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Code)","event":"backend_identity","model":"sonnet","segment":9},{"at":"2026-10-14T23:12:35Z","backend":"claude_code","cache_read_tokens":117593,"cache_write_tokens":106662,"cost_usd":"0","event":"attempt_usage","input_tokens":4,"model":"sonnet","output_tokens":18727,"peak_prompt_tokens":0,"segment":9,"stop_reason":"end_turn","success":true,"summarised_exchanges":0,"tool_calls":1,"turns":0},{"at":"2026-10-14T23:13:28Z","backend":"claude_code","cache_read_tokens":125074,"cache_write_tokens":120093,"cost_usd":"0","event":"attempt_usage","input_tokens":4,"model":"sonnet","output_tokens":24698,"peak_prompt_tokens":0,"segment":9,"stop_reason":"end_turn","success":true,"summarised_exchanges":0,"tool_calls":1,"turns":0},{"at":"2026-10-14T23:13:41Z","backend":"claude_code","cache_read_tokens":120736,"cache_write_tokens":118331,"cost_usd":"0","event":"attempt_usage","input_tokens":4,"model":"sonnet","output_tokens":27297,"peak_prompt_tokens":0,"segment":9,"stop_reason":"end_turn","success":true,"summarised_exchanges":0,"tool_calls":1,"turns":0},{"at":"2026-10-14T23:15:41Z","backend":"claude_code","cache_read_tokens":123636,"cache_write_tokens":116497,"cost_usd":"0","event":"attempt_usage","input_tokens":4,"model":"sonnet","output_tokens":22559,"peak_prompt_tokens":0,"segment":9,"stop_reason":"end_turn","success":true,"summarised_exchanges":0,"tool_calls":1,"turns":0},{"at":"2026-10-14T23:16:15Z","backend":"claude_code","cache_read_tokens":123549,"cache_write_tokens":114393,"cost_usd":"0","event":"attempt_usage","input_tokens":4,"model":"sonnet","output_tokens":20541,"peak_prompt_tokens":0,"segment":9,"stop_reason":"end_turn","success":true,"summarised_exchanges":0,"tool_calls":1,"turns":0},{"at":"2026-10-14T23:18:04Z","backend":"claude_code","cache_read_tokens":460946,"cache_write_tokens":93541,"cost_usd":"0","event":"attempt_usage","input_tokens":14,"model":"sonnet","output_tokens":15273,"peak_prompt_tokens":0,"segment":9,"stop_reason":"end_turn","success":true,"summarised_exchanges":0,"tool_calls":9,"turns":0},{"at":"2026-10-14T23:20:22Z","backend":"claude_code","cache_read_tokens":543463,"cache_write_tokens":86940,"cost_usd":"0","event":"attempt_usage","input_tokens":16,"model":"sonnet","output_tokens":25083,"peak_prompt_tokens":0,"segment":9,"stop_reason":"end_turn","success":true,"summarised_exchanges":0,"tool_calls":9,"turns":0}],"exported":"20261015","governance":"full","internal_review":{"combined":"weighted average","reviewers":{"data_reviewer":5.5,"methods_reviewer":5,"plausibility_reviewer":5.5,"technical_reviewer":6,"writing_reviewer":6.5},"rounds":1,"scale":10,"score":5.59,"weights":{"data_reviewer":1.25,"methods_reviewer":1.5,"plausibility_reviewer":1,"technical_reviewer":1,"writing_reviewer":0.75}},"mode":"single_pass","models":[{"backend":"claude_code","model":"sonnet"}],"outcome":{"at":"2026-10-14T23:20:22Z","status":"completed"},"template":"text-analysis"},"schema":"e2er-dossier/0.6","study":{"title":"How did the vocabulary of weather (rain, wind, storm, fog, snow, sun, cloud a..."},"workflow":[{"accepted":true,"backend":"claude_code","ended":"2026-10-14T17:58:43Z","model":"sonnet","output":{"file":"design/literature_review.md","sha256":"28bcc981f0ec7bad5ec2bd4b6454a57c9c1abede65983cd5260cb6600c584fce"},"phase":"initial","segment":0,"specialist":"literature_scanner","started":"2026-10-14T17:57:41Z","type":"specialist"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T17:58:59Z","model":"sonnet","output":{"file":"design/paper_plan.md","sha256":"1b78108d4f2499e03c3c1c4d6d02e5176ff3e4688049a8ee8435a054f6fcf1bf"},"phase":"initial","segment":0,"specialist":"idea_developer","started":"2026-10-14T17:57:41Z","type":"specialist"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T18:00:17Z","model":"sonnet","output":{"file":"data/data_dictionary.json","sha256":"5d24cbe39b37f6d1600997c14508a8236aa0f5a672a1e6e3aed3d58ddd49e508"},"outputs":[{"file":"data/data_dictionary.json","sha256":"5d24cbe39b37f6d1600997c14508a8236aa0f5a672a1e6e3aed3d58ddd49e508"},{"file":"design/corpus_design.json","sha256":"973a41331bfab0168fe02c38cfc969e5254668238f7d13c410bdbd11d247f9f5"}],"phase":"initial","segment":0,"specialist":"data_architect","started":"2026-10-14T17:58:59Z","type":"specialist"},{"at":"2026-10-14T18:00:17Z","check":"corpus_design","detail":"(d) preprocessing.lemmatisation must be \"none\" or {\"file\": \"<word,lemma table>\", \"source\": ...}; e2er applies the table, so a lemmatiser's output is recorded as one","enforced":true,"passed":false,"phase":"initial","segment":0,"type":"check"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T18:00:31Z","model":"sonnet","output":{"file":"data/data_dictionary.json","sha256":"5d24cbe39b37f6d1600997c14508a8236aa0f5a672a1e6e3aed3d58ddd49e508","unchanged":true},"outputs":[{"file":"data/data_dictionary.json","sha256":"5d24cbe39b37f6d1600997c14508a8236aa0f5a672a1e6e3aed3d58ddd49e508","unchanged":true},{"file":"design/corpus_design.json","sha256":"01b091abf4262e8bcfbc5e85293d123902827fb659b91071591bb5f0ef622a74"}],"phase":"initial","segment":0,"specialist":"data_architect","started":"2026-10-14T18:00:17Z","type":"specialist"},{"at":"2026-10-14T18:00:31Z","check":"corpus_design","enforced":true,"passed":true,"phase":"initial","segment":0,"type":"check"},{"action":"send_back","at":"2026-10-14T18:01:10Z","phase":"initial","remark":"Corpus design fixes (corpus_design.json and data_dictionary.json): (1) Exact word forms only, no wildcards at all: replace raindrop*, hailstone*, thunderstorm*, hurricane*, snowflake*, snowdrift*, icicle*, sunbeam* by their singular and plural forms (raindrop, raindrops, ...). (2) Drop 'hail' (greeting and 'within hail' outnumber weather hail in novels) and 'storming' (storming a fort); keep shower(s), thunder*, icy, cloud forms but list them as ambiguous in the validation note so the hand-checked sample covers them. (3) Drop pub_year entirely: no publication year is looked up or used, not even as a robustness check; the time axis is the author's birth year as the Gutenberg catalogue records it. (4) Translations: exclude books with a translator in the catalogue record and say so. (5) State in the limits that the frame is the 320 most popular catalogue rows per cohort search (popularity-sorted), and that word counts are known only after the texts are loaded, so the 30,000-token rule is applied after loading and may shrink a cohort below 20 (then all cohorts are cut to the smallest n, as planned). Keep about 120 novels, at most two per author, the seed and everything else.","segment":0,"step":"review_design","target":"data_architect","type":"researcher"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T18:02:33Z","model":"sonnet","output":{"file":"data/data_dictionary.json","sha256":"13b63cf1903e425f595cc890a9c8567e67f6359eac4bda5242d3dc1a04573e42"},"outputs":[{"file":"data/data_dictionary.json","sha256":"13b63cf1903e425f595cc890a9c8567e67f6359eac4bda5242d3dc1a04573e42"},{"file":"design/corpus_design.json","sha256":"b7c37b281253674f1c0ee895d3f8111d86763b38594297031cfbe97d8d11b011"}],"phase":"initial","segment":1,"sent_back":{"at":"2026-10-14T18:01:10Z","remark":"Corpus design fixes (corpus_design.json and data_dictionary.json): (1) Exact word forms only, no wildcards at all: replace raindrop*, hailstone*, thunderstorm*, hurricane*, snowflake*, snowdrift*, icicle*, sunbeam* by their singular and plural forms (raindrop, raindrops, ...). (2) Drop 'hail' (greeting and 'within hail' outnumber weather hail in novels) and 'storming' (storming a fort); keep shower(s), thunder*, icy, cloud forms but list them as ambiguous in the validation note so the hand-checked sample covers them. (3) Drop pub_year entirely: no publication year is looked up or used, not even as a robustness check; the time axis is the author's birth year as the Gutenberg catalogue records it. (4) Translations: exclude books with a translator in the catalogue record and say so. (5) State in the limits that the frame is the 320 most popular catalogue rows per cohort search (popularity-sorted), and that word counts are known only after the texts are loaded, so the 30,000-token rule is applied after loading and may shrink a cohort below 20 (then all cohorts are cut to the smallest n, as planned). Keep about 120 novels, at most two per author, the seed and everything else."},"specialist":"data_architect","started":"2026-10-14T18:01:10Z","type":"specialist"},{"at":"2026-10-14T18:02:33Z","check":"corpus_design","enforced":true,"passed":true,"phase":"initial","segment":1,"type":"check"},{"action":"instruction","at":"2026-10-14T18:02:44Z","phase":"initial","segment":1,"step":"review_design","text":"The corpus design (corpus_design.json, data_dictionary.json) replaces paper_plan.md where they differ: no publication years at all - neither looked up for a subsample nor approximated as birth year + k; the only time axis is the author's birth year as the Gutenberg catalogue records it, and the paper says that cohorts overlap in publication years and that period and cohort cannot be separated. No gender or nationality coding. Exact word forms as listed, no wildcards.","type":"researcher"},{"action":"approve","at":"2026-10-14T18:02:44Z","phase":"initial","segment":1,"step":"review_design","type":"researcher"},{"at":"2026-10-14T18:12:37Z","check":"contracts","detail":"summary_statistics.json: empty JSON ('{}'); data.db: data_dictionary.json declares tables that were not loaded — missing from data.db: gutenberg_texts_c1, gutenberg_texts_c2, gutenberg_texts_c3, gutenberg_texts_c4, gutenberg_texts_c5, gutenberg_texts_c6; loaded without values: gutenberg_books_c1.copyright: 0 of 320 non-null (needs 50%); gutenberg_books_c1.download_count: 0 of 320 non-null (needs 50%); gutenberg_books_c2.copyright: 0 of 320 non-null (needs 50%); gutenberg_books_c2.download_count: 0 of 320 non-null (needs 50%); gutenberg_books_c3.copyright: 0 of 320 non-null (needs 50%); gutenberg_books_c3.download_count: 0 of 320 non-null (needs 50%); gutenberg_books_c4.copyright: 0 of 320 non-null (needs 50%); gutenberg_books_c4.download_count: 0 of 320 non-null (needs 50%); gutenberg_books_c5.copyright: 0 of 320 non-null (needs 50%); gutenberg_books_c5.download_count: 0 of 320 non-null (needs 50%); gutenberg_books_c6.copyright: 0 of 320 non-null (needs 50%); gutenberg_books_c6.download_count: 0 of 320 non-null (needs 50%). Tables in data.db: gutenberg_books_c1 (320 rows), gutenberg_books_c2 (320 rows), gutenberg_books_c3 (320 rows), gutenberg_books_c4 (320 rows), gutenberg_books_c5 (320 rows), gutenberg_books_c6 (320 rows). Load each series with `e2er-data ... --table <name>`; a table whose values are NULL was not loaded (check the connector's error, e.g. an API key).","enforced":true,"passed":false,"phase":"initial","segment":2,"type":"check"},{"accepted":false,"backend":"claude_code","ended":"2026-10-14T18:12:37Z","model":"sonnet","output":{"file":"data/data_summary.md","sha256":"eb3c7ff241df966b35c82099930888574d65da8187db9f8c9174645c93f23b20"},"outputs":[{"file":"data/data_summary.md","sha256":"eb3c7ff241df966b35c82099930888574d65da8187db9f8c9174645c93f23b20"},{"file":"results/summary_statistics.json","sha256":"ca3d163bab055381827226140568f3bef7eaac187cebd76878e0b63e9e442356"},{"file":"results/figure_spec.json","sha256":"ca3d163bab055381827226140568f3bef7eaac187cebd76878e0b63e9e442356"},{"file":"data/corpus_manifest.json","sha256":"ca3d163bab055381827226140568f3bef7eaac187cebd76878e0b63e9e442356"}],"phase":"initial","segment":2,"specialist":"data_analyst","started":"2026-10-14T18:02:44Z","stopped_by":"contract violation: summary_statistics.json: empty JSON ('{}'); data.db: data_dictionary.json declares tables that were not loaded — missing from data.db: gutenberg_texts_c1, gutenberg_texts_c2, gutenberg_texts_c3, gutenberg_texts_c4, gutenberg_texts_c5, gutenberg_texts_c6; loaded without values: gutenberg_books_c1.copyright: 0 of 320 non-null (needs 50%); gutenberg_books_c1.download_count: 0 of 320 non-null (needs 50%); gutenberg_books_c2.copyright: 0 of 320 non-null (needs 50%); gutenberg_books_c2.download_count: 0 of 320 non-null (needs 50%); gutenberg_books_c3.copyright: 0 of 320 non-null (needs 50%); gutenberg_books_c3.download_count: 0 of 320 non-null (needs 50%); gutenberg_books_c4.copyright: 0 of 320 non-null (needs 50%); gutenberg_books_c4.download_count: 0 of 320 non-null (needs 50%); gutenberg_books_c5.copyright: 0 of 320 non-null (needs 50%); gutenberg_books_c5.download_count: 0 of 320 non-null (needs 50%); gutenberg_books_c6.copyright: 0 of 320 non-null (needs 50%); gutenberg_books_c6.download_count: 0 of 320 non-null (needs 50%). Tables in data.db: gutenberg_books_c1 (320 rows), gutenberg_books_c2 (320 rows), gutenberg_books_c3 (320 rows), gutenberg_books_c4 (320 rows), gutenberg_books_c5 (320 rows), gutenberg_books_c6 (320 rows). Load each series with `e2er-data ... --table <name>`; a table whose values are NULL was not loaded (check the connector's error, e.g. an API key).","type":"specialist"},{"at":"2026-10-14T18:13:37Z","check":"contracts","detail":"data.db: data_dictionary.json declares tables that were not loaded — missing from data.db: gutenberg_texts_c1, gutenberg_texts_c2, gutenberg_texts_c3, gutenberg_texts_c4, gutenberg_texts_c5, gutenberg_texts_c6. Tables in data.db: gutenberg_books_c1 (320 rows), gutenberg_books_c2 (320 rows), gutenberg_books_c3 (320 rows), gutenberg_books_c4 (320 rows), gutenberg_books_c5 (320 rows), gutenberg_books_c6 (320 rows). Load each series with `e2er-data ... --table <name>`; a table whose values are NULL was not loaded (check the connector's error, e.g. an API key).","enforced":true,"passed":false,"phase":"initial","segment":2,"type":"check"},{"accepted":false,"backend":"claude_code","ended":"2026-10-14T18:13:37Z","model":"sonnet","output":{"file":"data/data_summary.md","sha256":"e58c6bfe6827b00d26310043f1e3a7c32c2c700a27754e3d8968ecb1e1b4c90f"},"outputs":[{"file":"data/data_summary.md","sha256":"e58c6bfe6827b00d26310043f1e3a7c32c2c700a27754e3d8968ecb1e1b4c90f"},{"file":"results/summary_statistics.json","sha256":"0eaa5e278e189c74ac20ce4e0487d4cb21d2926fc601069ca35c729ddd252061"},{"file":"results/figure_spec.json","sha256":"ca3d163bab055381827226140568f3bef7eaac187cebd76878e0b63e9e442356","unchanged":true},{"file":"data/corpus_manifest.json","sha256":"ca3d163bab055381827226140568f3bef7eaac187cebd76878e0b63e9e442356","unchanged":true}],"phase":"initial","segment":2,"specialist":"data_analyst","started":"2026-10-14T18:12:37Z","stopped_by":"contract violation: data.db: data_dictionary.json declares tables that were not loaded — missing from data.db: gutenberg_texts_c1, gutenberg_texts_c2, gutenberg_texts_c3, gutenberg_texts_c4, gutenberg_texts_c5, gutenberg_texts_c6. Tables in data.db: gutenberg_books_c1 (320 rows), gutenberg_books_c2 (320 rows), gutenberg_books_c3 (320 rows), gutenberg_books_c4 (320 rows), gutenberg_books_c5 (320 rows), gutenberg_books_c6 (320 rows). Load each series with `e2er-data ... --table <name>`; a table whose values are NULL was not loaded (check the connector's error, e.g. an API key).","type":"specialist"},{"accepted":false,"backend":"claude_code","ended":"2026-10-14T18:43:37Z","model":"sonnet","output":{"file":"data/data_summary.md","sha256":"e58c6bfe6827b00d26310043f1e3a7c32c2c700a27754e3d8968ecb1e1b4c90f","unchanged":true},"outputs":[{"file":"data/data_summary.md","sha256":"e58c6bfe6827b00d26310043f1e3a7c32c2c700a27754e3d8968ecb1e1b4c90f","unchanged":true},{"file":"results/summary_statistics.json","sha256":"0eaa5e278e189c74ac20ce4e0487d4cb21d2926fc601069ca35c729ddd252061","unchanged":true},{"file":"results/figure_spec.json","sha256":"ca3d163bab055381827226140568f3bef7eaac187cebd76878e0b63e9e442356","unchanged":true},{"file":"data/corpus_manifest.json","sha256":"ca3d163bab055381827226140568f3bef7eaac187cebd76878e0b63e9e442356","unchanged":true}],"phase":"initial","segment":2,"specialist":"data_analyst","started":"2026-10-14T18:13:37Z","stopped_by":"Claude Code timed out after 1800s (limit 1800s)","type":"specialist"},{"at":"2026-10-14T18:55:25Z","check":"contracts","detail":"data_summary.md: data_summary.md must name each loaded table with its actual row count from data.db; missing or wrong for: gutenberg_books_c2 (actual 320 rows), gutenberg_books_c3 (actual 320 rows), gutenberg_books_c4 (actual 320 rows), gutenberg_books_c5 (actual 320 rows), gutenberg_texts_c2 (actual 19 rows), gutenberg_texts_c3 (actual 20 rows), gutenberg_texts_c4 (actual 20 rows), gutenberg_texts_c5 (actual 20 rows), gutenberg_texts_c6 (actual 20 rows)","enforced":true,"passed":false,"phase":"initial","segment":3,"type":"check"},{"accepted":false,"backend":"claude_code","ended":"2026-10-14T18:55:25Z","model":"sonnet","output":{"file":"data/data_summary.md","sha256":"f021d699a09fbf36ba057aa97c393a03c4b4415b024b058c07e8bc1dcc53bb05"},"outputs":[{"file":"data/data_summary.md","sha256":"f021d699a09fbf36ba057aa97c393a03c4b4415b024b058c07e8bc1dcc53bb05"},{"file":"results/summary_statistics.json","sha256":"3b1abb1dc5092bfe2abcbad60138c17fa9be10dddf097ad1c9ee6d6bc222d98f"},{"file":"results/figure_spec.json","sha256":"7a6f66284280d34ad3bd9d7133341838f71f61fbf1deecc72f0a8e7d770db45b"},{"file":"data/corpus_manifest.json","sha256":"08e5f808c2b437e3daa96c710dd252e7f5a00fcb7a68d32abeefe859c9b76f42"}],"phase":"initial","segment":3,"specialist":"data_analyst","started":"2026-10-14T18:43:51Z","stopped_by":"contract violation: data_summary.md: data_summary.md must name each loaded table with its actual row count from data.db; missing or wrong for: gutenberg_books_c2 (actual 320 rows), gutenberg_books_c3 (actual 320 rows), gutenberg_books_c4 (actual 320 rows), gutenberg_books_c5 (actual 320 rows), gutenberg_texts_c2 (actual 19 rows), gutenberg_texts_c3 (actual 20 rows), gutenberg_texts_c4 (actual 20 rows), gutenberg_texts_c5 (actual 20 rows), gutenberg_texts_c6 (actual 20 rows)","type":"specialist"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T18:55:56Z","model":"sonnet","output":{"file":"data/data_summary.md","sha256":"d0b619e6cbebfd904a565f251edfd517dc367502500038365d8e8b3ac764f859"},"outputs":[{"file":"data/data_summary.md","sha256":"d0b619e6cbebfd904a565f251edfd517dc367502500038365d8e8b3ac764f859"},{"file":"results/summary_statistics.json","sha256":"3b1abb1dc5092bfe2abcbad60138c17fa9be10dddf097ad1c9ee6d6bc222d98f","unchanged":true},{"file":"results/figure_spec.json","sha256":"7a6f66284280d34ad3bd9d7133341838f71f61fbf1deecc72f0a8e7d770db45b","unchanged":true},{"file":"data/corpus_manifest.json","sha256":"08e5f808c2b437e3daa96c710dd252e7f5a00fcb7a68d32abeefe859c9b76f42","unchanged":true}],"phase":"initial","segment":3,"specialist":"data_analyst","started":"2026-10-14T18:55:25Z","type":"specialist"},{"at":"2026-10-14T18:55:59Z","check":"corpus","enforced":true,"passed":true,"phase":"initial","segment":3,"type":"check"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T18:58:25Z","model":"sonnet","output":{"file":"design/econometric_spec.md","sha256":"18c0a21914e09d3fbd2bea0e2174e01108bb8adaeded073c6a87860449cbd119"},"outputs":[{"file":"design/econometric_spec.md","sha256":"18c0a21914e09d3fbd2bea0e2174e01108bb8adaeded073c6a87860449cbd119"},{"file":"results/estimation_results.json","sha256":"ee5e0b73d10ef7916d5ba4cd687def454ca1799aaffc8e80f1bf75c98a04e448"}],"phase":"initial","segment":3,"specialist":"econometrics_specialist","started":"2026-10-14T18:55:59Z","type":"specialist"},{"at":"2026-10-14T18:58:29Z","check":"text_results","detail":"(v) no figure shows models.keyness_c1 (key terms): add one to figure_spec.json with \"source\": {\"results\": \"models.keyness_c1.outputs...\", \"fields\": {...}}, and name it in the results' 'figures'; (v) no figure shows models.keyness_weather_c1 (key terms): add one to figure_spec.json with \"source\": {\"results\": \"models.keyness_weather_c1.outputs...\", \"fields\": {...}}, and name it in the results' 'figures'; (v) no figure shows models.keyness_c2 (key terms): add one to figure_spec.json with \"source\": {\"results\": \"models.keyness_c2.outputs...\", \"fields\": {...}}, and name it in the results' 'figures'; (v) no figure shows models.keyness_weather_c2 (key terms): add one to figure_spec.json with \"source\": {\"results\": \"models.keyness_weather_c2.outputs...\", \"fields\": {...}}, and name it in the results' 'figures'; (v) no figure shows models.keyness_c3 (key terms): add one to figure_spec.json with \"source\": {\"results\": \"models.keyness_c3.outputs...\", \"fields\": {...}}, and name it in the results' 'figures'; (v) no figure shows models.keyness_weather_c3 (key terms): add one to figure_spec.json with \"source\": {\"results\": \"models.keyness_weather_c3.outputs...\", \"fields\": {...}}, and name it in the results' 'figures'; (v) no figure shows models.keyness_c4 (key terms): add one to figure_spec.json with \"source\": {\"results\": \"models.keyness_c4.outputs...\", \"fields\": {...}}, and name it in the results' 'figures'; (v) no figure shows models.keyness_weather_c4 (key terms): add one to figure_spec.json with \"source\": {\"results\": \"models.keyness_weather_c4.outputs...\", \"fields\": {...}}, and name it in the results' 'figures'; (v) no figure shows models.keyness_c5 (key terms): add one to figure_spec.json with \"source\": {\"results\": \"models.keyness_c5.outputs...\", \"fields\": {...}}, and name it in the results' 'figures'; (v) no figure shows models.keyness_weather_c5 (key terms): add one to figure_spec.json with \"source\": {\"results\": \"models.keyness_weather_c5.outputs...\", \"fields\": {...}}, and name ","enforced":true,"passed":false,"phase":"initial","segment":3,"type":"check"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T18:59:22Z","model":"sonnet","output":{"file":"design/econometric_spec.md","sha256":"18c0a21914e09d3fbd2bea0e2174e01108bb8adaeded073c6a87860449cbd119","unchanged":true},"outputs":[{"file":"design/econometric_spec.md","sha256":"18c0a21914e09d3fbd2bea0e2174e01108bb8adaeded073c6a87860449cbd119","unchanged":true},{"file":"results/estimation_results.json","sha256":"8179c58a3b36b2f1a1b15c2458983d0860a8ec37464197abbb0a2a2760854fd5"}],"phase":"initial","segment":3,"specialist":"econometrics_specialist","started":"2026-10-14T18:58:29Z","type":"specialist"},{"at":"2026-10-14T18:59:27Z","check":"text_results","enforced":true,"passed":true,"phase":"initial","segment":3,"type":"check"},{"action":"rerun","at":"2026-10-14T19:03:12Z","phase":"initial","remark":"No translations: the corpus still holds ten translated novels - Dumas (pg1258, pg2759, pg1608), Gautier (pg1235), Muehlbach (pg4067), Gaboriau (pg1748, pg547), Verne (pg1353, pg83), Flaubert (pg1290). Exclude them, and exclude any candidate whose bookshelves name a non-English literature ('Category: French Literature', German, Russian, Italian, Spanish, Scandinavian, ...) or whose subjects say 'Translations into English'. Also drop non-novels kept by the loose subject filter: Sartor Resartus (pg1051), Our Village (pg2496), Nature and Human Nature (pg6112), A Dream of John Ball (pg357). Refill from the next ranks of the seeded draw order and from the catalogue tables and extra CSVs already in the workspace; load the replacement texts in at most one e2er-data gutenberg text call per cohort (Gutendex is slow; the texts already in data.db are not loaded again). If a cohort still has fewer than 18 eligible novels, cut every cohort to the smallest n, as the design says. Then redo the analysis on the corrected corpus; nothing else changes.","reruns":["initial","estimation_gate","figure_gate","review_draft","review","revision","replication"],"segment":3,"specialist":"data_analyst","step":"initial","target":"initial","type":"researcher"},{"at":"2026-10-14T19:03:16Z","check":"text_results","enforced":true,"passed":true,"phase":"initial","segment":4,"type":"check"},{"at":"2026-10-14T22:52:44Z","check":"contracts","detail":"data.db: data_dictionary.json declares tables that were not loaded — loaded without values: gutenberg_texts_c3_refill.authors: 8 of 9 non-null (needs 90%); gutenberg_texts_c3_refill.author_birth_year: 8 of 9 non-null (needs 90%); gutenberg_texts_c3_refill.author_death_year: 8 of 9 non-null (needs 90%). Tables in data.db: corpus (84 rows), corpus_cohort_summary (6 rows), corpus_token_counts (84 rows), gutenberg_books_c1 (320 rows), gutenberg_books_c2 (320 rows), gutenberg_books_c3 (320 rows), gutenberg_books_c4 (320 rows), gutenberg_books_c5 (320 rows), gutenberg_books_c6 (320 rows), gutenberg_texts_c1 (20 rows), gutenberg_texts_c2 (19 rows), gutenberg_texts_c3 (20 rows), gutenberg_texts_c3_refill (9 rows), gutenberg_texts_c4 (20 rows), gutenberg_texts_c5 (20 rows), gutenberg_texts_c6 (20 rows), weather_dictionary_hits (2311 rows). Load each series with `e2er-data ... --table <name>`; a table whose values are NULL was not loaded (check the connector's error, e.g. an API key).","enforced":true,"passed":false,"phase":"initial","segment":5,"type":"check"},{"accepted":false,"backend":"claude_code","ended":"2026-10-14T22:52:44Z","model":"sonnet","output":{"file":"data/data_summary.md","sha256":"92a0feb2649ee8ff6a3cc715fc9c023d9422a0564f86d9d307ff262ab8074fad"},"outputs":[{"file":"data/data_summary.md","sha256":"92a0feb2649ee8ff6a3cc715fc9c023d9422a0564f86d9d307ff262ab8074fad"},{"file":"results/summary_statistics.json","sha256":"fa29b2cfef56f3ff192cc0541c1da50411c6d80c8e81ba76d7622377e8dbcd3b"},{"file":"results/figure_spec.json","sha256":"dc9cae56975b01d441c439570696467e10212b417e9006dd4a29e75a2ebf3415"},{"file":"data/corpus_manifest.json","sha256":"c61f68bd62778156ca6b4e078e2039420da81a023fe732382697660aebdcc59a"}],"phase":"initial","segment":5,"specialist":"data_analyst","started":"2026-10-14T22:48:05Z","stopped_by":"contract violation: data.db: data_dictionary.json declares tables that were not loaded — loaded without values: gutenberg_texts_c3_refill.authors: 8 of 9 non-null (needs 90%); gutenberg_texts_c3_refill.author_birth_year: 8 of 9 non-null (needs 90%); gutenberg_texts_c3_refill.author_death_year: 8 of 9 non-null (needs 90%). Tables in data.db: corpus (84 rows), corpus_cohort_summary (6 rows), corpus_token_counts (84 rows), gutenberg_books_c1 (320 rows), gutenberg_books_c2 (320 rows), gutenberg_books_c3 (320 rows), gutenberg_books_c4 (320 rows), gutenberg_books_c5 (320 rows), gutenberg_books_c6 (320 rows), gutenberg_texts_c1 (20 rows), gutenberg_texts_c2 (19 rows), gutenberg_texts_c3 (20 rows), gutenberg_texts_c3_refill (9 rows), gutenberg_texts_c4 (20 rows), gutenberg_texts_c5 (20 rows), gutenberg_texts_c6 (20 rows), weather_dictionary_hits (2311 rows). Load each series with `e2er-data ... --table <name>`; a table whose values are NULL was not loaded (check the connector's error, e.g. an API key).","type":"specialist"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T22:53:15Z","model":"sonnet","output":{"file":"data/data_summary.md","sha256":"2292e2a0138d766b0869f60d06693d298f9aee7046f3adccc7dcc0a8e1bf360c"},"outputs":[{"file":"data/data_summary.md","sha256":"2292e2a0138d766b0869f60d06693d298f9aee7046f3adccc7dcc0a8e1bf360c"},{"file":"results/summary_statistics.json","sha256":"fa29b2cfef56f3ff192cc0541c1da50411c6d80c8e81ba76d7622377e8dbcd3b","unchanged":true},{"file":"results/figure_spec.json","sha256":"dc9cae56975b01d441c439570696467e10212b417e9006dd4a29e75a2ebf3415","unchanged":true},{"file":"data/corpus_manifest.json","sha256":"c61f68bd62778156ca6b4e078e2039420da81a023fe732382697660aebdcc59a","unchanged":true}],"phase":"initial","segment":5,"specialist":"data_analyst","started":"2026-10-14T22:52:44Z","type":"specialist"},{"at":"2026-10-14T22:53:16Z","check":"corpus","enforced":true,"passed":true,"phase":"initial","segment":5,"type":"check"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T22:53:53Z","model":"sonnet","output":{"file":"design/econometric_spec.md","sha256":"4836e9af62c4d9ef7f59b30cea291f47546246e4a706b15bfb064dda0b77b0ef"},"outputs":[{"file":"design/econometric_spec.md","sha256":"4836e9af62c4d9ef7f59b30cea291f47546246e4a706b15bfb064dda0b77b0ef"},{"file":"results/estimation_results.json","sha256":"fe9cca90838c7f722264e4541ee092d9cb3788c9e3bf8ebbe9c3f3129fe327b4"}],"phase":"initial","segment":5,"specialist":"econometrics_specialist","started":"2026-10-14T22:53:16Z","type":"specialist"},{"at":"2026-10-14T22:53:56Z","check":"text_results","enforced":true,"passed":true,"phase":"initial","segment":5,"type":"check"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T23:00:20Z","model":"sonnet","output":{"file":"results/text_validation.md","sha256":"dd84e0a69aa9e2e3facf53c3678a52bf1e913d7a602b6f635dbf7f2e1c212402"},"outputs":[{"file":"results/text_validation.md","sha256":"dd84e0a69aa9e2e3facf53c3678a52bf1e913d7a602b6f635dbf7f2e1c212402"},{"file":"results/text_validation.json","sha256":"893c28c8f544fa7c88e890ba72af4662c2ff701fc760b6f19f534dfa7203a50c"}],"phase":"initial","segment":5,"specialist":"text_validator","started":"2026-10-14T22:53:56Z","type":"specialist"},{"at":"2026-10-14T23:00:22Z","check":"text_validation","enforced":true,"passed":true,"phase":"initial","segment":5,"type":"check"},{"at":"2026-10-14T23:01:00Z","check":"contracts","detail":"abstract.tex: 1 construction(s) the researcher does not accept in a paper: \"rather than\" in: Cohort differences in these families may therefore reflect figurative use rather than weather description. (state the positive claim and stop). Rewrite each of these sentences without the construction; keep its content","enforced":true,"passed":false,"phase":"initial","segment":5,"type":"check"},{"accepted":false,"backend":"claude_code","ended":"2026-10-14T23:01:00Z","model":"sonnet","output":{"file":"paper/abstract.tex","sha256":"7c4cf8ed2e2e8ec3b30936be8906cdf266ec6c39cd5f84e885f88319f5bd0d5f"},"phase":"initial","segment":5,"specialist":"abstract_writer","started":"2026-10-14T23:00:22Z","stopped_by":"contract violation: abstract.tex: 1 construction(s) the researcher does not accept in a paper: \"rather than\" in: Cohort differences in these families may therefore reflect figurative use rather than weather description. (state the positive claim and stop). Rewrite each of these sentences without the construction; keep its content","type":"specialist"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T23:01:11Z","model":"sonnet","output":{"file":"paper/abstract.tex","sha256":"4909138d298a6835404e3cba9065dd398c3f0e31c20f943e7d57734b1604f0d5"},"phase":"initial","segment":5,"specialist":"abstract_writer","started":"2026-10-14T23:01:00Z","type":"specialist"},{"at":"2026-10-14T23:04:37Z","check":"contracts","detail":"paper_draft.tex: 2 construction(s) the researcher does not accept in a paper: \"Against this background\" in: Against this background, this paper fixes the genre (novels), fixes the source (Project Gutenberg), and indexes authors by birth year. (pivot to the contribution directly (for instance: To keep these quantities apart, we propose ...)); \"rather than\" in: First, we provide a new descriptive fact: a documented cohort profile of a concrete lexical field in the English novel, normalised by tokens and reported with intervals that resample authors rather than tokens. (state the positive claim and stop). Rewrite each of these sentences without the construction; keep its content","enforced":true,"passed":false,"phase":"initial","segment":5,"type":"check"},{"accepted":false,"backend":"claude_code","ended":"2026-10-14T23:04:37Z","model":"sonnet","output":{"file":"paper/paper.tex","sha256":"d025cd83ba4bf02e3c8f41efb5c242fcd398d9cb6fa404ca1500aa58177456e5"},"outputs":[{"file":"paper/paper.tex","sha256":"d025cd83ba4bf02e3c8f41efb5c242fcd398d9cb6fa404ca1500aa58177456e5"},{"file":"results/table_spec.json","sha256":"c8b527da54d61f90a1b568b6b8b2edd18ecfe34e330d048922c3373736e7f2c1"}],"phase":"initial","segment":5,"specialist":"paper_drafter","started":"2026-10-14T23:00:22Z","stopped_by":"contract violation: paper_draft.tex: 2 construction(s) the researcher does not accept in a paper: \"Against this background\" in: Against this background, this paper fixes the genre (novels), fixes the source (Project Gutenberg), and indexes authors by birth year. (pivot to the contribution directly (for instance: To keep these quantities apart, we propose ...)); \"rather than\" in: First, we provide a new descriptive fact: a documented cohort profile of a concrete lexical field in the English novel, normalised by tokens and reported with intervals that resample authors rather than tokens. (state the positive claim and stop). Rewrite each of these sentences without the construction; keep its content","type":"specialist"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T23:04:50Z","model":"sonnet","output":{"file":"paper/paper.tex","sha256":"1cee0b7284c9e83f2cdc13765275cbe03052c686c2684cd4c1e2a255f8e678db"},"outputs":[{"file":"paper/paper.tex","sha256":"1cee0b7284c9e83f2cdc13765275cbe03052c686c2684cd4c1e2a255f8e678db"},{"file":"results/table_spec.json","sha256":"c8b527da54d61f90a1b568b6b8b2edd18ecfe34e330d048922c3373736e7f2c1","unchanged":true}],"phase":"initial","segment":5,"specialist":"paper_drafter","started":"2026-10-14T23:04:37Z","type":"specialist"},{"at":"2026-10-14T23:04:50Z","check":"estimation","enforced":true,"passed":true,"phase":"estimation_gate","segment":5,"type":"check"},{"at":"2026-10-14T23:04:50Z","check":"figure_data","detail":"fig_corpus_tokens_by_cohort.pdf names no 'source' (a data.db table and columns, or a results path); fig_corpus_median_length_by_cohort.pdf names no 'source' (a data.db table and columns, or a results path); fig_cohort_trend.pdf series rain: not the values of estimation_results.json models.cohort_trend.outputs.series (x: the figure shows '1770-1799' where the data have 'c1'); fig_cohort_trend.pdf series wind: not the values of estimation_results.json models.cohort_trend.outputs.series (x: the figure shows '1770-1799' where the data have 'c1'); fig_cohort_trend.pdf series storm: not the values of estimation_results.json models.cohort_trend.outputs.series (x: the figure shows '1770-1799' where the data have 'c1'); fig_cohort_trend.pdf series fog: not the values of estimation_results.json models.cohort_trend.outputs.series (x: the figure shows '1770-1799' where the data have 'c1'); fig_cohort_trend.pdf series snow: not the values of estimation_results.json models.cohort_trend.outputs.series (x: the figure shows '1770-1799' where the data have 'c1'); fig_cohort_trend.pdf series sun: not the values of estimation_results.json models.cohort_trend.outputs.series (x: the figure shows '1770-1799' where the data have 'c1'); fig_cohort_trend.pdf series cloud: not the values of estimation_results.json models.cohort_trend.outputs.series (x: the figure shows '1770-1799' where the data have 'c1')","enforced":true,"passed":false,"phase":"figure_gate","segment":5,"type":"check"},{"at":"2026-10-14T23:04:50Z","check":"figure_gate","detail":"fig_corpus_tokens_by_cohort.pdf names no 'source' (a data.db table and columns, or a results path); fig_corpus_median_length_by_cohort.pdf names no 'source' (a data.db table and columns, or a results path); fig_cohort_trend.pdf series rain: not the values of estimation_results.json models.cohort_trend.outputs.series (x: the figure shows '1770-1799' where the data have 'c1'); fig_cohort_trend.pdf series wind: not the values of estimation_results.json models.cohort_trend.outputs.series (x: the figure shows '1770-1799' where the data have 'c1'); fig_cohort_trend.pdf series storm: not the values of estimation_results.json models.cohort_trend.outputs.series (x: the figure shows '1770-1799' where the data have 'c1'); fig_cohort_trend.pdf series fog: not the values of estimation_results.json models.cohort_trend.outputs.series (x: the figure shows '1770-1799' where the data have 'c1'); fig_cohort_trend.pdf series snow: not the values of estimation_results.json models.cohort_trend.outputs.series (x: the figure shows '1770-1799' where the data have 'c1'); fig_cohort_trend.pdf series sun: not the values of estimation_results.json models.cohort_trend.outputs.series (x: the figure shows '1770-1799' where the data have 'c1'); fig_cohort_trend.pdf series cloud: not the values of estimation_results.json models.cohort_trend.outputs.series (x: the figure shows '1770-1799' where the data have 'c1')","enforced":true,"halted":true,"passed":false,"phase":"figure_gate","segment":5,"type":"check"},{"action":"instruction","at":"2026-10-14T23:06:06Z","phase":"figure_gate","segment":5,"step":"figure_gate","text":"Draft corrections (for whoever writes or revises paper_draft.tex and abstract.tex): (1) Do not attribute to Waugh 2008 or Laughlin 2020 a claim that fog rose, storms receded, or sun and rain receded; say only what they treat (weather and climate as a theme of the Victorian novel; Dickens's fog and coal), and drop 'literary scholars often claim fog rising and storms receding' from the abstract. (2) Snow and ice: one consistent statement everywhere - 0.82 in cohort 1 to a peak of 2.06 in cohort 5 and 1.44 in cohort 6, not 'roughly double' in one place and 'two and a half times' in another. (3) Fog: the hand check found 0 of 4 fog hits in cohort 6 to be weather and fog precision is 0.50 overall, so the higher fog/mist rate in cohort 6 cannot be read as more fog description; say this wherever the fog rise is mentioned (abstract, results, conclusion) and report the post-hoc reduced-lexicon result next to it. (4) The robustness section shows flat, flat and rising profiles - no lexicon variant falls - so remove 'a flat, a rising or a falling profile' and 'can change sign'. (5) The collocates do not show 'no sign of a different sense in the aggregate': adjacent-pair PMI says nothing about figurative use, and the validation shows it for storm, fog and cloud words; delete that clause. (6) Limitations: no gender, ethnicity or nationality was coded and no publication year was looked up, so delete 'under-represents women, authors of colour', 'British and American' and 'mostly early works'; say instead that the composition by these traits is unknown. (7) Cohort labels: the design ranges are 1770-1799 ... 1880-1899, but the realised birth years are 1770-1797 ... 1880-1890; say so once in the corpus section and use 'born 1770-1890' for the corpus as a whole.","type":"researcher"},{"action":"send_back","at":"2026-10-14T23:06:06Z","phase":"figure_gate","remark":"Figure check: (a) fig_corpus_tokens_by_cohort and fig_corpus_median_length_by_cohort need a 'source' (data.db table and columns, or a results path) in figure_spec.json; (b) fig_cohort_trend must plot the x values as they are in models.cohort_trend.outputs.series ('c1'...'c6'); put the birth-year ranges in the caption or axis title, not in place of the data values. Also add ONE robustness entry, labelled as defined after the hand check (post hoc): the composite and the storm, fog and cloud families recomputed without the forms the validation judged unusable (thunder, thunders, thundered, thundering, hurricane, haze, hazy, clouded, cloudy, overcast) and without capitalised mid-sentence hits, with cohort rates per 10,000 tokens, author-bootstrap intervals and the Spearman summary as for the other lexicon variants. Change nothing else; rerun run_estimation.py so the results come from the code.","segment":5,"step":"figure_gate","target":"econometrics_specialist","then":"check_runs_again","type":"researcher"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T23:07:14Z","model":"sonnet","output":{"file":"design/econometric_spec.md","sha256":"636372e4e3607fc6b195d1063504738e26bd25aa9f5def0ca681bcd421feba1f"},"outputs":[{"file":"design/econometric_spec.md","sha256":"636372e4e3607fc6b195d1063504738e26bd25aa9f5def0ca681bcd421feba1f"},{"file":"results/estimation_results.json","sha256":"f09a2bb0b2d1db99cf8a137a4768166c79306ab2b141ec04c1cad7680f5c1b5b"}],"phase":"figure_gate","segment":6,"sent_back":{"at":"2026-10-14T23:06:06Z","remark":"Figure check: (a) fig_corpus_tokens_by_cohort and fig_corpus_median_length_by_cohort need a 'source' (data.db table and columns, or a results path) in figure_spec.json; (b) fig_cohort_trend must plot the x values as they are in models.cohort_trend.outputs.series ('c1'...'c6'); put the birth-year ranges in the caption or axis title, not in place of the data values. Also add ONE robustness entry, labelled as defined after the hand check (post hoc): the composite and the storm, fog and cloud families recomputed without the forms the validation judged unusable (thunder, thunders, thundered, thundering, hurricane, haze, hazy, clouded, cloudy, overcast) and without capitalised mid-sentence hits, with cohort rates per 10,000 tokens, author-bootstrap intervals and the Spearman summary as for the other lexicon variants. Change nothing else; rerun run_estimation.py so the results come from the code."},"specialist":"econometrics_specialist","started":"2026-10-14T23:06:06Z","type":"specialist"},{"at":"2026-10-14T23:07:17Z","check":"text_results","enforced":true,"passed":true,"phase":"figure_gate","segment":6,"type":"check"},{"at":"2026-10-14T23:07:19Z","check":"text_validation","enforced":true,"passed":true,"phase":"figure_gate","segment":6,"type":"check"},{"at":"2026-10-14T23:07:19Z","check":"estimation","enforced":true,"passed":true,"phase":"estimation_gate","segment":6,"type":"check"},{"at":"2026-10-14T23:07:19Z","check":"figure_data","enforced":true,"passed":true,"phase":"figure_gate","segment":6,"type":"check"},{"action":"send_back","at":"2026-10-14T23:07:40Z","phase":"figure_gate","remark":"Revise paper_draft.tex and abstract.tex on the current estimation_results.json: apply the seven draft corrections in researcher_instructions.md (instruction at the figure check). In addition: (8) report robustness.post_hoc_reduced_lexicon in the robustness section and say it was defined after the hand check: the composite becomes flat (Spearman -0.09), the fog/mist rise weakens (0.65 in cohort 1 to 1.26 in cohort 6, Spearman 0.54, p = 0.27), and storm words fall more steadily (1.68 to 0.96, Spearman -0.94, p = 0.005); because the term list was chosen after looking at the sample and the precision of the reduced storm family was not re-checked, call the storm decline a pattern worth testing, not a finding, and keep the snow and ice wording at 'suggestive'. (9) Figure captions: the trend figure's x axis shows cohorts c1-c6; give the realised birth years in the caption. Keep every number traced to a source comment as now; change nothing else.","segment":6,"step":"review_draft","target":"paper_drafter","type":"researcher"},{"at":"2026-10-14T23:07:40Z","check":"figure_data","enforced":true,"passed":true,"phase":"figure_gate","segment":7,"type":"check"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T23:08:58Z","model":"sonnet","output":{"file":"paper/paper.tex","sha256":"4b72c9383d7a265b725dac717e6fd3d31d94f686a24e0cc23b1bfd7f4872ff6d"},"outputs":[{"file":"paper/paper.tex","sha256":"4b72c9383d7a265b725dac717e6fd3d31d94f686a24e0cc23b1bfd7f4872ff6d"},{"file":"results/table_spec.json","sha256":"c8b527da54d61f90a1b568b6b8b2edd18ecfe34e330d048922c3373736e7f2c1","unchanged":true}],"phase":"figure_gate","segment":7,"sent_back":{"at":"2026-10-14T23:07:40Z","remark":"Revise paper_draft.tex and abstract.tex on the current estimation_results.json: apply the seven draft corrections in researcher_instructions.md (instruction at the figure check). In addition: (8) report robustness.post_hoc_reduced_lexicon in the robustness section and say it was defined after the hand check: the composite becomes flat (Spearman -0.09), the fog/mist rise weakens (0.65 in cohort 1 to 1.26 in cohort 6, Spearman 0.54, p = 0.27), and storm words fall more steadily (1.68 to 0.96, Spearman -0.94, p = 0.005); because the term list was chosen after looking at the sample and the precision of the reduced storm family was not re-checked, call the storm decline a pattern worth testing, not a finding, and keep the snow and ice wording at 'suggestive'. (9) Figure captions: the trend figure's x axis shows cohorts c1-c6; give the realised birth years in the caption. Keep every number traced to a source comment as now; change nothing else."},"specialist":"paper_drafter","started":"2026-10-14T23:07:40Z","type":"specialist"},{"action":"send_back","at":"2026-10-14T23:09:19Z","phase":"figure_gate","remark":"abstract.tex is from before the corrections and contradicts the revised paper (unsupported 'literary scholars often claim fog rising and storms receding', 'roughly double', uncaveated fog rise). Rewrite it from the abstract and results now in paper_draft.tex: snow/ice 0.82 to a 2.06 peak and 1.44 (suggestive), tempest in the earliest cohort, fog rise not readable as fog description (0 of 4 cohort-6 fog hits were weather), precision 0.71 overall and 0.42/0.50/0.58 for storm/fog/cloud, the post-hoc storm decline as a pattern worth testing; no causal claim.","segment":7,"step":"review_draft","target":"abstract_writer","type":"researcher"},{"at":"2026-10-14T23:09:19Z","check":"figure_data","enforced":true,"passed":true,"phase":"figure_gate","segment":8,"type":"check"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T23:09:43Z","model":"sonnet","output":{"file":"paper/abstract.tex","sha256":"167f6b850f4639ad56e3e54587a8d9a0cb52e9dcfed2c73caa52c0e88d5b5a59"},"phase":"figure_gate","segment":8,"sent_back":{"at":"2026-10-14T23:09:19Z","remark":"abstract.tex is from before the corrections and contradicts the revised paper (unsupported 'literary scholars often claim fog rising and storms receding', 'roughly double', uncaveated fog rise). Rewrite it from the abstract and results now in paper_draft.tex: snow/ice 0.82 to a 2.06 peak and 1.44 (suggestive), tempest in the earliest cohort, fog rise not readable as fog description (0 of 4 cohort-6 fog hits were weather), precision 0.71 overall and 0.42/0.50/0.58 for storm/fog/cloud, the post-hoc storm decline as a pattern worth testing; no causal claim."},"specialist":"abstract_writer","started":"2026-10-14T23:09:19Z","type":"specialist"},{"action":"approve","at":"2026-10-14T23:09:55Z","phase":"figure_gate","segment":8,"step":"review_draft","type":"researcher"},{"at":"2026-10-14T23:09:55Z","check":"estimation","enforced":true,"passed":true,"phase":"estimation_gate","segment":9,"type":"check"},{"at":"2026-10-14T23:09:55Z","check":"figure_data","enforced":true,"passed":true,"phase":"figure_gate","segment":9,"type":"check"},{"at":"2026-10-14T23:09:57Z","check":"numbers","enforced":true,"passed":true,"phase":"review","segment":9,"type":"check"},{"at":"2026-10-14T23:10:05Z","check":"citations","enforced":true,"passed":true,"phase":"review","segment":9,"type":"check"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T23:12:35Z","model":"sonnet","output":{"file":"reviews/review_writing.md","sha256":"11852395c4c047e6101867dc6a02b59cc5cf6a1c4d7129a1fedd0eaaf7a1bcb6"},"phase":"review","segment":9,"specialist":"writing_reviewer","started":"2026-10-14T23:10:05Z","type":"specialist"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T23:13:28Z","model":"sonnet","output":{"file":"reviews/review_technical.md","sha256":"f52768dc34f0ff0b5f284be2be343717c9c59622c7373a621a25275edb40d589"},"phase":"review","segment":9,"specialist":"technical_reviewer","started":"2026-10-14T23:10:05Z","type":"specialist"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T23:13:41Z","model":"sonnet","output":{"file":"reviews/review_data.md","sha256":"f5295a6c4d0a5f5ca757658d33beb5b104ee6c3d8a166c3517364c9efa70b11b"},"phase":"review","segment":9,"specialist":"data_reviewer","started":"2026-10-14T23:10:05Z","type":"specialist"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T23:15:41Z","model":"sonnet","output":{"file":"reviews/review_methods.md","sha256":"51f0472b54b04adfe0f50c9d6bdfe4ddfc59f41ce8b6ed64f2689fbf4b7330f9"},"phase":"review","segment":9,"specialist":"methods_reviewer","started":"2026-10-14T23:12:35Z","type":"specialist"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T23:16:15Z","model":"sonnet","output":{"file":"reviews/review_plausibility.md","sha256":"e9a1d3d426e9101e14abe61ba9223c16015fbdd38da6b2e2d3afdc2ba931fee0"},"phase":"review","segment":9,"specialist":"plausibility_reviewer","started":"2026-10-14T23:13:28Z","type":"specialist"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T23:18:04Z","model":"sonnet","output":{"file":"misc/paper_draft.tex.edits.json","sha256":"acbf8d3f6256502426e36090df8da25e3ca1610b0947d2784b0d48abf898d0a2"},"phase":"revision","segment":9,"specialist":"patch_revisor","started":"2026-10-14T23:16:16Z","type":"specialist"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T23:20:22Z","model":"sonnet","output":{"file":"replication/estimation.py","sha256":"e30852fabefa4216d522846dddf7d05a1845e31c0385deb50f27b26a8549c74e"},"phase":"replication","segment":9,"specialist":"replication_packager","started":"2026-10-14T23:18:04Z","type":"specialist"}]}