the baseline is not the estimand.\n- Yearly balanced means are in `balanced_yearly_means`. They peak in 2019 (82.073) and dip in 2020 (81.320).\n\n## Uncertainty\nThese are descriptive figures computed exactly for a fixed set of countries. There is no sampling design, so no sampling standard error or confidence interval is reported. The only uncertainty is in the source estimates themselves: WDI values are modelled and get revised between releases, and this file does not quantify that.\n\n## Limitations and open issues\n- The balanced set has 15 countries and is mostly high-income (TUR is the only middle-income member). It leaves out all four lower-income countries and Switzerland. So the change describes this group of countries, not the 20-country sample or the world.\n- Switzerland is excluded only because of one empty cell (2007). This follows the rule as written (no interpolation).\n- The means are unweighted, not weighted by population.\n- The two TUR extractions agreed exactly, so the deduplication did not depend on which copy was kept. 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writes out/results.json, out/analytic.csv, out/exclusions.csv.\nNo randomness is used.\"\"\"\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={\"country_name\": str, \"iso3\": str})\nraw[\"iso3\"] = raw[\"iso3\"].str.strip()\nraw[\"country_name\"] = raw[\"country_name\"].str.strip()\nn_raw = len(raw)\nledger, issues = [], []\n\ndef step(name, rule, before, after, rationale):\n ledger.append(dict(step=name, rule=rule, n_before=len(before), n_after=len(after),\n n_removed=len(before) - len(after), rationale=rationale))\n\n# --- Step 1: restrict to 2000-2022 (scope check) ---\nd0 = raw\nd1 = d0[d0[\"year\"].isin(YEARS)].copy()\nstep(\"1_year_scope\", \"keep rows with year in 2000..2022\", d0, d1, \"Task scope; all rows already in range\")\n\n# --- Step 2: resolve duplicate (iso3, year) rows ---\ndup_mask = d1.duplicated([\"iso3\", \"year\"], keep=False)\ndups = d1[dup_mask]\nconflicts = []\nfor (iso, yr), g in dups.groupby([\"iso3\", \"year\"]):\n vals = g[\"life_expectancy\"].dropna().unique()\n if len(vals) > 1:\n conflicts.append((iso, yr, list(vals)))\nif len(conflicts):\n raise SystemExit(f\"Conflicting duplicate values, need a decision: {conflicts}\")\nnames_by_iso = d1.groupby(\"iso3\")[\"country_name\"].unique()\nmulti_name = {k: sorted(v) for k, v in names_by_iso.items() if len(v) > 1}\n# canonical name: current World Bank spelling for TUR; otherwise the unique name\ncanonical = {\"TUR\": \"Turkiye\"}\nd1[\"country_name\"] = [canonical.get(i, n) if i in multi_name else n\n for i, n in zip(d1[\"iso3\"], d1[\"country_name\"])]\nd2 = d1.sort_values([\"iso3\", \"year\"]).drop_duplicates([\"iso3\", \"year\"], keep=\"first\")\nstep(\"2_dedupe_iso3_year\", \"one row per (iso3, year); duplicates had identical values\",\n d1, d2, \"Country identified by iso3; each country counted at most once per year\")\nif len(dups):\n for iso, nm in multi_name.items():\n n_dup = int(dups[dups.iso3 == iso].shape[0] - dups[dups.iso3 == iso][[\"iso3\", \"year\"]].drop_duplicates().shape[0])\n issues.append({\"issue\": f\"{iso} appears under two country names {nm} (two extractions), giving {n_dup} duplicated (iso3, year) rows with identical life_expectancy values\",\n \"handling\": f\"Kept one row per (iso3, year) (values identical, so no conflict); country_name harmonised to '{canonical.get(iso)}'; country counted once\"})\n\n# --- Step 3: drop countries lacking a row for some year ---\nyrs_present = d2.groupby(\"iso3\")[\"year\"].nunique()\nshort = sorted(yrs_present[yrs_present < len(YEARS)].index)\nd3 = d2[~d2[\"iso3\"].isin(short)]\nstep(\"3_drop_countries_missing_year_rows\", \"drop countries without a row for every year 2000..2022\",\n d2, d3, f\"Not in balanced panel (no imputation allowed): {', '.join(short)}\")\nfor iso in short:\n yy = d2[d2.iso3 == iso][\"year\"]\n issues.append({\"issue\": f\"{iso} has rows only for {yy.min()}-{yy.max()} ({yy.nunique()} of 23 years)\",\n \"handling\": \"Excluded from the balanced set; no imputation. Included in the 2022 all-available baseline\"})\n\n# --- Step 4: drop countries with an empty value in any year ---\nnmiss = d3.groupby(\"iso3\")[\"life_expectancy\"].apply(lambda s: s.isna().sum())\nwithna = sorted(nmiss[nmiss > 0].index)\nd4 = d3[~d3[\"iso3\"].isin(withna)]\nstep(\"4_drop_countries_with_empty_value\", \"drop countries with an empty life_expectancy cell in any year\",\n d3, d4, f\"Missing country-year => not balanced (no interpolation): {', '.join(withna)}\")\nfor iso in withna:\n yy = d3[(d3.iso3 == iso) & d3.life_expectancy.isna()][\"year\"].tolist()\n issues.append({\"issue\": f\"{iso} has an empty life_expectancy cell for year(s) {yy}\",\n \"handling\": \"Treated as missing (no interpolation); country excluded from the balanced set\"})\n\nanalytic = d4[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]].sort_values([\"iso3\", \"year\"]).reset_index(drop=True)\nbalanced = sorted(analytic[\"iso3\"].unique())\nassert analytic.groupby(\"iso3\")[\"year\"].nunique().eq(len(YEARS)).all()\nassert analytic[\"life_expectancy\"].notna().all()\nassert len(analytic) == len(balanced) * len(YEARS)\n\nym = analytic.groupby(\"year\")[\"life_expectancy\"].mean()\nm2000, m2022 = float(ym.loc[2000]), float(ym.loc[2022])\n\n# baseline: all distinct countries with a recorded 2022 value (after dedupe, each counted once)\nb22 = d2[(d2.year == 2022) & d2.life_expectancy.notna()]\nassert b22[\"iso3\"].is_unique\nbase2022 = float(b22[\"life_expectancy\"].mean())\n\nn_total = int(d2[\"iso3\"].nunique())\nres = {\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": n_total,\n \"n_countries_balanced\": len(balanced),\n \"n_countries_incomplete\": n_total - len(balanced),\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(base2022, 6),\n \"n_countries_2022_all_available\": int(b22[\"iso3\"].nunique()),\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"life_expectancy_panel.csv\": int(n_raw)},\n \"random_seed\": None,\n \"balanced_yearly_means\": {str(k): round(float(v), 6) for k, v in ym.items()},\n}\nanalytic.to_csv(f\"{OUT}/analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(f\"{OUT}/exclusions.csv\", index=False)\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2, ensure_ascii=False)\nprint(f\"balanced countries={len(balanced)}, n_analysed={len(analytic)}, \"\n f\"mean2000={m2000:.4f}, mean2022={m2022:.4f}, change={m2022-m2000:.4f}, baseline2022={base2022:.4f}\")\nstep,rule,n_before,n_after,n_removed,rationale\n1_year_scope,keep rows with year in 2000..2022,439,439,0,Task scope; all rows already in range\n2_dedupe_iso3_year,\"one row per (iso3, year); duplicates had identical values\",439,416,23,Country identified by iso3; each country counted at most once per year\n3_drop_countries_missing_year_rows,drop countries without a row for every year 2000..2022,416,368,48,\"Not in balanced panel (no imputation allowed): BGD, ETH, IND, NGA\"\n4_drop_countries_with_empty_value,drop countries with an empty life_expectancy cell in any year,368,345,23,Missing country-year => not balanced (no interpolation): CHE\n# 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.\ncountry_name,iso3,year,life_expectancy\nAustralia,AUS,2000,79.234\nAustralia,AUS,2001,79.634\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json","is_error":false}]},"uuid":"013f85d4-e2ed-47c3-9e83-236d5c943bc3","timestamp":"2026-10-30T04:44:37.852Z","toolUseResult":{"stdout":"\"\"\"F1-conf-02: balanced-panel mean life expectancy 2000-2022.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/life_expectancy_panel.csv; writes out/results.json, out/analytic.csv, out/exclusions.csv.\nNo randomness is used.\"\"\"\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={\"country_name\": str, \"iso3\": str})\nraw[\"iso3\"] = raw[\"iso3\"].str.strip()\nraw[\"country_name\"] = raw[\"country_name\"].str.strip()\nn_raw = len(raw)\nledger, issues = [], []\n\ndef step(name, rule, before, after, rationale):\n ledger.append(dict(step=name, rule=rule, n_before=len(before), n_after=len(after),\n n_removed=len(before) - len(after), rationale=rationale))\n\n# --- Step 1: restrict to 2000-2022 (scope check) ---\nd0 = raw\nd1 = d0[d0[\"year\"].isin(YEARS)].copy()\nstep(\"1_year_scope\", \"keep rows with year in 2000..2022\", d0, d1, \"Task scope; all rows already in range\")\n\n# --- Step 2: resolve duplicate (iso3, year) rows ---\ndup_mask = d1.duplicated([\"iso3\", \"year\"], keep=False)\ndups = d1[dup_mask]\nconflicts = []\nfor (iso, yr), g in dups.groupby([\"iso3\", \"year\"]):\n vals = g[\"life_expectancy\"].dropna().unique()\n if len(vals) > 1:\n conflicts.append((iso, yr, list(vals)))\nif len(conflicts):\n raise SystemExit(f\"Conflicting duplicate values, need a decision: {conflicts}\")\nnames_by_iso = d1.groupby(\"iso3\")[\"country_name\"].unique()\nmulti_name = {k: sorted(v) for k, v in names_by_iso.items() if len(v) > 1}\n# canonical name: current World Bank spelling for TUR; otherwise the unique name\ncanonical = {\"TUR\": \"Turkiye\"}\nd1[\"country_name\"] = [canonical.get(i, n) if i in multi_name else n\n for i, n in zip(d1[\"iso3\"], d1[\"country_name\"])]\nd2 = d1.sort_values([\"iso3\", \"year\"]).drop_duplicates([\"iso3\", \"year\"], keep=\"first\")\nstep(\"2_dedupe_iso3_year\", \"one row per (iso3, year); duplicates had identical values\",\n d1, d2, \"Country identified by iso3; each country counted at most once per year\")\nif len(dups):\n for iso, nm in multi_name.items():\n n_dup = int(dups[dups.iso3 == iso].shape[0] - dups[dups.iso3 == iso][[\"iso3\", \"year\"]].drop_duplicates().shape[0])\n issues.append({\"issue\": f\"{iso} appears under two country names {nm} (two extractions), giving {n_dup} duplicated (iso3, year) rows with identical life_expectancy values\",\n \"handling\": f\"Kept one row per (iso3, year) (values identical, so no conflict); country_name harmonised to '{canonical.get(iso)}'; country counted once\"})\n\n# --- Step 3: drop countries lacking a row for some year ---\nyrs_present = d2.groupby(\"iso3\")[\"year\"].nunique()\nshort = sorted(yrs_present[yrs_present < len(YEARS)].index)\nd3 = d2[~d2[\"iso3\"].isin(short)]\nstep(\"3_drop_countries_missing_year_rows\", \"drop countries without a row for every year 2000..2022\",\n d2, d3, f\"Not in balanced panel (no imputation allowed): {', '.join(short)}\")\nfor iso in short:\n yy = d2[d2.iso3 == iso][\"year\"]\n issues.append({\"issue\": f\"{iso} has rows only for {yy.min()}-{yy.max()} ({yy.nunique()} of 23 years)\",\n \"handling\": \"Excluded from the balanced set; no imputation. Included in the 2022 all-available baseline\"})\n\n# --- Step 4: drop countries with an empty value in any year ---\nnmiss = d3.groupby(\"iso3\")[\"life_expectancy\"].apply(lambda s: s.isna().sum())\nwithna = sorted(nmiss[nmiss > 0].index)\nd4 = d3[~d3[\"iso3\"].isin(withna)]\nstep(\"4_drop_countries_with_empty_value\", \"drop countries with an empty life_expectancy cell in any year\",\n d3, d4, f\"Missing country-year => not balanced (no interpolation): {', '.join(withna)}\")\nfor iso in withna:\n yy = d3[(d3.iso3 == iso) & d3.life_expectancy.isna()][\"year\"].tolist()\n issues.append({\"issue\": f\"{iso} has an empty life_expectancy cell for year(s) {yy}\",\n \"handling\": \"Treated as missing (no interpolation); country excluded from the balanced set\"})\n\nanalytic = d4[[\"iso3\", \"country_name\", \"year\", \"life_expectancy\"]].sort_values([\"iso3\", \"year\"]).reset_index(drop=True)\nbalanced = sorted(analytic[\"iso3\"].unique())\nassert analytic.groupby(\"iso3\")[\"year\"].nunique().eq(len(YEARS)).all()\nassert analytic[\"life_expectancy\"].notna().all()\nassert len(analytic) == len(balanced) * len(YEARS)\n\nym = analytic.groupby(\"year\")[\"life_expectancy\"].mean()\nm2000, m2022 = float(ym.loc[2000]), float(ym.loc[2022])\n\n# baseline: all distinct countries with a recorded 2022 value (after dedupe, each counted once)\nb22 = d2[(d2.year == 2022) & d2.life_expectancy.notna()]\nassert b22[\"iso3\"].is_unique\nbase2022 = float(b22[\"life_expectancy\"].mean())\n\nn_total = int(d2[\"iso3\"].nunique())\nres = {\n \"task_id\": \"F1-conf-02\",\n \"n_countries_total\": n_total,\n \"n_countries_balanced\": len(balanced),\n \"n_countries_incomplete\": n_total - len(balanced),\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(base2022, 6),\n \"n_countries_2022_all_available\": int(b22[\"iso3\"].nunique()),\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"life_expectancy_panel.csv\": int(n_raw)},\n \"random_seed\": None,\n \"balanced_yearly_means\": {str(k): round(float(v), 6) for k, v in ym.items()},\n}\nanalytic.to_csv(f\"{OUT}/analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(f\"{OUT}/exclusions.csv\", index=False)\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2, ensure_ascii=False)\nprint(f\"balanced countries={len(balanced)}, n_analysed={len(analytic)}, \"\n f\"mean2000={m2000:.4f}, mean2022={m2022:.4f}, change={m2022-m2000:.4f}, baseline2022={base2022:.4f}\")\nstep,rule,n_before,n_after,n_removed,rationale\n1_year_scope,keep rows with year in 2000..2022,439,439,0,Task scope; all rows already in range\n2_dedupe_iso3_year,\"one row per (iso3, year); duplicates had identical values\",439,416,23,Country identified by iso3; each country counted at most once per year\n3_drop_countries_missing_year_rows,drop countries without a row for every year 2000..2022,416,368,48,\"Not in balanced panel (no imputation allowed): BGD, ETH, IND, NGA\"\n4_drop_countries_with_empty_value,drop countries with an empty life_expectancy cell in any year,368,345,23,Missing country-year => not balanced (no interpolation): CHE\n# 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.\ncountry_name,iso3,year,life_expectancy\nAustralia,AUS,2000,79.234\nAustralia,AUS,2001,79.634\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"1f698296-6271-4a5d-b7e1-7f481b82cb01","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0147","sessionId":"ccb5348e-3642-4dc8-ba39-4943a3e00e1a","version":"2.1.314","gitBranch":"HEAD"}20{"type":"api-request-blob","sessionId":"ccb5348e-3642-4dc8-ba39-4943a3e00e1a","hash":"3cc0ffccbf53d186e3bbfd726fc616adf467c3c1f2473cef998de6a389900dcd","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01S4AzasSkQqja6raiuYtxWe","name":"Bash","input":{"command":"cat out/analysis.py; cat out/exclusions.csv; cat data/README.md; head -3 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-d); mkdir -p $S/data $S/out; cp data/* $S/data/; cp out/analysis.py $S/out/; cd $S && ~/wp29/venv/bin/python out/analysis.py && for f in results.json analytic.csv exclusions.csv; do cmp $f ~/wp29/runs/RC0147/out/$f 2>/dev/null; diff -q out/$f ~/wp29/runs/RC0147/out/$f && echo same $f; done\ncd ~/wp29/runs/RC0147; ~/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.iso3.value_counts().sort_index().to_dict())\nprint(d.groupby('iso3').country_name.unique().to_dict())\nprint(sorted(d.year.unique()))\nbad=[v for v in d.life_expectancy if v!='' and not v.replace('.','',1).isdigit()]; print('bad',bad)\nx=pd.to_numeric(d.life_expectancy,errors='coerce'); print(x.describe()); print(d[x.isna()])\nprint(d[[i!=i.strip() or i!=i.upper() for i in d.iso3]])\ndd=d[d.duplicated(['iso3','year'],keep=False)]; print(dd.groupby(['iso3','year']).life_expectancy.nunique().max(), len(dd))\nd['x']=x; print(d[d.year=='2022'].groupby('iso3').x.first().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":3786,"cache_read_input_tokens":7324,"output_tokens":662,"output_tokens_details":{"thinking_tokens":59},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":3786},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp3agXgrDobe82zaJ6E","requestRef":"e3828a1d-3310-46af-91ee-2a29a4848cec","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"caf24401-5eef-424b-a9be-b0214da03c27","timestamp":"2026-10-30T04:44:44.333Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0147","sessionId":"ccb5348e-3642-4dc8-ba39-4943a3e00e1a","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"caf24401-5eef-424b-a9be-b0214da03c27","isSidechain":false,"promptId":"aaa63407-251e-4f13-b6ba-51614096a7de","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0117UzfQCRD1x1c9v4VhSy29","type":"tool_result","content":"balanced countries=15, n_analysed=345, mean2000=78.0603, mean2022=81.5531, change=3.4928, baseline2022=78.6920\nsame results.json\nsame analytic.csv\nsame exclusions.csv\n(439, 4)\n{'AUS': 23, 'AUT': 23, 'BEL': 23, 'BGD': 12, 'CAN': 23, 'CHE': 23, 'DEU': 23, 'ESP': 23, 'ETH': 12, 'FRA': 23, 'GBR': 23, 'IND': 12, 'ITA': 23, 'JPN': 23, 'KOR': 23, 'NGA': 12, 'NLD': 23, 'SWE': 23, 'TUR': 46, 'USA': 23}\n{'AUS': array(['Australia'], dtype=object), 'AUT': array(['Austria'], dtype=object), 'BEL': array(['Belgium'], dtype=object), 'BGD': array(['Bangladesh'], dtype=object), 'CAN': array(['Canada'], dtype=object), 'CHE': array(['Switzerland'], dtype=object), 'DEU': array(['Germany'], dtype=object), 'ESP': array(['Spain'], dtype=object), 'ETH': array(['Ethiopia'], dtype=object), 'FRA': array(['France'], dtype=object), 'GBR': array(['United Kingdom'], dtype=object), 'IND': array(['India'], dtype=object), 'ITA': array(['Italy'], dtype=object), 'JPN': array(['Japan'], dtype=object), 'KOR': array(['Korea, Rep.'], dtype=object), 'NGA': array(['Nigeria'], dtype=object), 'NLD': array(['Netherlands'], dtype=object), 'SWE': array(['Sweden'], dtype=object), 'TUR': array(['Turkey', 'Turkiye'], dtype=object), 'USA': array(['United States'], dtype=object)}\n['2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021', '2022']\nbad []\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 \nEmpty DataFrame\nColumns: [country_name, iso3, year, life_expectancy]\nIndex: []\n1 46\niso3\nNGA 54.079\nETH 66.897\nIND 71.698\nBGD 74.265\nUSA 77.434\nTUR 77.591\nDEU 80.608\nGBR 81.011\nCAN 81.095\nAUT 81.295\nNLD 81.615\nBEL 81.749\nFRA 82.129\nKOR 82.680\nITA 82.700\nSWE 83.059\nESP 83.134\nAUS 83.200\nCHE 83.605\nJPN 83.996\nName: x, dtype: float64","is_error":false}]},"uuid":"b628bc73-32b3-48e2-aad6-0a461a001809","timestamp":"2026-10-30T04:44:44.743Z","toolUseResult":{"stdout":"balanced countries=15, n_analysed=345, mean2000=78.0603, mean2022=81.5531, change=3.4928, baseline2022=78.6920\nsame results.json\nsame analytic.csv\nsame exclusions.csv\n(439, 4)\n{'AUS': 23, 'AUT': 23, 'BEL': 23, 'BGD': 12, 'CAN': 23, 'CHE': 23, 'DEU': 23, 'ESP': 23, 'ETH': 12, 'FRA': 23, 'GBR': 23, 'IND': 12, 'ITA': 23, 'JPN': 23, 'KOR': 23, 'NGA': 12, 'NLD': 23, 'SWE': 23, 'TUR': 46, 'USA': 23}\n{'AUS': array(['Australia'], dtype=object), 'AUT': array(['Austria'], dtype=object), 'BEL': array(['Belgium'], dtype=object), 'BGD': array(['Bangladesh'], dtype=object), 'CAN': array(['Canada'], dtype=object), 'CHE': array(['Switzerland'], dtype=object), 'DEU': array(['Germany'], dtype=object), 'ESP': array(['Spain'], dtype=object), 'ETH': array(['Ethiopia'], dtype=object), 'FRA': array(['France'], dtype=object), 'GBR': array(['United Kingdom'], dtype=object), 'IND': array(['India'], dtype=object), 'ITA': array(['Italy'], dtype=object), 'JPN': array(['Japan'], dtype=object), 'KOR': array(['Korea, Rep.'], dtype=object), 'NGA': array(['Nigeria'], dtype=object), 'NLD': array(['Netherlands'], dtype=object), 'SWE': array(['Sweden'], dtype=object), 'TUR': array(['Turkey', 'Turkiye'], dtype=object), 'USA': array(['United States'], dtype=object)}\n['2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021', '2022']\nbad []\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 \nEmpty DataFrame\nColumns: [country_name, iso3, year, life_expectancy]\nIndex: []\n1 46\niso3\nNGA 54.079\nETH 66.897\nIND 71.698\nBGD 74.265\nUSA 77.434\nTUR 77.591\nDEU 80.608\nGBR 81.011\nCAN 81.095\nAUT 81.295\nNLD 81.615\nBEL 81.749\nFRA 82.129\nKOR 82.680\nITA 82.700\nSWE 83.059\nESP 83.134\nAUS 83.200\nCHE 83.605\nJPN 83.996\nName: x, dtype: 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-d); mkdir -p $S/data $S/out; cp data/* $S/data/; cp out/analysis.py $S/out/; cd $S && ~/wp29/venv/bin/python out/analysis.py && for f in results.json analytic.csv exclusions.csv; do cmp $f ~/wp29/runs/RC0147/out/$f 2>/dev/null; diff -q out/$f ~/wp29/runs/RC0147/out/$f && echo same $f; done\ncd ~/wp29/runs/RC0147; ~/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.iso3.value_counts().sort_index().to_dict())\nprint(d.groupby('iso3').country_name.unique().to_dict())\nprint(sorted(d.year.unique()))\nbad=[v for v in d.life_expectancy if v!='' and not v.replace('.','',1).isdigit()]; print('bad',bad)\nx=pd.to_numeric(d.life_expectancy,errors='coerce'); print(x.describe()); print(d[x.isna()])\nprint(d[[i!=i.strip() or i!=i.upper() for i in d.iso3]])\ndd=d[d.duplicated(['iso3','year'],keep=False)]; print(dd.groupby(['iso3','year']).life_expectancy.nunique().max(), len(dd))\nd['x']=x; print(d[d.year=='2022'].groupby('iso3').x.first().sort_values())\n\""}}]}}26{"type":"api-request","sessionId":"ccb5348e-3642-4dc8-ba39-4943a3e00e1a","id":"1b4b1dc0-5ddc-46fb-aef8-6731fba09900","timestamp":"2026-10-30T04:44:44.745Z","version":"2.1.314","querySource":"sdk","shapeHash":"75678d9264abf48a230f6905289901befc644d46f2fa6c898e63863aebba6654","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":"e3828a1d-3310-46af-91ee-2a29a4848cec","keep":6,"tail":["ca82c6b0c6e825dbc9ab2e48c131b90118b17bad48998202d2146f19bcee7f76","09dd21e2288025c56dcabd41671ab49d1bf4be67ea7d86f99549d85628fbdaff"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"788419e13a9db7ca4344c84cef2ac816809555fbff740035367fde0d9546b699"}27{"type":"last-prompt","lastPrompt":"Review","leafUuid":"b628bc73-32b3-48e2-aad6-0a461a001809","sessionId":"ccb5348e-3642-4dc8-ba39-4943a3e00e1a"}28{"type":"atis-latch","atis":"","sessionId":"ccb5348e-3642-4dc8-ba39-4943a3e00e1a"}29{"parentUuid":"b628bc73-32b3-48e2-aad6-0a461a001809","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp46RSn5FUmuVavs9Q2","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":1775,"cache_read_input_tokens":11110,"output_tokens":341,"output_tokens_details":{"thinking_tokens":108},"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":1775},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1109,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXp46A5U513avSfS9ToM","requestRef":"1b4b1dc0-5ddc-46fb-aef8-6731fba09900","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9e051abc-3494-42ea-a924-eb715f327c64","timestamp":"2026-10-30T04:44:47.277Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0147","sessionId":"ccb5348e-3642-4dc8-ba39-4943a3e00e1a","version":"2.1.314","gitBranch":"HEAD"}30{"parentUuid":"9e051abc-3494-42ea-a924-eb715f327c64","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp46RSn5FUmuVavs9Q2","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018BKnG2CorQVVQBgcrZ6zXQ","name":"Bash","input":{"command":"cd ~/wp29/runs/RC0147; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/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()>1.2])\nprint(d.groupby('iso3').life_expectancy.agg(['min','max']))\n\"; head -40 out/state/checks.md"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1775,"cache_read_input_tokens":11110,"output_tokens":341,"output_tokens_details":{"thinking_tokens":108},"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":1775},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp46A5U513avSfS9ToM","requestRef":"1b4b1dc0-5ddc-46fb-aef8-6731fba09900","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e995ef3e-b8ce-4fe5-82ee-560b8a9bc8f9","timestamp":"2026-10-30T04:44:48.821Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0147","sessionId":"ccb5348e-3642-4dc8-ba39-4943a3e00e1a","version":"2.1.314","gitBranch":"HEAD"}31{"parentUuid":"e995ef3e-b8ce-4fe5-82ee-560b8a9bc8f9","isSidechain":false,"promptId":"aaa63407-251e-4f13-b6ba-51614096a7de","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018BKnG2CorQVVQBgcrZ6zXQ","type":"tool_result","content":" country_name iso3 year life_expectancy dif\n78 Belgium BEL 2020 80.695 -1.300\n55 Bangladesh BGD 2020 71.421 -1.204\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\n194 Italy ITA 2020 82.195 -1.303\n367 Turkey TUR 2020 76.525 -1.212\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\n# Checks\n1. Rerun of out/analysis.py: results.json byte-identical (cmp) — PASS.\n2. Independent recomputation with csv module (no pandas): 20 countries, 15 balanced, mean2000 78.060267, mean2022 81.553067, change 3.4928, baseline 78.692 (20 countries) — PASS (matches).\n3. n_analysed = 15 × 23 = 345 = rows of analytic.csv; no duplicate (iso3,year); no missing values — PASS.\n4. Exclusion ledger: 439 → 439 → 416 → 368 → 345; n_before − n_removed = n_after each row, chained — PASS.\n5. n_countries_balanced + n_countries_incomplete = 15 + 5 = 20 = n_countries_total — PASS.\n6. change = mean_2022_balanced − mean_2000_balanced (81.553067 − 78.060267 = 3.4928) — PASS.\n7. Magnitude: baseline 2022 mean (78.692) lower than balanced 2022 (81.553) as expected since baseline adds lower-LE BGD/ETH/IND/NGA and CHE — PASS (plausible).\n8. Duplicate TUR rows have identical values (script aborts on conflict) — PASS.","is_error":false}]},"uuid":"5523f693-d5c0-4a09-b833-5c367d90fb50","timestamp":"2026-10-30T04:44:49.049Z","toolUseResult":{"stdout":" country_name iso3 year life_expectancy dif\n78 Belgium BEL 2020 80.695 -1.300\n55 Bangladesh BGD 2020 71.421 -1.204\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\n194 Italy ITA 2020 82.195 -1.303\n367 Turkey TUR 2020 76.525 -1.212\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\n# Checks\n1. Rerun of out/analysis.py: results.json byte-identical (cmp) — PASS.\n2. Independent recomputation with csv module (no pandas): 20 countries, 15 balanced, mean2000 78.060267, mean2022 81.553067, change 3.4928, baseline 78.692 (20 countries) — PASS (matches).\n3. n_analysed = 15 × 23 = 345 = rows of analytic.csv; no duplicate (iso3,year); no missing values — PASS.\n4. Exclusion ledger: 439 → 439 → 416 → 368 → 345; n_before − n_removed = n_after each row, chained — PASS.\n5. n_countries_balanced + n_countries_incomplete = 15 + 5 = 20 = n_countries_total — PASS.\n6. change = mean_2022_balanced − mean_2000_balanced (81.553067 − 78.060267 = 3.4928) — PASS.\n7. Magnitude: baseline 2022 mean (78.692) lower than balanced 2022 (81.553) as expected since baseline adds lower-LE BGD/ETH/IND/NGA and CHE — PASS (plausible).\n8. Duplicate TUR rows have identical values (script aborts on conflict) — PASS.","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e995ef3e-b8ce-4fe5-82ee-560b8a9bc8f9","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0147","sessionId":"ccb5348e-3642-4dc8-ba39-4943a3e00e1a","version":"2.1.314","gitBranch":"HEAD"}32{"type":"api-request-blob","sessionId":"ccb5348e-3642-4dc8-ba39-4943a3e00e1a","hash":"08ba271e237dcff94ce57c65532b3abc0625d2098e30b244f50679e106751903","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_018BKnG2CorQVVQBgcrZ6zXQ","name":"Bash","input":{"command":"cd ~/wp29/runs/RC0147; 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cat > out/review/review.json <<'EOF'\n{\n \"verdict\": \"accept\",\n \"issues\": [\n {\n \"id\": \"R1\",\n \"severity\": \"minor\",\n \"location\": \"out/report.md (Results) / claims.json C3\",\n \"issue\": \"The ~2.9-year gap between the 2022 baseline and the balanced mean is put down to BGD/ETH/IND/NGA alone. The baseline also includes CHE (83.605 in 2022), which pushes the baseline up and partly offsets that gap. The direction of the claim is right, but the wording is incomplete.\",\n \"suggested_fix\": \"Say that the baseline adds BGD, ETH, IND and NGA (which lower it) and CHE (which raises it).\"\n },\n {\n \"id\": \"R2\",\n \"severity\": \"minor\",\n \"location\": \"out/analysis.py (canonical name map)\",\n \"issue\": \"The canonical name 'Turkiye' is hardcoded for TUR. The data contain only 'Turkey' and 'Turkiye', so the choice is arbitrary but does not affect any numeric result. Any other iso3 with more than one name would get None from canonical.get in the data_issues text.\",\n \"suggested_fix\": \"Pick the name from the later or wider extraction by a rule, or say that the choice is cosmetic.\"\n },\n {\n \"id\": \"R3\",\n \"severity\": \"minor\",\n \"location\": \"out/report.md (Limitations)\",\n \"issue\": \"Income-group labels (e.g. 'TUR is the only middle-income member') come from outside knowledge, not from the input file.\",\n \"suggested_fix\": \"Mark these labels as contextual, or describe the set by the life-expectancy values in the data.\"\n }\n ],\n \"summary\": \"I re-ran analysis.py in a scratch copy and got byte-identical results.json, analytic.csv and exclusions.csv. I also checked the raw CSV independently. It has 439 rows and 20 iso3 codes. TUR appears 46 times under two names, and the 23 duplicate pairs have identical values. BGD, ETH, IND and NGA each have only 12 years (2011-2022). CHE has one empty cell (2007). There are no non-numeric values, no whitespace or case problems in the codes, and no years outside 2000-2022. This gives 15 balanced countries and 345 rows, and the exclusion ledger chains correctly (439→439→416→368→345). The baseline counts each of the 20 countries once. The code does no imputation and reads only the allowed input. Claims match the results, uncertainty is handled appropriately for an exact descriptive quantity, and the limitations are stated honestly. Only minor wording issues remain.\"\n}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1286,"cache_read_input_tokens":12885,"output_tokens":1180,"output_tokens_details":{"thinking_tokens":177},"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":1286},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp4QZbtUWUqBMkKNixF","requestRef":"e7c8966d-8199-4caf-9dba-3b606b5c7ca1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"04582573-972d-4d5d-a9e1-ff6b4f34ba79","timestamp":"2026-10-30T04:45:01.141Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0147","sessionId":"ccb5348e-3642-4dc8-ba39-4943a3e00e1a","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"04582573-972d-4d5d-a9e1-ff6b4f34ba79","isSidechain":false,"promptId":"aaa63407-251e-4f13-b6ba-51614096a7de","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HjTu97mXzjanhhLkNSDMoQ","type":"tool_result","content":"ok","is_error":false}]},"uuid":"261d399e-fcec-45ab-8490-7b09d4e00df2","timestamp":"2026-10-30T04:45:01.210Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"04582573-972d-4d5d-a9e1-ff6b4f34ba79","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0147","sessionId":"ccb5348e-3642-4dc8-ba39-4943a3e00e1a","version":"2.1.314","gitBranch":"HEAD"}37{"type":"api-request-blob","sessionId":"ccb5348e-3642-4dc8-ba39-4943a3e00e1a","hash":"53102185b1cdafecf33cc6d471e10375147eb8a31c5afb60da70e242ddd439e5","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01HjTu97mXzjanhhLkNSDMoQ","name":"Bash","input":{"command":"cd ~/wp29/runs/RC0147; cat > out/review/review.json <<'EOF'\n{\n \"verdict\": \"accept\",\n \"issues\": [\n {\n \"id\": \"R1\",\n \"severity\": \"minor\",\n \"location\": \"out/report.md (Results) / claims.json C3\",\n \"issue\": \"The ~2.9-year gap between the 2022 baseline and the balanced mean is put down to BGD/ETH/IND/NGA alone. The baseline also includes CHE (83.605 in 2022), which pushes the baseline up and partly offsets that gap. The direction of the claim is right, but the wording is incomplete.\",\n \"suggested_fix\": \"Say that the baseline adds BGD, ETH, IND and NGA (which lower it) and CHE (which raises it).\"\n },\n {\n \"id\": \"R2\",\n \"severity\": \"minor\",\n \"location\": \"out/analysis.py (canonical name map)\",\n \"issue\": \"The canonical name 'Turkiye' is hardcoded for TUR. The data contain only 'Turkey' and 'Turkiye', so the choice is arbitrary but does not affect any numeric result. Any other iso3 with more than one name would get None from canonical.get in the data_issues text.\",\n \"suggested_fix\": \"Pick the name from the later or wider extraction by a rule, or say that the choice is cosmetic.\"\n },\n {\n \"id\": \"R3\",\n \"severity\": \"minor\",\n \"location\": \"out/report.md (Limitations)\",\n \"issue\": \"Income-group labels (e.g. 'TUR is the only middle-income member') come from outside knowledge, not from the input file.\",\n \"suggested_fix\": \"Mark these labels as contextual, or describe the set by the life-expectancy values in the data.\"\n }\n ],\n \"summary\": \"I re-ran analysis.py in a scratch copy and got byte-identical results.json, analytic.csv and exclusions.csv. I also checked the raw CSV independently. It has 439 rows and 20 iso3 codes. TUR appears 46 times under two names, and the 23 duplicate pairs have identical values. BGD, ETH, IND and NGA each have only 12 years (2011-2022). CHE has one empty cell (2007). There are no non-numeric values, no whitespace or case problems in the codes, and no years outside 2000-2022. This gives 15 balanced countries and 345 rows, and the exclusion ledger chains correctly (439→439→416→368→345). The baseline counts each of the 20 countries once. The code does no imputation and reads only the allowed input. Claims match the results, uncertainty is handled appropriately for an exact descriptive quantity, and the limitations are stated honestly. Only minor wording issues remain.\"\n}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok"}}]}}38{"type":"api-request","sessionId":"ccb5348e-3642-4dc8-ba39-4943a3e00e1a","id":"cea2c985-b558-4bb7-878b-b855e26a983f","timestamp":"2026-10-30T04:45:01.213Z","version":"2.1.314","querySource":"sdk","shapeHash":"75678d9264abf48a230f6905289901befc644d46f2fa6c898e63863aebba6654","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":"e7c8966d-8199-4caf-9dba-3b606b5c7ca1","keep":10,"tail":["53102185b1cdafecf33cc6d471e10375147eb8a31c5afb60da70e242ddd439e5","59dadc96812787215ed69c005386eae0fab738a6bac9f3c2c631740cf93e17ff"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"8261c30a94159aeddb2b756c6eca978daa3138318cc12d22fc648daed2544a41"}39{"parentUuid":"261d399e-fcec-45ab-8490-7b09d4e00df2","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp5JuKBEJxFCD6QMgKj","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":1187,"cache_read_input_tokens":14171,"output_tokens":793,"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":1187},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6456,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXp5JaxSLNTvgYUPNDHt","requestRef":"cea2c985-b558-4bb7-878b-b855e26a983f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"88b6d2d4-9026-4726-921a-d415527df08c","timestamp":"2026-10-30T04:45:09.334Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0147","sessionId":"ccb5348e-3642-4dc8-ba39-4943a3e00e1a","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"88b6d2d4-9026-4726-921a-d415527df08c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp5JuKBEJxFCD6QMgKj","type":"message","role":"assistant","content":[{"type":"text","text":"I recommend **accepting** this submission. I found no critical or major problems, only three minor wording points. The review is saved to `out/review/review.json`.\n\n**Results reproduce exactly.** I re-ran `out/analysis.py` in a scratch copy and it produced byte-identical `results.json`, `analytic.csv` and `exclusions.csv`.\n\n**My own check of the raw data matches theirs:**\n- The file has 439 rows and 20 countries.\n- Turkey appears twice under two names (\"Turkey\" and \"Turkiye\"), giving 23 duplicate country-years. The values are identical in every pair, so removing the duplicates doesn't change any number.\n- Bangladesh, Ethiopia, India and Nigeria have data for only 12 of the 23 years (2011–2022).\n- Switzerland has one empty value, for 2007.\n- There are no other problems: no non-numeric values, no stray spaces or case differences in the country codes, and no years outside 2000–2022.\n\n**The numbers and rules check out:**\n- 15 countries have a value in every year, giving 345 country-years.\n- Every row of the exclusion ledger adds up, and each step starts where the previous one ended (439 → 439 → 416 → 368 → 345).\n- The balanced mean is 78.060 years in 2000 and 81.553 in 2022, a rise of 3.493 years.\n- The 2022 comparison mean over all 20 countries is 78.692, with each country counted once.\n- The code uses only the one allowed input file and does not fill in any missing values.\n- The claims match the results. Reporting no confidence interval is reasonable, since these are exact averages for a fixed set of countries, and the limitations are stated honestly.\n\n**Minor points:**\n1. The report explains the 2.9-year gap between the 2022 balanced mean and the all-countries mean as coming from the four lower-income countries alone. But Switzerland is also in the all-countries mean and pulls it up (83.605 in 2022). The direction of the claim is still right; only the explanation is incomplete.\n2. The code hardcodes \"Turkiye\" as Turkey's name. This doesn't affect any number. If another country ever had two names, the data-issues text would print \"None\" for its harmonised name.\n3. The income-group labels in the limitations section (for example, Turkey as the only middle-income country in the set) come from outside knowledge, not from the data file."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1187,"cache_read_input_tokens":14171,"output_tokens":793,"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":1187},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp5JaxSLNTvgYUPNDHt","requestRef":"cea2c985-b558-4bb7-878b-b855e26a983f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"33942b0b-2d3e-4bd3-94a2-ca659ff7f39e","timestamp":"2026-10-30T04:45:09.918Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0147","sessionId":"ccb5348e-3642-4dc8-ba39-4943a3e00e1a","version":"2.1.314","gitBranch":"HEAD"}41{"type":"last-prompt","lastPrompt":"Review","leafUuid":"33942b0b-2d3e-4bd3-94a2-ca659ff7f39e","sessionId":"ccb5348e-3642-4dc8-ba39-4943a3e00e1a"}42{"type":"cost-state","sessionId":"ccb5348e-3642-4dc8-ba39-4943a3e00e1a","totalCostUSD":0.14236880000000002,"totalAPIDuration":35788,"totalAPIDurationWithoutRetries":35785,"totalToolDuration":739,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":36652,"startTime":1793335473294,"modelUsage":{"claude-opus-5-5":{"inputTokens":14,"outputTokens":3160,"thinkingTokens":349,"cacheReadInputTokens":49214,"cacheCreationInputTokens":13854,"cacheCreationEphemeral5mInputTokens":13854,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.14236880000000002}},"hasUnknownModelCost":false}43