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The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. 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If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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{"parentUuid":"2be57ddc-4092-40a3-a8de-a37443a37582","isSidechain":false,"promptId":"c68085df-cb49-4ec2-9139-96f84bf5c3f8","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\n# F2-conf-01 — Effect of a school-level programme on student scores, with valid uncertainty\n\n## Scientific question\nA programme was assigned at the school level: every student in a treated school is exposed, no student in a control school is. Estimate the average effect of the programme on the student test score and report a valid measure of its sampling uncertainty. The estimand τ is the difference between the mean score of students in treated schools and the mean score of students in control schools (student-weighted means, i.e. the OLS coefficient of `score` on `treated` with an intercept). Uncertainty must reflect the design: the programme varies only between schools, and students within the same school share unobserved school-level influences; schools are independent of each other. Decide, at the 5% level (two-sided), whether the data support a non-zero effect.\n\n## Inputs\n- `data/schools.csv` — simulated data (a synthetic dataset generated for this exercise; it is not observed data). Columns: `school_id` (integer school identifier), `student_id` (integer student identifier, unique), `treated` (1 if the student's school received the programme, 0 otherwise — constant within a school), `score` (test score, points). One row per student; rows are in random order. The design is balanced: every school has the same number of students.\n\n## Information-availability rules\n- Use only `data/schools.csv`. No other input exists.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | – | \"F2-conf-01\" |\n| `tau_hat` | number | points | estimated programme effect (OLS coefficient of score on treated, with intercept) |\n| `se_iid` | number | points | conventional OLS standard error of `tau_hat` assuming independent, homoskedastic errors — the naïve baseline |\n| `se_cluster` | number | points | standard error of `tau_hat` that is valid under the two-level design described above (your main uncertainty measure) |\n| `p_value` | number | – | two-sided p-value of H0: τ = 0 using `se_cluster` |\n| `tau_ci_low`, `tau_ci_high` | number | points | 95% confidence interval for τ built from `se_cluster` |\n| `effect_significant_5pct` | boolean | – | true if and only if H0: τ = 0 is rejected at the 5% level with the valid uncertainty measure |\n| `icc_hat` | number | – | estimated intraclass correlation of the score residual (share of residual variance that is between schools, after removing the treatment effect) |\n| `n_schools` | integer | – | number of distinct schools |\n| `n_treated_schools` | integer | – | number of treated schools |\n| `n_analysed` | integer | – | students used in the estimate |\n| `feasibility` | string | – | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | – | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | – | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | – | {\"schools.csv\": rows read} |\n| `random_seed` | integer or null | – | seed used, if any (e.g. for a bootstrap) |\n\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` 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\": \"...\"}]. Include one claim about whether the programme has a non-zero effect at the 5% level.\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 24\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:29 .\ndrwxr-xr-x  77 simon-pierreboucher  staff  2464 oct.  30 00:35 ..\n-rw-r--r--   1 simon-pierreboucher  staff   421 oct.  29 23:29 bundle.json\ndrwxr-xr-x   3 simon-pierreboucher  staff    96 oct.  29 23:26 data\n-rw-r--r--   1 simon-pierreboucher  staff  1609 oct.  29 23:28 gate_spec.json\n-rw-r--r--   1 simon-pierreboucher  staff  3925 oct.  29 23:28 TASK.md\n\n./data:\ntotal 40\ndrwxr-xr-x  3 simon-pierreboucher  staff     96 oct.  29 23:26 .\ndrwxr-xr-x  6 simon-pierreboucher  staff    192 oct.  29 23:29 ..\n-rw-r--r--  1 simon-pierreboucher  staff  16818 oct.  29 23:26 schools.csv\n(eval):1: no matches found: out/state/*","is_error":true,"tool_use_id":"toolu_015oaSTRYRPkdrZ1uK8hA5EK"}]},"uuid":"633204b8-728c-4be0-9409-af0318e670a4","timestamp":"2026-10-30T04:35:23.636Z","toolUseResult":"Error: Exit code 1\n# F2-conf-01 — Effect of a school-level programme on student scores, with valid uncertainty\n\n## Scientific question\nA programme was assigned at the school level: every student in a treated school is exposed, no student in a control school is. Estimate the average effect of the programme on the student test score and report a valid measure of its sampling uncertainty. The estimand τ is the difference between the mean score of students in treated schools and the mean score of students in control schools (student-weighted means, i.e. the OLS coefficient of `score` on `treated` with an intercept). Uncertainty must reflect the design: the programme varies only between schools, and students within the same school share unobserved school-level influences; schools are independent of each other. Decide, at the 5% level (two-sided), whether the data support a non-zero effect.\n\n## Inputs\n- `data/schools.csv` — simulated data (a synthetic dataset generated for this exercise; it is not observed data). Columns: `school_id` (integer school identifier), `student_id` (integer student identifier, unique), `treated` (1 if the student's school received the programme, 0 otherwise — constant within a school), `score` (test score, points). One row per student; rows are in random order. The design is balanced: every school has the same number of students.\n\n## Information-availability rules\n- Use only `data/schools.csv`. No other input exists.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | – | \"F2-conf-01\" |\n| `tau_hat` | number | points | estimated programme effect (OLS coefficient of score on treated, with intercept) |\n| `se_iid` | number | points | conventional OLS standard error of `tau_hat` assuming independent, homoskedastic errors — the naïve baseline |\n| `se_cluster` | number | points | standard error of `tau_hat` that is valid under the two-level design described above (your main uncertainty measure) |\n| `p_value` | number | – | two-sided p-value of H0: τ = 0 using `se_cluster` |\n| `tau_ci_low`, `tau_ci_high` | number | points | 95% confidence interval for τ built from `se_cluster` |\n| `effect_significant_5pct` | boolean | – | true if and only if H0: τ = 0 is rejected at the 5% level with the valid uncertainty measure |\n| `icc_hat` | number | – | estimated intraclass correlation of the score residual (share of residual variance that is between schools, after removing the treatment effect) |\n| `n_schools` | integer | – | number of distinct schools |\n| `n_treated_schools` | integer | – | number of treated schools |\n| `n_analysed` | integer | – | students used in the estimate |\n| `feasibility` | string | – | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | – | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | – | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | – | {\"schools.csv\": rows read} |\n| `random_seed` | integer or null | – | seed used, if any (e.g. for a bootstrap) |\n\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` 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\": \"...\"}]. Include one claim about whether the programme has a non-zero effect at the 5% level.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). 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No randomness.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport statsmodels.formula.api as smf\nfrom scipy import stats\n\nos.makedirs(\"out\", exist_ok=True)\nraw = pd.read_csv(\"data/schools.csv\")\nn_raw = len(raw)\nissues, excl = [], []\nd = raw.copy()\n\ndef step(name, rule, mask, why):\n    global d\n    nb = len(d); d = d[mask(d)].copy(); na = len(d)\n    excl.append(dict(step=name, rule=rule, n_before=nb, n_after=na, n_removed=nb - na, rationale=why))\n\nstep(\"1\", \"drop rows with missing school_id/treated/score\", lambda x: x[[\"school_id\", \"treated\", \"score\"]].notna().all(axis=1), \"cannot be used in OLS\")\nstep(\"2\", \"drop exact duplicate rows\", lambda x: ~x.duplicated(), \"duplicate records\")\nstep(\"3\", \"drop duplicated student_id (keep first)\", lambda x: ~x.student_id.duplicated(), \"student_id must be unique\")\nstep(\"4\", \"treated must be 0/1\", lambda x: x.treated.isin([0, 1]), \"binary treatment\")\n# within-school treatment consistency\nbad = d.groupby(\"school_id\").treated.nunique()\nbad = bad[bad > 1].index\nstep(\"5\", \"drop schools with non-constant treated\", lambda x: ~x.school_id.isin(bad), \"treatment must be constant within school\")\nfor e in excl:\n    if e[\"n_removed\"] > 0:\n        issues.append({\"issue\": f\"{e['rule']}: {e['n_removed']} rows\", \"handling\": \"excluded\"})\npd.DataFrame(excl).to_csv(\"out/exclusions.csv\", index=False)\n\nsizes = d.groupby(\"school_id\").size()\nif sizes.nunique() != 1:\n    issues.append({\"issue\": \"unequal school sizes\", \"handling\": \"kept; student-weighted OLS as defined\"})\n\nG = d.school_id.nunique()\nG1 = int(d.groupby(\"school_id\").treated.first().sum())\nN = len(d)\n\n# Baseline: conventional iid OLS\nm_iid = smf.ols(\"score ~ treated\", data=d).fit()\ntau = float(m_iid.params[\"treated\"]); se_iid = float(m_iid.bse[\"treated\"])\n\n# Main: cluster-robust (CR1, Liang-Zeger with small-sample factor G/(G-1)*(N-1)/(N-K)), t with G-1 df\nm_cl = smf.ols(\"score ~ treated\", data=d).fit(cov_type=\"cluster\", cov_kwds={\"groups\": d.school_id}, use_t=True)\nse_cl = float(m_cl.bse[\"treated\"])\ndf_t = G - 1\ntstat = tau / se_cl\np = float(2 * stats.t.sf(abs(tstat), df_t))\nq = stats.t.ppf(0.975, df_t)\nlo, hi = tau - q * se_cl, tau + q * se_cl\n\n# Sensitivity: school-means Welch/pooled t-test (one obs per school)\nsm = d.groupby(\"school_id\").agg(y=(\"score\", \"mean\"), t=(\"treated\", \"first\"))\ntt = stats.ttest_ind(sm.y[sm.t == 1], sm.y[sm.t == 0], equal_var=True)\nse_means = float((sm.y[sm.t == 1].mean() - sm.y[sm.t == 0].mean()) / tt.statistic)\n\n# ICC of residual (one-way ANOVA estimator, school as random factor)\nd[\"res\"] = m_iid.resid\nk = N / G if sizes.nunique() == 1 else (N - (sizes**2).sum() / N) / (G - 1)\ngm = d.groupby(\"school_id\").res.agg([\"mean\", \"size\"])\nssb = float((gm[\"size\"] * (gm[\"mean\"] - d.res.mean())**2).sum())\nssw = float(((d.res - d.school_id.map(gm[\"mean\"]))**2).sum())\nmsb = ssb / (G - 2)   # one df lost to treatment effect (between-school)\nmsw = ssw / (N - G)\ns2b = max((msb - msw) / k, 0.0)\nicc = s2b / (s2b + msw)\ndeff = 1 + (k - 1) * icc\n\nres = {\n    \"task_id\": \"F2-conf-01\",\n    \"tau_hat\": tau,\n    \"se_iid\": se_iid,\n    \"se_cluster\": se_cl,\n    \"p_value\": p,\n    \"tau_ci_low\": float(lo),\n    \"tau_ci_high\": float(hi),\n    \"effect_significant_5pct\": bool(p < 0.05),\n    \"icc_hat\": float(icc),\n    \"n_schools\": int(G),\n    \"n_treated_schools\": G1,\n    \"n_analysed\": int(N),\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"schools.csv\": int(n_raw)},\n    \"random_seed\": None,\n    # supplementary\n    \"t_stat_cluster\": float(tstat),\n    \"df_cluster\": int(df_t),\n    \"p_value_iid\": float(m_iid.pvalues[\"treated\"]),\n    \"se_ratio_cluster_over_iid\": se_cl / se_iid,\n    \"design_effect_from_icc\": float(deff),\n    \"school_size\": float(k),\n    \"sens_school_means_se\": se_means,\n    \"sens_school_means_p\": float(tt.pvalue),\n    \"mean_treated\": float(d.score[d.treated == 1].mean()),\n    \"mean_control\": float(d.score[d.treated == 0].mean()),\n}\nwith open(\"out/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py; 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No randomness.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport statsmodels.formula.api as smf\nfrom scipy import stats\n\nos.makedirs(\"out\", exist_ok=True)\nraw = pd.read_csv(\"data/schools.csv\")\nn_raw = len(raw)\nissues, excl = [], []\nd = raw.copy()\n\ndef step(name, rule, mask, why):\n    global d\n    nb = len(d); d = d[mask(d)].copy(); na = len(d)\n    excl.append(dict(step=name, rule=rule, n_before=nb, n_after=na, n_removed=nb - na, rationale=why))\n\nstep(\"1\", \"drop rows with missing school_id/treated/score\", lambda x: x[[\"school_id\", \"treated\", \"score\"]].notna().all(axis=1), \"cannot be used in OLS\")\nstep(\"2\", \"drop exact duplicate rows\", lambda x: ~x.duplicated(), \"duplicate records\")\nstep(\"3\", \"drop duplicated student_id (keep first)\", lambda x: ~x.student_id.duplicated(), \"student_id must be unique\")\nstep(\"4\", \"treated must be 0/1\", lambda x: x.treated.isin([0, 1]), \"binary treatment\")\n# within-school treatment consistency\nbad = d.groupby(\"school_id\").treated.nunique()\nbad = bad[bad > 1].index\nstep(\"5\", \"drop schools with non-constant treated\", lambda x: ~x.school_id.isin(bad), \"treatment must be constant within school\")\nfor e in excl:\n    if e[\"n_removed\"] > 0:\n        issues.append({\"issue\": f\"{e['rule']}: {e['n_removed']} rows\", \"handling\": \"excluded\"})\npd.DataFrame(excl).to_csv(\"out/exclusions.csv\", index=False)\n\nsizes = d.groupby(\"school_id\").size()\nif sizes.nunique() != 1:\n    issues.append({\"issue\": \"unequal school sizes\", \"handling\": \"kept; student-weighted OLS as defined\"})\n\nG = d.school_id.nunique()\nG1 = int(d.groupby(\"school_id\").treated.first().sum())\nN = len(d)\n\n# Baseline: conventional iid OLS\nm_iid = smf.ols(\"score ~ treated\", data=d).fit()\ntau = float(m_iid.params[\"treated\"]); se_iid = float(m_iid.bse[\"treated\"])\n\n# Main: cluster-robust (CR1, Liang-Zeger with small-sample factor G/(G-1)*(N-1)/(N-K)), t with G-1 df\nm_cl = smf.ols(\"score ~ treated\", data=d).fit(cov_type=\"cluster\", cov_kwds={\"groups\": d.school_id}, use_t=True)\nse_cl = float(m_cl.bse[\"treated\"])\ndf_t = G - 1\ntstat = tau / se_cl\np = float(2 * stats.t.sf(abs(tstat), df_t))\nq = stats.t.ppf(0.975, df_t)\nlo, hi = tau - q * se_cl, tau + q * se_cl\n\n# Sensitivity: school-means Welch/pooled t-test (one obs per school)\nsm = d.groupby(\"school_id\").agg(y=(\"score\", \"mean\"), t=(\"treated\", \"first\"))\ntt = stats.ttest_ind(sm.y[sm.t == 1], sm.y[sm.t == 0], equal_var=True)\nse_means = float((sm.y[sm.t == 1].mean() - sm.y[sm.t == 0].mean()) / tt.statistic)\n\n# ICC of residual (one-way ANOVA estimator, school as random factor)\nd[\"res\"] = m_iid.resid\nk = N / G if sizes.nunique() == 1 else (N - (sizes**2).sum() / N) / (G - 1)\ngm = d.groupby(\"school_id\").res.agg([\"mean\", \"size\"])\nssb = float((gm[\"size\"] * (gm[\"mean\"] - d.res.mean())**2).sum())\nssw = float(((d.res - d.school_id.map(gm[\"mean\"]))**2).sum())\nmsb = ssb / (G - 2)   # one df lost to treatment effect (between-school)\nmsw = ssw / (N - G)\ns2b = max((msb - msw) / k, 0.0)\nicc = s2b / (s2b + msw)\ndeff = 1 + (k - 1) * icc\n\nres = {\n    \"task_id\": \"F2-conf-01\",\n    \"tau_hat\": tau,\n    \"se_iid\": se_iid,\n    \"se_cluster\": se_cl,\n    \"p_value\": p,\n    \"tau_ci_low\": float(lo),\n    \"tau_ci_high\": float(hi),\n    \"effect_significant_5pct\": bool(p < 0.05),\n    \"icc_hat\": float(icc),\n    \"n_schools\": int(G),\n    \"n_treated_schools\": G1,\n    \"n_analysed\": int(N),\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"schools.csv\": int(n_raw)},\n    \"random_seed\": None,\n    # supplementary\n    \"t_stat_cluster\": float(tstat),\n    \"df_cluster\": int(df_t),\n    \"p_value_iid\": float(m_iid.pvalues[\"treated\"]),\n    \"se_ratio_cluster_over_iid\": se_cl / se_iid,\n    \"design_effect_from_icc\": float(deff),\n    \"school_size\": float(k),\n    \"sens_school_means_se\": se_means,\n    \"sens_school_means_p\": float(tt.pvalue),\n    \"mean_treated\": float(d.score[d.treated == 1].mean()),\n    \"mean_control\": float(d.score[d.treated == 0].mean()),\n}\nwith open(\"out/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv","description":"Write and run analysis script"}}]}}
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{"parentUuid":"2e2469b0-1dab-44ca-85fb-51d2941fc69f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoNSkSrzfVAcpDV5ook","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DPGkzeerxxy4mKo22MpSbt","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; rm out/results.json out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp /tmp/r1.json out/results.json && echo IDENTICAL\n~/wp29/venv/bin/python -c \"\nimport json,pandas as pd,math;r=json.load(open('out/results.json'));e=pd.read_csv('out/exclusions.csv')\nprint('ci',r['tau_ci_low']<r['tau_hat']<r['tau_ci_high']);print('se>0',r['se_iid']>0,r['se_cluster']>0)\nprint('excl', r['n_input_rows']['schools.csv']-e.n_removed.sum()==r['n_analysed'])\nprint('tau=diff', abs(r['tau_hat']-(r['mean_treated']-r['mean_control']))<1e-9)\nprint('sig consistent',(r['p_value']<0.05)==r['effect_significant_5pct'], (r['tau_ci_low']>0 or r['tau_ci_high']<0)==r['effect_significant_5pct'])\nprint('sqrt deff',math.sqrt(r['design_effect_from_icc']),'ratio',r['se_ratio_cluster_over_iid'])\nprint('icc in [0,1]',0<=r['icc_hat']<=1)\"","description":"Rerun for reproducibility and check consistency"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3029,"cache_read_input_tokens":7290,"output_tokens":640,"output_tokens_details":{"thinking_tokens":52},"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":3029},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXoNSR7TpTYdvhynPrJp","requestRef":"74973358-68a2-4cc8-be54-39e122b1c340","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e3eff655-1af2-48b9-b273-df84f1133134","timestamp":"2026-10-30T04:35:52.728Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0117","sessionId":"cebd2538-39ea-46c0-bfc7-b96e9cbfd583","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"6e077eb2-2625-4b89-8dcd-967dacac0653","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoNwa3uixH4hb4dC5hu","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019ueauzmno8AAtPyBWqngfC","name":"Bash","input":{"command":"cd out\ncat > state/contract.md <<'EOF'\n# Contract (F2-conf-01)\n- Question: average effect of a school-assigned programme on student test scores, with design-valid uncertainty; decide at 5% (two-sided) whether τ≠0.\n- Estimand τ: difference in student-weighted mean score, treated minus control schools = OLS slope of score on treated (with intercept). Meaningful as a causal effect if school assignment is as-good-as-random (simulated design); inference assumes schools independent, students within school correlated.\n- Uncertainty: treatment varies only between schools → cluster-robust SE at school level (main); iid OLS SE as naïve baseline only.\n- Outputs: out/results.json (task_id, tau_hat, se_iid, se_cluster, p_value, tau_ci_low, tau_ci_high, effect_significant_5pct, icc_hat, n_schools, n_treated_schools, n_analysed, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed), out/report.md (6 sections), out/analysis.py, out/claims.json (incl. 5% claim).\n- Information rule: only data/schools.csv.\n- Invalidity: treatment varying within school, too few clusters (or only one arm), missing/duplicate records not handled, using iid SE for the decision.\nEOF\ncat > state/data_audit.md <<'EOF'\n# Data audit\n- data/schools.csv SHA-256 0f1d8378fff2f0898762feaa08dd94d9ad04156dad6330572d98997c9005c88a (matches bundle.json)\n- 1000 rows × 4 columns: school_id (int, 100–139), student_id (int, 1–1000, unique), treated (int 0/1), score (float, points, 35.72–64.17, mean 50.80, sd 4.48).\n- Missing values: 0 in every column. Exact duplicates: 0. Duplicate student_id: 0.\n- 40 schools, all with exactly 25 students (balanced). treated constant within every school; 20 treated / 20 control schools.\n- No dates (no chronology). No anomalies detected; no availability-rule violations (single permitted input).\n- Exclusions: none needed (ledger in out/exclusions.csv, all steps remove 0).\nEOF\ncat > state/checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (deleted results.json & exclusions.csv) reproduces byte-identical results.json | PASS |\n| CI [tau_ci_low, tau_ci_high] contains tau_hat | PASS |\n| se_iid > 0, se_cluster > 0 | PASS |\n| n_input_rows − Σ n_removed (exclusions.csv) = n_analysed (1000−0=1000) | PASS |\n| tau_hat = mean_treated − mean_control | PASS |\n| effect_significant_5pct consistent with p_value<0.05 and CI excluding 0 | PASS |\n| Magnitude: se_cluster/se_iid = 2.58 ≈ sqrt(design effect 1+(m−1)ICC)=2.60 | PASS |\n| icc_hat in [0,1] | PASS |\n| Sensitivity: school-means t-test (df 38) gives SE 0.738, p 0.201 — same decision | PASS |\nEOF\ncat > state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0 passed: contract written.\n- 2026-10-30 P1 passed: data clean, no exclusions.\n- 2026-10-30 P2 passed: baseline iid OLS SE computed (se_iid).\n- 2026-10-30 P3 passed: decision — CR1 cluster-robust SE by school, t(G−1=39) reference; ICC via one-way ANOVA on OLS residuals with G−2 between df. No randomness (random_seed null).\n- 2026-10-30 P4 passed: reproducibility and consistency checks all pass.\n- 2026-10-30 P5 passed: report and claims written.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The estimated programme effect is 0.96 points (treated minus control student-weighted mean score).\",\"results_keys\":[\"tau_hat\",\"mean_treated\",\"mean_control\"],\"status\":\"supported\",\"caveat\":\"Point estimate only; imprecise given 40 schools.\"},\n {\"id\":\"C2\",\"text\":\"With school-clustered standard errors the effect is not statistically different from zero at the 5% level (p≈0.195; 95% CI −0.51 to 2.44).\",\"results_keys\":[\"se_cluster\",\"p_value\",\"tau_ci_low\",\"tau_ci_high\",\"effect_significant_5pct\"],\"status\":\"supported\",\"caveat\":\"Failure to reject is not evidence of no effect; the CI includes effects up to ~2.4 points.\"},\n {\"id\":\"C3\",\"text\":\"The naive iid OLS standard error understates uncertainty by a factor of about 2.6 and would wrongly suggest significance (p≈0.0007).\",\"results_keys\":[\"se_iid\",\"se_cluster\",\"se_ratio_cluster_over_iid\",\"p_value_iid\"],\"status\":\"supported\",\"caveat\":\"The iid SE is reported only as a baseline and is invalid for this design.\"},\n {\"id\":\"C4\",\"text\":\"About 24% of residual score variance lies between schools (ICC≈0.24), implying a design effect of about 6.8 with 25 students per school.\",\"results_keys\":[\"icc_hat\",\"design_effect_from_icc\",\"school_size\"],\"status\":\"supported\",\"caveat\":\"ANOVA estimator; ICC estimated from 40 clusters is itself uncertain.\"},\n {\"id\":\"C5\",\"text\":\"The conclusion is robust to analysing school means with a two-sample t-test (p≈0.20).\",\"results_keys\":[\"sens_school_means_se\",\"sens_school_means_p\"],\"status\":\"supported\",\"caveat\":\"Equivalent to cluster inference in this balanced design.\"}\n]\nEOF\ncat > report.md <<'EOF'\n# F2-conf-01 — School-level programme effect on student scores\n\n## Question\nA programme was assigned to whole schools. We estimate τ, the difference between the student-weighted mean score in treated and control schools (OLS slope of `score` on `treated` with an intercept), give an uncertainty measure that respects the school-level design, and test H0: τ = 0 at the 5% level (two-sided).\n\n## Data and exclusions\nInput: `data/schools.csv`, 1000 rows (`n_input_rows`), simulated data. Audit (`out/state/data_audit.md`): no missing values, no duplicates, unique student IDs, 40 schools (`n_schools`) of exactly 25 students (`school_size`), treatment constant within school, 20 treated schools (`n_treated_schools`). No rows excluded (`out/exclusions.csv`); `n_analysed` = 1000. `data_issues` is empty.\n\n## Method\n- Point estimate: OLS of score on treated with intercept (`tau_hat`).\n- Naïve baseline: conventional homoskedastic iid OLS SE (`se_iid`).\n- Main uncertainty: cluster-robust (Liang–Zeger, CR1 small-sample correction) SE clustered by `school_id` (`se_cluster`). Test statistic and 95% CI use a t distribution with G−1 = 39 df (`df_cluster`), which is appropriate with a moderate number of clusters.\n- ICC: one-way random-effects ANOVA estimator on the OLS residuals, with G−2 between-school df (one lost to the treatment effect) (`icc_hat`).\n- Sensitivity: two-sample t-test on the 40 school means (`sens_school_means_se`, `sens_school_means_p`).\n- Deterministic; no randomness (`random_seed` = null).\n\n## Results\n- `tau_hat` = 0.961 points (treated mean 51.28 `mean_treated` vs control 50.32 `mean_control`).\n- `se_cluster` = 0.729; t = 1.32 (`t_stat_cluster`); `p_value` = 0.195.\n- 95% CI: [`tau_ci_low` = −0.513, `tau_ci_high` = 2.436].\n- `effect_significant_5pct` = false: the data do not support a non-zero effect at the 5% level.\n- `icc_hat` = 0.240; implied design effect 6.77 (`design_effect_from_icc`).\n\n## Uncertainty\nThe naïve `se_iid` = 0.282 would give p = 0.0007 (`p_value_iid`) and a spurious \"significant\" result. Because the programme varies only between schools and students in a school share school-level shocks, the effective sample is closer to 40 schools than 1000 students: the clustered SE is 2.58× larger (`se_ratio_cluster_over_iid`), consistent with √(design effect) ≈ 2.60. The school-means t-test gives the same conclusion (SE 0.738, p = 0.201). Verification checks are listed in `out/state/checks.md`.\n\n## Limitations and open issues\n- Not rejecting H0 does not mean the effect is zero; the CI is compatible with effects from about −0.5 to +2.4 points.\n- With 40 clusters, CR1 with t(39) is a standard and reasonably accurate choice; wild-cluster bootstrap was not used (not required; balanced design makes CR1 ≈ school-means t-test, which was checked).\n- Causal interpretation relies on the school assignment being as-good-as-random, as in the simulated design; no covariates were available.\n- The data are synthetic.\nEOF\ncat > state/phases.json <<'EOF'\n{\n \"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read; contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches bundle.json\",\"no missing/duplicates\",\"treatment constant within school\",\"exclusion ledger sums 1000-0=1000\"],\"notes\":\"no exclusions\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"se_iid computed = 0.282\"],\"notes\":\"naive baseline\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py runs, writes results.json, no randomness\"],\"notes\":\"CR1 cluster SE, t(39)\"},\n \"P4\":{\"status\":\"passed\",\"checks\":[\"clean rerun byte-identical\",\"CI contains estimate\",\"SE positive\",\"exclusions sum\",\"tau=mean diff\",\"significance flag consistent\",\"se ratio ~ sqrt(deff)\",\"school-means sensitivity agrees\"],\"notes\":\"see checks.md\"},\n \"P5\":{\"status\":\"passed\",\"checks\":[\"report.md has 6 required sections\",\"claims.json keys exist in results.json\"],\"notes\":\"\"}\n}\nEOF\n~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('results.json'));c=json.load(open('claims.json'))\nprint(all(k in r for x in c for k in x['results_keys']))\"; grep '^## ' report.md","description":"Write state, claims, report files and validate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":721,"cache_read_input_tokens":10319,"output_tokens":3813,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":721},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXoNwJgh3B5vokwjTfyC","requestRef":"a0cc1848-d258-4b1d-a4fc-72b16d99d8c8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b9a4894d-0828-4bc7-9e3d-bc8a87ecdd92","timestamp":"2026-10-30T04:36:25.359Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0117","sessionId":"cebd2538-39ea-46c0-bfc7-b96e9cbfd583","version":"2.1.314","gitBranch":"HEAD"}
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Meaningful as a causal effect if school assignment is as-good-as-random (simulated design); inference assumes schools independent, students within school correlated.\n- Uncertainty: treatment varies only between schools → cluster-robust SE at school level (main); iid OLS SE as naïve baseline only.\n- Outputs: out/results.json (task_id, tau_hat, se_iid, se_cluster, p_value, tau_ci_low, tau_ci_high, effect_significant_5pct, icc_hat, n_schools, n_treated_schools, n_analysed, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed), out/report.md (6 sections), out/analysis.py, out/claims.json (incl. 5% claim).\n- Information rule: only data/schools.csv.\n- Invalidity: treatment varying within school, too few clusters (or only one arm), missing/duplicate records not handled, using iid SE for the decision.\nEOF\ncat > state/data_audit.md <<'EOF'\n# Data audit\n- data/schools.csv SHA-256 0f1d8378fff2f0898762feaa08dd94d9ad04156dad6330572d98997c9005c88a (matches bundle.json)\n- 1000 rows × 4 columns: school_id (int, 100–139), student_id (int, 1–1000, unique), treated (int 0/1), score (float, points, 35.72–64.17, mean 50.80, sd 4.48).\n- Missing values: 0 in every column. Exact duplicates: 0. Duplicate student_id: 0.\n- 40 schools, all with exactly 25 students (balanced). treated constant within every school; 20 treated / 20 control schools.\n- No dates (no chronology). No anomalies detected; no availability-rule violations (single permitted input).\n- Exclusions: none needed (ledger in out/exclusions.csv, all steps remove 0).\nEOF\ncat > state/checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (deleted results.json & exclusions.csv) reproduces byte-identical results.json | PASS |\n| CI [tau_ci_low, tau_ci_high] contains tau_hat | PASS |\n| se_iid > 0, se_cluster > 0 | PASS |\n| n_input_rows − Σ n_removed (exclusions.csv) = n_analysed (1000−0=1000) | PASS |\n| tau_hat = mean_treated − mean_control | PASS |\n| effect_significant_5pct consistent with p_value<0.05 and CI excluding 0 | PASS |\n| Magnitude: se_cluster/se_iid = 2.58 ≈ sqrt(design effect 1+(m−1)ICC)=2.60 | PASS |\n| icc_hat in [0,1] | PASS |\n| Sensitivity: school-means t-test (df 38) gives SE 0.738, p 0.201 — same decision | PASS |\nEOF\ncat > state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0 passed: contract written.\n- 2026-10-30 P1 passed: data clean, no exclusions.\n- 2026-10-30 P2 passed: baseline iid OLS SE computed (se_iid).\n- 2026-10-30 P3 passed: decision — CR1 cluster-robust SE by school, t(G−1=39) reference; ICC via one-way ANOVA on OLS residuals with G−2 between df. No randomness (random_seed null).\n- 2026-10-30 P4 passed: reproducibility and consistency checks all pass.\n- 2026-10-30 P5 passed: report and claims written.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The estimated programme effect is 0.96 points (treated minus control student-weighted mean score).\",\"results_keys\":[\"tau_hat\",\"mean_treated\",\"mean_control\"],\"status\":\"supported\",\"caveat\":\"Point estimate only; imprecise given 40 schools.\"},\n {\"id\":\"C2\",\"text\":\"With school-clustered standard errors the effect is not statistically different from zero at the 5% level (p≈0.195; 95% CI −0.51 to 2.44).\",\"results_keys\":[\"se_cluster\",\"p_value\",\"tau_ci_low\",\"tau_ci_high\",\"effect_significant_5pct\"],\"status\":\"supported\",\"caveat\":\"Failure to reject is not evidence of no effect; the CI includes effects up to ~2.4 points.\"},\n {\"id\":\"C3\",\"text\":\"The naive iid OLS standard error understates uncertainty by a factor of about 2.6 and would wrongly suggest significance (p≈0.0007).\",\"results_keys\":[\"se_iid\",\"se_cluster\",\"se_ratio_cluster_over_iid\",\"p_value_iid\"],\"status\":\"supported\",\"caveat\":\"The iid SE is reported only as a baseline and is invalid for this design.\"},\n {\"id\":\"C4\",\"text\":\"About 24% of residual score variance lies between schools (ICC≈0.24), implying a design effect of about 6.8 with 25 students per school.\",\"results_keys\":[\"icc_hat\",\"design_effect_from_icc\",\"school_size\"],\"status\":\"supported\",\"caveat\":\"ANOVA estimator; ICC estimated from 40 clusters is itself uncertain.\"},\n {\"id\":\"C5\",\"text\":\"The conclusion is robust to analysing school means with a two-sample t-test (p≈0.20).\",\"results_keys\":[\"sens_school_means_se\",\"sens_school_means_p\"],\"status\":\"supported\",\"caveat\":\"Equivalent to cluster inference in this balanced design.\"}\n]\nEOF\ncat > report.md <<'EOF'\n# F2-conf-01 — School-level programme effect on student scores\n\n## Question\nA programme was assigned to whole schools. We estimate τ, the difference between the student-weighted mean score in treated and control schools (OLS slope of `score` on `treated` with an intercept), give an uncertainty measure that respects the school-level design, and test H0: τ = 0 at the 5% level (two-sided).\n\n## Data and exclusions\nInput: `data/schools.csv`, 1000 rows (`n_input_rows`), simulated data. Audit (`out/state/data_audit.md`): no missing values, no duplicates, unique student IDs, 40 schools (`n_schools`) of exactly 25 students (`school_size`), treatment constant within school, 20 treated schools (`n_treated_schools`). No rows excluded (`out/exclusions.csv`); `n_analysed` = 1000. `data_issues` is empty.\n\n## Method\n- Point estimate: OLS of score on treated with intercept (`tau_hat`).\n- Naïve baseline: conventional homoskedastic iid OLS SE (`se_iid`).\n- Main uncertainty: cluster-robust (Liang–Zeger, CR1 small-sample correction) SE clustered by `school_id` (`se_cluster`). Test statistic and 95% CI use a t distribution with G−1 = 39 df (`df_cluster`), which is appropriate with a moderate number of clusters.\n- ICC: one-way random-effects ANOVA estimator on the OLS residuals, with G−2 between-school df (one lost to the treatment effect) (`icc_hat`).\n- Sensitivity: two-sample t-test on the 40 school means (`sens_school_means_se`, `sens_school_means_p`).\n- Deterministic; no randomness (`random_seed` = null).\n\n## Results\n- `tau_hat` = 0.961 points (treated mean 51.28 `mean_treated` vs control 50.32 `mean_control`).\n- `se_cluster` = 0.729; t = 1.32 (`t_stat_cluster`); `p_value` = 0.195.\n- 95% CI: [`tau_ci_low` = −0.513, `tau_ci_high` = 2.436].\n- `effect_significant_5pct` = false: the data do not support a non-zero effect at the 5% level.\n- `icc_hat` = 0.240; implied design effect 6.77 (`design_effect_from_icc`).\n\n## Uncertainty\nThe naïve `se_iid` = 0.282 would give p = 0.0007 (`p_value_iid`) and a spurious \"significant\" result. Because the programme varies only between schools and students in a school share school-level shocks, the effective sample is closer to 40 schools than 1000 students: the clustered SE is 2.58× larger (`se_ratio_cluster_over_iid`), consistent with √(design effect) ≈ 2.60. The school-means t-test gives the same conclusion (SE 0.738, p = 0.201). Verification checks are listed in `out/state/checks.md`.\n\n## Limitations and open issues\n- Not rejecting H0 does not mean the effect is zero; the CI is compatible with effects from about −0.5 to +2.4 points.\n- With 40 clusters, CR1 with t(39) is a standard and reasonably accurate choice; wild-cluster bootstrap was not used (not required; balanced design makes CR1 ≈ school-means t-test, which was checked).\n- Causal interpretation relies on the school assignment being as-good-as-random, as in the simulated design; no covariates were available.\n- The data are synthetic.\nEOF\ncat > state/phases.json <<'EOF'\n{\n \"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read; contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches bundle.json\",\"no missing/duplicates\",\"treatment constant within school\",\"exclusion ledger sums 1000-0=1000\"],\"notes\":\"no exclusions\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"se_iid computed = 0.282\"],\"notes\":\"naive baseline\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py runs, writes results.json, no randomness\"],\"notes\":\"CR1 cluster SE, t(39)\"},\n \"P4\":{\"status\":\"passed\",\"checks\":[\"clean rerun byte-identical\",\"CI contains estimate\",\"SE positive\",\"exclusions sum\",\"tau=mean diff\",\"significance flag consistent\",\"se ratio ~ sqrt(deff)\",\"school-means sensitivity agrees\"],\"notes\":\"see checks.md\"},\n \"P5\":{\"status\":\"passed\",\"checks\":[\"report.md has 6 required sections\",\"claims.json keys exist in results.json\"],\"notes\":\"\"}\n}\nEOF\n~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('results.json'));c=json.load(open('claims.json'))\nprint(all(k in r for x in c for k in x['results_keys']))\"; grep '^## ' report.md","description":"Write state, claims, report files and validate"}}]}}
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{"parentUuid":"fb45fbeb-258c-44b0-8b4f-73708d59101a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoRH3PBBcuLZUpoPKzX","type":"message","role":"assistant","content":[{"type":"text","text":"The programme's estimated effect is about +0.96 points, but it is not statistically different from zero at the 5% level once uncertainty is measured at the school level (p = 0.195).\n\n**Data:** `data/schools.csv` has 1,000 students in 40 schools of 25 students each; 20 schools are treated and 20 are controls. It had no missing values or duplicates, and treatment never varied within a school, so no rows were dropped.\n\n**Results** (all in `out/results.json`):\n\n| Quantity | Value |\n|---|---|\n| Estimated effect (`tau_hat`) | 0.961 points |\n| Naïve standard error, assuming independent students (`se_iid`) | 0.282 |\n| School-clustered standard error, the main measure (`se_cluster`) | 0.729 |\n| Test statistic | t = 1.32, using a t distribution with 39 degrees of freedom |\n| p-value | 0.195 |\n| 95% confidence interval | −0.51 to 2.44 points |\n| Significant at 5%? (`effect_significant_5pct`) | false |\n| Share of score variation between schools (`icc_hat`) | 0.24 |\n\n- **The naïve standard error would mislead.** It gives p ≈ 0.0007, a false \"significant\" result. The clustered standard error is 2.58 times larger, which matches what the 0.24 between-school share predicts (about 2.60).\n- **The conclusion holds up under a second method.** Comparing the 40 school averages directly with a t-test gives p ≈ 0.20, the same answer.\n\nRe-running `out/analysis.py` from scratch reproduces `out/results.json` byte for byte, and all the consistency checks passed. No random numbers are used, so there is no seed.\n\n**Limitations:**\n- Not rejecting zero doesn't show the effect is zero: the interval still allows effects up to about +2.4 points.\n- I didn't run a cluster bootstrap. The task didn't require one, and the school-average check gives the same answer.\n- Reading the estimate as a causal effect assumes schools were effectively assigned at random.\n- The data are simulated.\n\nEverything is in `out/`:\n- `results.json`\n- `report.md`\n- `analysis.py`\n- `claims.json`\n- `exclusions.csv`\n- `state/` (contract, data audit, checks, log, phase ledger)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3858,"cache_read_input_tokens":11040,"output_tokens":777,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":3858},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXoRGnzkFTzvvFPUHfdj","requestRef":"0e2676b0-49e2-4263-9330-30cfba64fbe1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"565cab49-8648-4c3f-aa12-f341214f833a","timestamp":"2026-10-30T04:36:33.422Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0117/out","sessionId":"cebd2538-39ea-46c0-bfc7-b96e9cbfd583","version":"2.1.314","gitBranch":"HEAD"}
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