1{"type":"atis-latch","atis":"","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T04:16:19.110Z","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","content":"Review"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T04:16:19.115Z","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4"}4{"parentUuid":null,"isSidechain":false,"promptId":"3e720f54-456b-45b7-b91d-31cbf750238b","type":"user","message":{"role":"user","content":"Review"},"uuid":"3b7588d3-5e9e-4c42-87a8-ea64e46ff4ba","timestamp":"2026-10-30T04:16:19.119Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0050","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","shapeHash":"d79ca2fe63fd65b75c7832bac2805c6639fa2d5442e00a9a7ef1763668474b77","timestamp":"2026-10-30T04:16:19.134Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RC0050\nDate: 2026-10-30\n\nYou are an independent scientific reviewer. In the current working directory, `TASK.md` describes a bounded scientific task and the directory `out/` contains the deliverables submitted by another assistant (you have not seen how they were produced and you must not look for their working transcript). There is no network access. Use `~/wp29/venv/bin/python` if you need to re-run or test code. Do not modify any file under `out/` except that you must create `out/review/review.json`.\n\nReview the deliverables against `TASK.md` only. Specifically check:\n1. Does `out/results.json` contain every required key with legitimately computed values? Re-run `out/analysis.py` in a scratch copy if needed and compare.\n2. Are the information-availability rules of `TASK.md` respected by the code (for example no use of future information, no use of forbidden inputs)? Read the code to verify; do not trust the report.\n3. Are the central claims in `out/report.md` and `out/claims.json` supported by the numbers in `out/results.json`, with appropriate uncertainty and caveats? Flag any claim that is stronger than the evidence.\n4. Are counts, exclusions, units, signs and magnitudes internally consistent? Are baseline comparisons present when the task requires them?\n5. Are limitations stated honestly? Is anything declared complete that is not?\n\nYou have no reference answers; do not guess what the \"right\" number is. Report only issues you can justify from the files. Write `out/review/review.json` as {\"verdict\": \"accept\"|\"revise\", \"issues\": [{\"id\": \"R1\", \"severity\": \"critical\"|\"major\"|\"minor\", \"location\": \"<file or key>\", \"issue\": \"...\", \"suggested_fix\": \"...\"}], \"summary\": \"...\"}. Use \"revise\" if there is at least one critical or major issue. Be concise and specific. End with a one-paragraph plain-text summary.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. Never use words like \"complex\" or \"risk\" in the description - just describe what it does.\n\nSay what the command does in plain words: do not echo the command's text, its flags, or file paths - the user reads this description, often without seeing the command.\n\nFor simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):\n- ls → \"List files in current directory\"\n- git status → \"Show working tree status\"\n- npm install → \"Install package dependencies\"\n\nFor commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:\n- find . -name \"*.tmp\" -exec rm {} \\; → \"Find and delete all .tmp files recursively\"\n- git reset --hard origin/main → \"Discard all local changes and match remote main\"\n- curl -s url | jq '.data[]' → \"Fetch JSON from URL and extract data array elements\"","type":"string"},"dangerouslyDisableSandbox":{"description":"Set this to true to dangerously override sandbox mode and run commands without sandboxing.","type":"boolean"}},"required":["command"],"additionalProperties":false}},{"name":"Edit","description":"modify file contents in place","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to modify","type":"string"},"old_string":{"description":"The text to replace","type":"string"},"new_string":{"description":"The text to replace it with (must be different from old_string)","type":"string"},"replace_all":{"description":"Replace all occurrences of old_string (default false)","default":false,"type":"boolean"}},"required":["file_path","old_string","new_string"],"additionalProperties":false}},{"name":"Read","description":"read files, images, PDFs, notebooks","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to read","type":"string"},"offset":{"description":"The line number to start reading from. Only provide if the file is too large to read at once","type":"integer","minimum":0,"maximum":9007199254740991},"limit":{"description":"The number of lines to read. Only provide if the file is too large to read at once.","type":"integer","exclusiveMinimum":0,"maximum":9007199254740991},"pages":{"description":"Page range for PDF files (e.g., \"1-5\", \"3\", \"10-20\"). Only applicable to PDF files. Maximum 20 pages per request.","type":"string"},"allow_large":{"description":"Set to true to read a text file, or a line range of one, that is over the usual size limits, up to what still fits in your context. Only use this when you genuinely need all of it or the user asked for the whole file; otherwise read it in parts with offset and limit, or search it.","type":"boolean"}},"required":["file_path"],"additionalProperties":false}}]}}6{"type":"api-request-blob","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","hash":"d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9","message":{"role":"system","content":[],"output_config":{"effort":"medium"}}}7{"type":"api-request","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","id":"a31c0452-4986-4ec8-9b7a-47b101016267","timestamp":"2026-10-30T04:16:19.134Z","version":"2.1.314","querySource":"sdk","shapeHash":"d79ca2fe63fd65b75c7832bac2805c6639fa2d5442e00a9a7ef1763668474b77","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"keep":0,"tail":["99833769e4310fc124186eb9e97416cea64dc8ececb015cbe8928bf0c2bd9984","d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",0,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"bf8ecb4f3bd49530a75b92bf4a21e52935deae44da80e18801d25362d50bf2d9"}8{"parentUuid":"3b7588d3-5e9e-4c42-87a8-ea64e46ff4ba","isSidechain":false,"attachment":{"type":"session_context","context":{}},"type":"attachment","uuid":"514bf9b8-4321-45d4-99cc-0f9e3ef994df","timestamp":"2026-10-30T04:16:19.125Z","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0050","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","version":"2.1.314","gitBranch":"HEAD"}9{"parentUuid":"514bf9b8-4321-45d4-99cc-0f9e3ef994df","isSidechain":false,"attachment":{"type":"date","date":"2026-10-30"},"type":"attachment","uuid":"03f3975c-3873-41e7-bfd2-3becf8bb0d78","timestamp":"2026-10-30T04:16:19.125Z","rendered":[{"content":"<system-reminder>\nToday's date is 2026-10-30.\n</system-reminder>"}],"renderedRole":"system","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0050","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","version":"2.1.314","gitBranch":"HEAD"}10{"parentUuid":"03f3975c-3873-41e7-bfd2-3becf8bb0d78","isSidechain":false,"attachment":{"type":"remote_session_change","url":null,"commit":"Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>","pr":"🤖 Generated with [Claude Code](https://claude.com/claude-code)","sendUserFileHint":false,"managedCommit":false,"managedPr":false},"type":"attachment","uuid":"629c5f62-0cf4-4fa7-8e9e-961fea019a18","timestamp":"2026-10-30T04:16:19.126Z","rendered":[{"content":"<system-reminder>\nAttribution for git commits and pull requests you create from here on (this replaces Claude Code's own earlier attribution guidance, such as a previous copy of this reminder; the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0050","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4"}12{"parentUuid":"629c5f62-0cf4-4fa7-8e9e-961fea019a18","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmtPv9WjLdER6jDtWeN","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Bn9SrMz4dTay267dRpkfKZ","name":"Bash","input":{"command":"cat TASK.md; ls -la out; cat out/results.json; cat out/claims.json"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":716,"cache_read_input_tokens":1504,"output_tokens":76,"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":716},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmtPcoAFfEcvgssG2rL","requestRef":"a31c0452-4986-4ec8-9b7a-47b101016267","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"49b013dd-27b9-4d21-ac3b-9adb2a14c701","timestamp":"2026-10-30T04:16:21.439Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0050","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"49b013dd-27b9-4d21-ac3b-9adb2a14c701","isSidechain":false,"promptId":"3e720f54-456b-45b7-b91d-31cbf750238b","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Bn9SrMz4dTay267dRpkfKZ","type":"tool_result","content":"# F2-conf-03 — Monte Carlo coverage of OLS confidence intervals in a small heteroskedastic sample\n\n## Scientific question\nHow well do nominal 95% confidence intervals for an OLS slope perform in a small sample with heteroskedastic errors? Consider the fixed-design linear model y_i = α + β x_i + σ_i ε_i, i = 1, …, 30, with α = 1, β = 0.5, ε_i independent standard normal, and x_i and σ_i fixed at the values given in `data/design.csv` (the same 30 values in every replication; only ε is redrawn). The estimands are (a) the true coverage probability of the interval β̂ ± t_{0.975}(28) · se_conv, where se_conv is the conventional OLS standard error (residual variance with divisor n − 2); (b) the true coverage probability of the interval β̂ ± t_{0.975}(28) · se_HC1, where se_HC1 is the heteroskedasticity-robust HC1 standard error (White's HC0 sandwich multiplied by n/(n − 2)); (c) the exact finite-sample standard deviation of β̂ under this design; (d) the expected value of se_conv. Estimate (a), (b) and (d) by Monte Carlo with exactly R = 2000 replications and a fixed seed, compute (c) analytically, and conclude whether the conventional interval attains its nominal coverage.\n\n## Inputs\n- `data/design.csv` — the fixed design (a synthetic design generated for this exercise; not observed data). Columns: `i` (observation index 1–30), `x` (regressor value, dimensionless), `sigma` (standard deviation of the error for observation i, dimensionless). 30 rows.\n\n## Information-availability rules\n- Use only `data/design.csv`; the model constants (α = 1, β = 0.5, n = 30, R = 2000, nominal level 95%, t(28) critical value) are given above. Do not redraw x or σ.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | – | \"F2-conf-03\" |\n| `coverage_conventional` | number | proportion | share of the 2000 replications in which the conventional interval contains β = 0.5 — the naïve baseline |\n| `coverage_hc1` | number | proportion | share of the 2000 replications in which the HC1 interval contains β = 0.5 |\n| `sd_beta_exact` | number | – | exact standard deviation of β̂ under the design (analytical, no simulation) |\n| `sd_beta_mc` | number | – | sample standard deviation (divisor R − 1) of the 2000 simulated β̂ |\n| `mean_se_conventional` | number | – | average of se_conv over the 2000 replications |\n| `conventional_ci_undercovers` | boolean | – | true if and only if `coverage_conventional` is below 0.95 by more than three Monte Carlo standard errors (binomial standard error at R = 2000) |\n| `n_replications` | integer | – | number of replications (2000) |\n| `n_obs_per_replication` | integer | – | sample size per replication (30) |\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 | – | {\"design.csv\": rows read} |\n| `random_seed` | integer | – | the seed used for the simulation (required; the simulation must be reproducible) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty (include the Monte Carlo standard error of each coverage estimate); 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 (same seed, same numbers).\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 conventional interval attains its nominal coverage.\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; the simulation itself should take seconds.\ntotal 40\ndrwxr-xr-x@ 9 simon-pierreboucher staff 288 oct. 30 00:16 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 00:15 ..\n-rw-r--r--@ 1 simon-pierreboucher staff 2704 oct. 30 00:15 analysis.py\n-rw-r--r--@ 1 simon-pierreboucher staff 1228 oct. 30 00:16 claims.json\n-rw-r--r--@ 1 simon-pierreboucher staff 152 oct. 30 00:15 exclusions.csv\n-rw-r--r--@ 1 simon-pierreboucher staff 3246 oct. 30 00:16 report.md\n-rw-r--r--@ 1 simon-pierreboucher staff 819 oct. 30 00:15 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 00:16 review\ndrwxr-xr-x@ 8 simon-pierreboucher staff 256 oct. 30 00:15 state\n{\n \"task_id\": \"F2-conf-03\",\n \"coverage_conventional\": 0.844,\n \"coverage_hc1\": 0.902,\n \"sd_beta_exact\": 0.2679805928302833,\n \"sd_beta_mc\": 0.2696473708309126,\n \"mean_se_conventional\": 0.19026231012152658,\n \"conventional_ci_undercovers\": true,\n \"n_replications\": 2000,\n \"n_obs_per_replication\": 30,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [],\n \"n_input_rows\": {\n \"design.csv\": 30\n },\n \"random_seed\": 20261030,\n \"t_critical\": 2.048407141795244,\n \"mcse_coverage_conventional\": 0.00811369213120636,\n \"mcse_coverage_hc1\": 0.006648157639526908,\n \"mcse_at_nominal\": 0.004873397172404485,\n \"undercoverage_threshold\": 0.9353798084827865,\n \"mean_se_hc1\": 0.24080977065103962,\n \"sqrt_E_s2_over_Sxx_analytic\": 0.19318017740129878,\n \"baseline_coverage_conventional\": 0.844\n}[\n {\"id\":\"C1\",\"text\":\"The conventional 95% interval does not attain nominal coverage: estimated coverage 0.844, more than three MC standard errors below 0.95.\",\"results_keys\":[\"coverage_conventional\",\"conventional_ci_undercovers\",\"mcse_coverage_conventional\",\"undercoverage_threshold\"],\"status\":\"supported\",\"caveat\":\"Conditional on this fixed design, normal errors and seed; MC SE about 0.008.\"},\n {\"id\":\"C2\",\"text\":\"The HC1 interval covers better (0.902) than the conventional interval but still falls below 95%.\",\"results_keys\":[\"coverage_hc1\",\"mcse_coverage_hc1\",\"mcse_at_nominal\"],\"status\":\"supported\",\"caveat\":\"MC SE about 0.007; small-sample HC variants not evaluated.\"},\n {\"id\":\"C3\",\"text\":\"The exact SD of the slope estimator is 0.268, and the Monte Carlo SD (0.270) agrees with it.\",\"results_keys\":[\"sd_beta_exact\",\"sd_beta_mc\"],\"status\":\"supported\",\"caveat\":\"Exact value is analytic, given the design.\"},\n {\"id\":\"C4\",\"text\":\"The conventional SE is biased downward: its mean (0.190) is about 29% below the true SD of the slope estimator.\",\"results_keys\":[\"mean_se_conventional\",\"sd_beta_exact\",\"sqrt_E_s2_over_Sxx_analytic\"],\"status\":\"supported\",\"caveat\":\"MC average; consistent with the analytic sqrt(E[s^2]/Sxx).\"}\n]","is_error":false}]},"uuid":"b4103523-f39a-4979-aa1e-98efb965f1f6","timestamp":"2026-10-30T04:16:21.521Z","toolUseResult":{"stdout":"# F2-conf-03 — Monte Carlo coverage of OLS confidence intervals in a small heteroskedastic sample\n\n## Scientific question\nHow well do nominal 95% confidence intervals for an OLS slope perform in a small sample with heteroskedastic errors? Consider the fixed-design linear model y_i = α + β x_i + σ_i ε_i, i = 1, …, 30, with α = 1, β = 0.5, ε_i independent standard normal, and x_i and σ_i fixed at the values given in `data/design.csv` (the same 30 values in every replication; only ε is redrawn). The estimands are (a) the true coverage probability of the interval β̂ ± t_{0.975}(28) · se_conv, where se_conv is the conventional OLS standard error (residual variance with divisor n − 2); (b) the true coverage probability of the interval β̂ ± t_{0.975}(28) · se_HC1, where se_HC1 is the heteroskedasticity-robust HC1 standard error (White's HC0 sandwich multiplied by n/(n − 2)); (c) the exact finite-sample standard deviation of β̂ under this design; (d) the expected value of se_conv. Estimate (a), (b) and (d) by Monte Carlo with exactly R = 2000 replications and a fixed seed, compute (c) analytically, and conclude whether the conventional interval attains its nominal coverage.\n\n## Inputs\n- `data/design.csv` — the fixed design (a synthetic design generated for this exercise; not observed data). Columns: `i` (observation index 1–30), `x` (regressor value, dimensionless), `sigma` (standard deviation of the error for observation i, dimensionless). 30 rows.\n\n## Information-availability rules\n- Use only `data/design.csv`; the model constants (α = 1, β = 0.5, n = 30, R = 2000, nominal level 95%, t(28) critical value) are given above. Do not redraw x or σ.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | – | \"F2-conf-03\" |\n| `coverage_conventional` | number | proportion | share of the 2000 replications in which the conventional interval contains β = 0.5 — the naïve baseline |\n| `coverage_hc1` | number | proportion | share of the 2000 replications in which the HC1 interval contains β = 0.5 |\n| `sd_beta_exact` | number | – | exact standard deviation of β̂ under the design (analytical, no simulation) |\n| `sd_beta_mc` | number | – | sample standard deviation (divisor R − 1) of the 2000 simulated β̂ |\n| `mean_se_conventional` | number | – | average of se_conv over the 2000 replications |\n| `conventional_ci_undercovers` | boolean | – | true if and only if `coverage_conventional` is below 0.95 by more than three Monte Carlo standard errors (binomial standard error at R = 2000) |\n| `n_replications` | integer | – | number of replications (2000) |\n| `n_obs_per_replication` | integer | – | sample size per replication (30) |\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 | – | {\"design.csv\": rows read} |\n| `random_seed` | integer | – | the seed used for the simulation (required; the simulation must be reproducible) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty (include the Monte Carlo standard error of each coverage estimate); 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 (same seed, same numbers).\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 conventional interval attains its nominal coverage.\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; the simulation itself should take seconds.\ntotal 40\ndrwxr-xr-x@ 9 simon-pierreboucher staff 288 oct. 30 00:16 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 00:15 ..\n-rw-r--r--@ 1 simon-pierreboucher staff 2704 oct. 30 00:15 analysis.py\n-rw-r--r--@ 1 simon-pierreboucher staff 1228 oct. 30 00:16 claims.json\n-rw-r--r--@ 1 simon-pierreboucher staff 152 oct. 30 00:15 exclusions.csv\n-rw-r--r--@ 1 simon-pierreboucher staff 3246 oct. 30 00:16 report.md\n-rw-r--r--@ 1 simon-pierreboucher staff 819 oct. 30 00:15 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 00:16 review\ndrwxr-xr-x@ 8 simon-pierreboucher staff 256 oct. 30 00:15 state\n{\n \"task_id\": \"F2-conf-03\",\n \"coverage_conventional\": 0.844,\n \"coverage_hc1\": 0.902,\n \"sd_beta_exact\": 0.2679805928302833,\n \"sd_beta_mc\": 0.2696473708309126,\n \"mean_se_conventional\": 0.19026231012152658,\n \"conventional_ci_undercovers\": true,\n \"n_replications\": 2000,\n \"n_obs_per_replication\": 30,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [],\n \"n_input_rows\": {\n \"design.csv\": 30\n },\n \"random_seed\": 20261030,\n \"t_critical\": 2.048407141795244,\n \"mcse_coverage_conventional\": 0.00811369213120636,\n \"mcse_coverage_hc1\": 0.006648157639526908,\n \"mcse_at_nominal\": 0.004873397172404485,\n \"undercoverage_threshold\": 0.9353798084827865,\n \"mean_se_hc1\": 0.24080977065103962,\n \"sqrt_E_s2_over_Sxx_analytic\": 0.19318017740129878,\n \"baseline_coverage_conventional\": 0.844\n}[\n {\"id\":\"C1\",\"text\":\"The conventional 95% interval does not attain nominal coverage: estimated coverage 0.844, more than three MC standard errors below 0.95.\",\"results_keys\":[\"coverage_conventional\",\"conventional_ci_undercovers\",\"mcse_coverage_conventional\",\"undercoverage_threshold\"],\"status\":\"supported\",\"caveat\":\"Conditional on this fixed design, normal errors and seed; MC SE about 0.008.\"},\n {\"id\":\"C2\",\"text\":\"The HC1 interval covers better (0.902) than the conventional interval but still falls below 95%.\",\"results_keys\":[\"coverage_hc1\",\"mcse_coverage_hc1\",\"mcse_at_nominal\"],\"status\":\"supported\",\"caveat\":\"MC SE about 0.007; small-sample HC variants not evaluated.\"},\n {\"id\":\"C3\",\"text\":\"The exact SD of the slope estimator is 0.268, and the Monte Carlo SD (0.270) agrees with it.\",\"results_keys\":[\"sd_beta_exact\",\"sd_beta_mc\"],\"status\":\"supported\",\"caveat\":\"Exact value is analytic, given the design.\"},\n {\"id\":\"C4\",\"text\":\"The conventional SE is biased downward: its mean (0.190) is about 29% below the true SD of the slope estimator.\",\"results_keys\":[\"mean_se_conventional\",\"sd_beta_exact\",\"sqrt_E_s2_over_Sxx_analytic\"],\"status\":\"supported\",\"caveat\":\"MC average; consistent with the analytic sqrt(E[s^2]/Sxx).\"}\n]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"49b013dd-27b9-4d21-ac3b-9adb2a14c701","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0050","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","hash":"7b1aeaa1b312991966359b4df16d5b6fe7cf9bfa0b948a324cf517ca101cf069","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01Bn9SrMz4dTay267dRpkfKZ","name":"Bash","input":{"command":"cat TASK.md; ls -la out; cat out/results.json; cat out/claims.json"}}]}}15{"type":"api-request","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","id":"99fadf95-d94c-4e7b-8454-1bd1047f11e9","timestamp":"2026-10-30T04:16:21.526Z","version":"2.1.314","querySource":"sdk","shapeHash":"d79ca2fe63fd65b75c7832bac2805c6639fa2d5442e00a9a7ef1763668474b77","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"a31c0452-4986-4ec8-9b7a-47b101016267","keep":2,"tail":["7b1aeaa1b312991966359b4df16d5b6fe7cf9bfa0b948a324cf517ca101cf069","54a3d05c53e8e5913e7616dfea65884df0f413e2c7ef71997ebc6bea46153169"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"265c6a4e3be19285f34e556f9c6ceec2846a22afe2645dd48fa8814261c37671"}16{"parentUuid":"b4103523-f39a-4979-aa1e-98efb965f1f6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmtXgxg56ud383v32Pu","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014oJgfsesytMvJ72sJJkYbR","name":"Bash","input":{"command":"cat out/analysis.py; cat out/report.md; cat out/exclusions.csv; ls out/state; cat data/design.csv | head -5"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2984,"cache_read_input_tokens":2220,"output_tokens":94,"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":2984},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmtXP7cUhYQ8GqwiwpN","requestRef":"99fadf95-d94c-4e7b-8454-1bd1047f11e9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"87190390-434a-457d-b6fc-2fc295c1aff4","timestamp":"2026-10-30T04:16:23.087Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0050","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","version":"2.1.314","gitBranch":"HEAD"}17{"parentUuid":"87190390-434a-457d-b6fc-2fc295c1aff4","isSidechain":false,"promptId":"3e720f54-456b-45b7-b91d-31cbf750238b","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014oJgfsesytMvJ72sJJkYbR","type":"tool_result","content":"\"\"\"F2-conf-03: Monte Carlo coverage of OLS CIs under heteroskedasticity.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\nSEED = 20261030\nALPHA, BETA, N, R, LEVEL = 1.0, 0.5, 30, 2000, 0.95\n\nd = pd.read_csv(\"data/design.csv\")\nn_rows = len(d)\nissues = []\nassert n_rows == N and d[[\"x\", \"sigma\"]].notna().all().all() and (d.sigma > 0).all()\nx = d[\"x\"].to_numpy(float); sig = d[\"sigma\"].to_numpy(float)\n\n# exact SD of beta_hat: beta_hat - beta = sum w_i sigma_i eps_i\nxc = x - x.mean(); Sxx = np.sum(xc**2); w = xc / Sxx\nsd_exact = float(np.sqrt(np.sum(w**2 * sig**2)))\n\nrng = np.random.default_rng(SEED)\neps = rng.standard_normal((R, N))\nY = ALPHA + BETA * x[None, :] + sig[None, :] * eps\nybar = Y.mean(axis=1, keepdims=True)\nb = (Y - ybar) @ xc / Sxx\na = ybar[:, 0] - b * x.mean()\nE = Y - a[:, None] - b[:, None] * x[None, :]\ns2 = np.sum(E**2, axis=1) / (N - 2)\nse_conv = np.sqrt(s2 / Sxx)\nse_hc0 = np.sqrt(np.sum((xc[None, :] ** 2) * E**2, axis=1)) / Sxx\nse_hc1 = se_hc0 * np.sqrt(N / (N - 2))\ntcrit = stats.t.ppf(0.5 + LEVEL / 2, N - 2)\ncov_conv = float(np.mean(np.abs(b - BETA) <= tcrit * se_conv))\ncov_hc1 = float(np.mean(np.abs(b - BETA) <= tcrit * se_hc1))\nmcse0 = np.sqrt(LEVEL * (1 - LEVEL) / R)\nundercovers = bool(cov_conv < LEVEL - 3 * mcse0)\n\n# cross-check one replication against statsmodels\nimport statsmodels.api as sm\nm = sm.OLS(Y[0], sm.add_constant(x)).fit()\nassert np.isclose(m.bse[1], se_conv[0]) and np.isclose(m.HC1_se[1], se_hc1[0])\n\nres = {\n \"task_id\": \"F2-conf-03\",\n \"coverage_conventional\": cov_conv,\n \"coverage_hc1\": cov_hc1,\n \"sd_beta_exact\": sd_exact,\n \"sd_beta_mc\": float(np.std(b, ddof=1)),\n \"mean_se_conventional\": float(se_conv.mean()),\n \"conventional_ci_undercovers\": undercovers,\n \"n_replications\": R,\n \"n_obs_per_replication\": N,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"design.csv\": int(n_rows)},\n \"random_seed\": SEED,\n # supplementary\n \"t_critical\": float(tcrit),\n \"mcse_coverage_conventional\": float(np.sqrt(cov_conv * (1 - cov_conv) / R)),\n \"mcse_coverage_hc1\": float(np.sqrt(cov_hc1 * (1 - cov_hc1) / R)),\n \"mcse_at_nominal\": float(mcse0),\n \"undercoverage_threshold\": float(LEVEL - 3 * mcse0),\n \"mean_se_hc1\": float(se_hc1.mean()),\n \"sqrt_E_s2_over_Sxx_analytic\": float(np.sqrt((np.sum(sig**2) - np.sum((1/N + xc**2/Sxx) * sig**2)) / (N - 2) / Sxx)),\n \"baseline_coverage_conventional\": cov_conv,\n}\nos.makedirs(\"out\", exist_ok=True)\nwith open(\"out/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2))\n# F2-conf-03 — Monte Carlo coverage of OLS confidence intervals (n = 30, heteroskedastic)\n\n## Question\nIn the fixed-design model y_i = 1 + 0.5 x_i + σ_i ε_i (i = 1..30, ε_i iid N(0,1), x_i and σ_i fixed from `data/design.csv`), do nominal 95% t(28) intervals for the slope based on (a) the conventional OLS SE and (b) the HC1 robust SE reach 95% coverage? We also report (c) the exact SD of β̂ and (d) E[se_conv].\n\n## Data and exclusions\n`data/design.csv` (SHA-256 b1a17a52…3e74f0): 30 rows, columns i, x, sigma; no missing values, no duplicates, all σ_i > 0 (`n_input_rows`, `data_issues` = []). No rows excluded (`out/exclusions.csv`: 30 → 30). Design feature: σ_i² is strongly correlated with x_i² (r = 0.987, computed in the audit), so error variance is largest at high-leverage points.\n\n## Method\nAnalytic: β̂ − β = Σ w_i σ_i ε_i with w_i = (x_i − x̄)/S_xx, so SD(β̂) = sqrt(Σ w_i² σ_i²). Monte Carlo: R = 2000 replications (`n_replications`), n = 30 (`n_obs_per_replication`), numpy `default_rng` seed 20261030 (`random_seed`); only ε redrawn. In each replication OLS is fitted; se_conv = sqrt(s²/S_xx), s² = RSS/(n−2); se_HC1 = sqrt(n/(n−2)) · sqrt(Σ (x_i−x̄)² e_i²)/S_xx. Intervals β̂ ± t_{0.975}(28)·se with t = 2.0484 (`t_critical`). Both SE formulas were cross-checked against statsmodels for one replication. Undercoverage rule: coverage_conventional < 0.95 − 3·sqrt(0.95·0.05/2000) = 0.9354 (`undercoverage_threshold`). The conventional interval's coverage is the naïve baseline (`baseline_coverage_conventional`).\n\n## Results\n- Conventional interval coverage: **0.844** (`coverage_conventional`), far below 95%.\n- HC1 interval coverage: **0.902** (`coverage_hc1`) — better, but still below 95%.\n- Exact SD of β̂: **0.2680** (`sd_beta_exact`); Monte Carlo SD: 0.2696 (`sd_beta_mc`), agreeing within MC error.\n- Mean conventional SE: **0.1903** (`mean_se_conventional`), about 29% below the true SD of 0.2680; mean HC1 SE is 0.2408 (`mean_se_hc1`), also below the true SD. Analytic check: sqrt(E[s²]/S_xx) = 0.1932 (`sqrt_E_s2_over_Sxx_analytic`), consistent with the MC mean SE (Jensen).\n- Conclusion: the conventional interval does **not** attain its nominal coverage (`conventional_ci_undercovers` = true).\n\n## Uncertainty\nBinomial Monte Carlo standard errors: conventional coverage 0.844 ± 0.0081 (`mcse_coverage_conventional`); HC1 coverage 0.902 ± 0.0067 (`mcse_coverage_hc1`); MC SE at the nominal 0.95 is 0.0049 (`mcse_at_nominal`). The conventional shortfall (0.106) is about 22 nominal-level MC SEs; the HC1 shortfall (0.048) is about 10. The sd_beta_exact value is analytic (no MC error); sd_beta_mc differs from it by 0.6%, below its approximate MC relative SE of 1.6%.\n\n## Limitations and open issues\n- Results are conditional on this one synthetic fixed design and normal errors; they do not generalise to other designs.\n- Coverage estimates depend on the seed at the level of the reported MC SEs; R = 2000 is fixed by the task.\n- HC1 also undercovers in this n = 30 high-leverage design; small-sample refinements (HC2/HC3, wild bootstrap) were not evaluated because the task does not require them.\n- No data problems were found; feasibility is \"complete\".\nstep,rule,n_before,n_after,n_removed,rationale\n1,none (all design rows retained),30,30,0,design is complete and valid; task forbids altering x or sigma\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json\ni,x,sigma\n1,-1.3942,1.1831\n2,-0.9737,0.8844\n3,-0.8373,0.8103\n4,-0.7367,0.7628","is_error":false}]},"uuid":"fa59050b-6f13-4909-bc5c-5812e237a8bc","timestamp":"2026-10-30T04:16:23.133Z","toolUseResult":{"stdout":"\"\"\"F2-conf-03: Monte Carlo coverage of OLS CIs under heteroskedasticity.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\nSEED = 20261030\nALPHA, BETA, N, R, LEVEL = 1.0, 0.5, 30, 2000, 0.95\n\nd = pd.read_csv(\"data/design.csv\")\nn_rows = len(d)\nissues = []\nassert n_rows == N and d[[\"x\", \"sigma\"]].notna().all().all() and (d.sigma > 0).all()\nx = d[\"x\"].to_numpy(float); sig = d[\"sigma\"].to_numpy(float)\n\n# exact SD of beta_hat: beta_hat - beta = sum w_i sigma_i eps_i\nxc = x - x.mean(); Sxx = np.sum(xc**2); w = xc / Sxx\nsd_exact = float(np.sqrt(np.sum(w**2 * sig**2)))\n\nrng = np.random.default_rng(SEED)\neps = rng.standard_normal((R, N))\nY = ALPHA + BETA * x[None, :] + sig[None, :] * eps\nybar = Y.mean(axis=1, keepdims=True)\nb = (Y - ybar) @ xc / Sxx\na = ybar[:, 0] - b * x.mean()\nE = Y - a[:, None] - b[:, None] * x[None, :]\ns2 = np.sum(E**2, axis=1) / (N - 2)\nse_conv = np.sqrt(s2 / Sxx)\nse_hc0 = np.sqrt(np.sum((xc[None, :] ** 2) * E**2, axis=1)) / Sxx\nse_hc1 = se_hc0 * np.sqrt(N / (N - 2))\ntcrit = stats.t.ppf(0.5 + LEVEL / 2, N - 2)\ncov_conv = float(np.mean(np.abs(b - BETA) <= tcrit * se_conv))\ncov_hc1 = float(np.mean(np.abs(b - BETA) <= tcrit * se_hc1))\nmcse0 = np.sqrt(LEVEL * (1 - LEVEL) / R)\nundercovers = bool(cov_conv < LEVEL - 3 * mcse0)\n\n# cross-check one replication against statsmodels\nimport statsmodels.api as sm\nm = sm.OLS(Y[0], sm.add_constant(x)).fit()\nassert np.isclose(m.bse[1], se_conv[0]) and np.isclose(m.HC1_se[1], se_hc1[0])\n\nres = {\n \"task_id\": \"F2-conf-03\",\n \"coverage_conventional\": cov_conv,\n \"coverage_hc1\": cov_hc1,\n \"sd_beta_exact\": sd_exact,\n \"sd_beta_mc\": float(np.std(b, ddof=1)),\n \"mean_se_conventional\": float(se_conv.mean()),\n \"conventional_ci_undercovers\": undercovers,\n \"n_replications\": R,\n \"n_obs_per_replication\": N,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"design.csv\": int(n_rows)},\n \"random_seed\": SEED,\n # supplementary\n \"t_critical\": float(tcrit),\n \"mcse_coverage_conventional\": float(np.sqrt(cov_conv * (1 - cov_conv) / R)),\n \"mcse_coverage_hc1\": float(np.sqrt(cov_hc1 * (1 - cov_hc1) / R)),\n \"mcse_at_nominal\": float(mcse0),\n \"undercoverage_threshold\": float(LEVEL - 3 * mcse0),\n \"mean_se_hc1\": float(se_hc1.mean()),\n \"sqrt_E_s2_over_Sxx_analytic\": float(np.sqrt((np.sum(sig**2) - np.sum((1/N + xc**2/Sxx) * sig**2)) / (N - 2) / Sxx)),\n \"baseline_coverage_conventional\": cov_conv,\n}\nos.makedirs(\"out\", exist_ok=True)\nwith open(\"out/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2))\n# F2-conf-03 — Monte Carlo coverage of OLS confidence intervals (n = 30, heteroskedastic)\n\n## Question\nIn the fixed-design model y_i = 1 + 0.5 x_i + σ_i ε_i (i = 1..30, ε_i iid N(0,1), x_i and σ_i fixed from `data/design.csv`), do nominal 95% t(28) intervals for the slope based on (a) the conventional OLS SE and (b) the HC1 robust SE reach 95% coverage? We also report (c) the exact SD of β̂ and (d) E[se_conv].\n\n## Data and exclusions\n`data/design.csv` (SHA-256 b1a17a52…3e74f0): 30 rows, columns i, x, sigma; no missing values, no duplicates, all σ_i > 0 (`n_input_rows`, `data_issues` = []). No rows excluded (`out/exclusions.csv`: 30 → 30). Design feature: σ_i² is strongly correlated with x_i² (r = 0.987, computed in the audit), so error variance is largest at high-leverage points.\n\n## Method\nAnalytic: β̂ − β = Σ w_i σ_i ε_i with w_i = (x_i − x̄)/S_xx, so SD(β̂) = sqrt(Σ w_i² σ_i²). Monte Carlo: R = 2000 replications (`n_replications`), n = 30 (`n_obs_per_replication`), numpy `default_rng` seed 20261030 (`random_seed`); only ε redrawn. In each replication OLS is fitted; se_conv = sqrt(s²/S_xx), s² = RSS/(n−2); se_HC1 = sqrt(n/(n−2)) · sqrt(Σ (x_i−x̄)² e_i²)/S_xx. Intervals β̂ ± t_{0.975}(28)·se with t = 2.0484 (`t_critical`). Both SE formulas were cross-checked against statsmodels for one replication. Undercoverage rule: coverage_conventional < 0.95 − 3·sqrt(0.95·0.05/2000) = 0.9354 (`undercoverage_threshold`). The conventional interval's coverage is the naïve baseline (`baseline_coverage_conventional`).\n\n## Results\n- Conventional interval coverage: **0.844** (`coverage_conventional`), far below 95%.\n- HC1 interval coverage: **0.902** (`coverage_hc1`) — better, but still below 95%.\n- Exact SD of β̂: **0.2680** (`sd_beta_exact`); Monte Carlo SD: 0.2696 (`sd_beta_mc`), agreeing within MC error.\n- Mean conventional SE: **0.1903** (`mean_se_conventional`), about 29% below the true SD of 0.2680; mean HC1 SE is 0.2408 (`mean_se_hc1`), also below the true SD. Analytic check: sqrt(E[s²]/S_xx) = 0.1932 (`sqrt_E_s2_over_Sxx_analytic`), consistent with the MC mean SE (Jensen).\n- Conclusion: the conventional interval does **not** attain its nominal coverage (`conventional_ci_undercovers` = true).\n\n## Uncertainty\nBinomial Monte Carlo standard errors: conventional coverage 0.844 ± 0.0081 (`mcse_coverage_conventional`); HC1 coverage 0.902 ± 0.0067 (`mcse_coverage_hc1`); MC SE at the nominal 0.95 is 0.0049 (`mcse_at_nominal`). The conventional shortfall (0.106) is about 22 nominal-level MC SEs; the HC1 shortfall (0.048) is about 10. The sd_beta_exact value is analytic (no MC error); sd_beta_mc differs from it by 0.6%, below its approximate MC relative SE of 1.6%.\n\n## Limitations and open issues\n- Results are conditional on this one synthetic fixed design and normal errors; they do not generalise to other designs.\n- Coverage estimates depend on the seed at the level of the reported MC SEs; R = 2000 is fixed by the task.\n- HC1 also undercovers in this n = 30 high-leverage design; small-sample refinements (HC2/HC3, wild bootstrap) were not evaluated because the task does not require them.\n- No data problems were found; feasibility is \"complete\".\nstep,rule,n_before,n_after,n_removed,rationale\n1,none (all design rows retained),30,30,0,design is complete and valid; task forbids altering x or sigma\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json\ni,x,sigma\n1,-1.3942,1.1831\n2,-0.9737,0.8844\n3,-0.8373,0.8103\n4,-0.7367,0.7628","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"87190390-434a-457d-b6fc-2fc295c1aff4","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0050","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","version":"2.1.314","gitBranch":"HEAD"}18{"type":"api-request-blob","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","hash":"8cc4ad867468d1e23a4c768a869a735dc3d1ed9c13528cbdcd20da87b11505f9","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_014oJgfsesytMvJ72sJJkYbR","name":"Bash","input":{"command":"cat out/analysis.py; cat out/report.md; cat out/exclusions.csv; ls out/state; cat data/design.csv | head -5"}}]}}19{"type":"api-request","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","id":"cca67d45-0a09-4b1d-b32d-f21aee637a94","timestamp":"2026-10-30T04:16:23.137Z","version":"2.1.314","querySource":"sdk","shapeHash":"d79ca2fe63fd65b75c7832bac2805c6639fa2d5442e00a9a7ef1763668474b77","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"99fadf95-d94c-4e7b-8454-1bd1047f11e9","keep":4,"tail":["8cc4ad867468d1e23a4c768a869a735dc3d1ed9c13528cbdcd20da87b11505f9","9dea91f72fc2323d3c94aa9ba4da6fc55f54cde16dc69fb3b24110ca88b44449"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"2d16ec3371f3669a46aaba012b269f0133712f42611cdef3744424ba15ba29a2"}20{"type":"last-prompt","lastPrompt":"Review","leafUuid":"fa59050b-6f13-4909-bc5c-5812e237a8bc","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4"}21{"type":"atis-latch","atis":"","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4"}22{"parentUuid":"fa59050b-6f13-4909-bc5c-5812e237a8bc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmteWUyDw4yEkACqmJo","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3289,"cache_read_input_tokens":5204,"output_tokens":514,"output_tokens_details":{"thinking_tokens":124},"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":3289},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":987,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmteFs324AZ3H2W6TbU","requestRef":"cca67d45-0a09-4b1d-b32d-f21aee637a94","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8522be6e-bfd6-4d2e-92bc-52c7a200a910","timestamp":"2026-10-30T04:16:25.385Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0050","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","version":"2.1.314","gitBranch":"HEAD"}23{"parentUuid":"8522be6e-bfd6-4d2e-92bc-52c7a200a910","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmteWUyDw4yEkACqmJo","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012Jh4QYJFjn3w4oBVWFzbqj","name":"Bash","input":{"command":"cd /tmp && rm -rf rc50 && mkdir rc50 && cp -r ~/wp29/runs/RC0050/data rc50/ && mkdir rc50/out && cp ~/wp29/runs/RC0050/out/analysis.py rc50/out/ && cd rc50 && ~/wp29/venv/bin/python out/analysis.py >/dev/null && diff <(python3 -m json.tool out/results.json) <(python3 -m json.tool ~/wp29/runs/RC0050/out/results.json) && echo SAME; ~/wp29/venv/bin/python -c \"\nimport pandas as pd,numpy as np\nd=pd.read_csv('data/design.csv');print(d.describe());print(np.corrcoef(d.sigma**2,d.x**2)[0,1], d.duplicated().sum())\nprint((0.95-0.844)/0.004873,(0.95-0.902)/0.004873, 1-0.19026/0.26798, 1/np.sqrt(2*1999))\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3289,"cache_read_input_tokens":5204,"output_tokens":514,"output_tokens_details":{"thinking_tokens":124},"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":3289},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmteFs324AZ3H2W6TbU","requestRef":"cca67d45-0a09-4b1d-b32d-f21aee637a94","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"838a9ab1-89a4-45a2-8dad-6aaffa36bb84","timestamp":"2026-10-30T04:16:28.092Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0050","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"838a9ab1-89a4-45a2-8dad-6aaffa36bb84","isSidechain":false,"promptId":"3e720f54-456b-45b7-b91d-31cbf750238b","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012Jh4QYJFjn3w4oBVWFzbqj","type":"tool_result","content":"SAME\n i x sigma\ncount 30.000000 30.000000 30.000000\nmean 15.500000 0.282297 0.819457\nstd 8.803408 0.821180 0.290442\nmin 1.000000 -1.394200 0.603400\n25% 8.250000 -0.231900 0.617025\n50% 15.500000 0.231150 0.735150\n75% 22.750000 0.765550 0.869300\nmax 30.000000 1.991900 1.790300\n0.9866773427269261 0\n21.75251385183665 9.850194951775073 0.29002164340622427 0.015815342630852665\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RC0050","is_error":false}]},"uuid":"7c185142-ff4a-4387-9cf2-bc7bad66a73e","timestamp":"2026-10-30T04:16:29.363Z","toolUseResult":{"stdout":"SAME\n i x sigma\ncount 30.000000 30.000000 30.000000\nmean 15.500000 0.282297 0.819457\nstd 8.803408 0.821180 0.290442\nmin 1.000000 -1.394200 0.603400\n25% 8.250000 -0.231900 0.617025\n50% 15.500000 0.231150 0.735150\n75% 22.750000 0.765550 0.869300\nmax 30.000000 1.991900 1.790300\n0.9866773427269261 0\n21.75251385183665 9.850194951775073 0.29002164340622427 0.015815342630852665","stderr":"\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RC0050","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"838a9ab1-89a4-45a2-8dad-6aaffa36bb84","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0050","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","version":"2.1.314","gitBranch":"HEAD"}25{"type":"api-request-blob","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","hash":"79aa22ae7c305f752ac4471f46a4057828beb675abf1978cf015a2f361ff884a","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_012Jh4QYJFjn3w4oBVWFzbqj","name":"Bash","input":{"command":"cd /tmp && rm -rf rc50 && mkdir rc50 && cp -r ~/wp29/runs/RC0050/data rc50/ && mkdir rc50/out && cp ~/wp29/runs/RC0050/out/analysis.py rc50/out/ && cd rc50 && ~/wp29/venv/bin/python out/analysis.py >/dev/null && diff <(python3 -m json.tool out/results.json) <(python3 -m json.tool ~/wp29/runs/RC0050/out/results.json) && echo SAME; ~/wp29/venv/bin/python -c \"\nimport pandas as pd,numpy as np\nd=pd.read_csv('data/design.csv');print(d.describe());print(np.corrcoef(d.sigma**2,d.x**2)[0,1], d.duplicated().sum())\nprint((0.95-0.844)/0.004873,(0.95-0.902)/0.004873, 1-0.19026/0.26798, 1/np.sqrt(2*1999))\""}}]}}26{"type":"api-request","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","id":"190a2803-8504-4246-ab33-e45cc23961a6","timestamp":"2026-10-30T04:16:29.366Z","version":"2.1.314","querySource":"sdk","shapeHash":"d79ca2fe63fd65b75c7832bac2805c6639fa2d5442e00a9a7ef1763668474b77","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"cca67d45-0a09-4b1d-b32d-f21aee637a94","keep":6,"tail":["79aa22ae7c305f752ac4471f46a4057828beb675abf1978cf015a2f361ff884a","865495b415ab2f43811277233af4a06305ecaafa38d7dc7e357d7f296927bf1f"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"6f899c2999d5a31d3b61a09e98258eb3e7cdcd0d6d7c0330dfa0c94b877e964f"}27{"parentUuid":"7c185142-ff4a-4387-9cf2-bc7bad66a73e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmu7BrLaaAvhJTFX7P6","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":754,"cache_read_input_tokens":8493,"output_tokens":1063,"output_tokens_details":{"thinking_tokens":201},"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":754},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1518,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmu6ujatYcTFPuaf2VS","requestRef":"190a2803-8504-4246-ab33-e45cc23961a6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"61d769ab-77cc-4440-9063-eadc480fb8a1","timestamp":"2026-10-30T04:16:32.873Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0050","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","version":"2.1.314","gitBranch":"HEAD"}28{"parentUuid":"61d769ab-77cc-4440-9063-eadc480fb8a1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmu7BrLaaAvhJTFX7P6","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NZ7qJPraNBCaWQWFEQdwhe","name":"Bash","input":{"command":"cat > out/review/review.json <<'EOF'\n{\"verdict\":\"accept\",\n\"issues\":[\n{\"id\":\"R1\",\"severity\":\"minor\",\"location\":\"out/claims.json C2\",\"issue\":\"C2 compares HC1 coverage with conventional coverage, but its results_keys do not include coverage_conventional or mcse_coverage_conventional.\",\"suggested_fix\":\"Add coverage_conventional and mcse_coverage_conventional to C2's results_keys.\"},\n{\"id\":\"R2\",\"severity\":\"minor\",\"location\":\"out/results.json conventional_ci_undercovers / report Method\",\"issue\":\"The task says 'binomial standard error at R = 2000' without saying which proportion to use. The code uses the SE at the nominal 0.95 (0.0049). Using the SE at the estimated coverage (0.0081) gives the same conclusion, but the report does not say this.\",\"suggested_fix\":\"Add one sentence saying the conclusion holds under either choice of SE (the shortfall is about 13 SEs even at the estimated coverage).\"},\n{\"id\":\"R3\",\"severity\":\"minor\",\"location\":\"out/report.md Results/Uncertainty\",\"issue\":\"The '29% below the true SD' figure in C4 is a Monte Carlo average, and no MC SE is given for mean_se_conventional. The analytic upper bound 0.1932 supports the claim, so this is a reporting gap only.\",\"suggested_fix\":\"Report the MC SE of mean_se_conventional (sd(se_conv)/sqrt(R)).\"}\n],\n\"summary\":\"I re-ran analysis.py in a scratch copy and got a byte-identical results.json, so the output is deterministic with a fixed seed. All required keys are present with correct types. The code uses only data/design.csv and redraws only epsilon, never x or sigma. The conventional and HC1 SEs are checked against statsmodels. The exact SD formula sqrt(sum w_i^2 sigma_i^2) is correct, and so is the analytic E[s^2] based on (1-h_ii). The undercoverage rule follows the task. The claims are supported: conventional coverage is 0.844, about 22 MC SEs below 0.95; HC1 coverage is 0.902, about 10 MC SEs below. The report includes the baseline, MC SEs and limitations. Only minor documentation issues remain.\"}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":754,"cache_read_input_tokens":8493,"output_tokens":1063,"output_tokens_details":{"thinking_tokens":201},"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":754},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmu6ujatYcTFPuaf2VS","requestRef":"190a2803-8504-4246-ab33-e45cc23961a6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"26f44b7e-cd2a-4f33-80a6-1bf6c0a5735f","timestamp":"2026-10-30T04:16:39.942Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0050","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"26f44b7e-cd2a-4f33-80a6-1bf6c0a5735f","isSidechain":false,"promptId":"3e720f54-456b-45b7-b91d-31cbf750238b","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NZ7qJPraNBCaWQWFEQdwhe","type":"tool_result","content":"ok","is_error":false}]},"uuid":"d1c52135-fdf9-43a4-9621-f66f3f3fd148","timestamp":"2026-10-30T04:16:40.030Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"26f44b7e-cd2a-4f33-80a6-1bf6c0a5735f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0050","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","version":"2.1.314","gitBranch":"HEAD"}30{"type":"api-request-blob","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","hash":"9a0ec9b0ada86490a3548d78aedac073d90586f2363bba4731bbc4224eb53ab8","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01NZ7qJPraNBCaWQWFEQdwhe","name":"Bash","input":{"command":"cat > out/review/review.json <<'EOF'\n{\"verdict\":\"accept\",\n\"issues\":[\n{\"id\":\"R1\",\"severity\":\"minor\",\"location\":\"out/claims.json C2\",\"issue\":\"C2 compares HC1 coverage with conventional coverage, but its results_keys do not include coverage_conventional or mcse_coverage_conventional.\",\"suggested_fix\":\"Add coverage_conventional and mcse_coverage_conventional to C2's results_keys.\"},\n{\"id\":\"R2\",\"severity\":\"minor\",\"location\":\"out/results.json conventional_ci_undercovers / report Method\",\"issue\":\"The task says 'binomial standard error at R = 2000' without saying which proportion to use. The code uses the SE at the nominal 0.95 (0.0049). Using the SE at the estimated coverage (0.0081) gives the same conclusion, but the report does not say this.\",\"suggested_fix\":\"Add one sentence saying the conclusion holds under either choice of SE (the shortfall is about 13 SEs even at the estimated coverage).\"},\n{\"id\":\"R3\",\"severity\":\"minor\",\"location\":\"out/report.md Results/Uncertainty\",\"issue\":\"The '29% below the true SD' figure in C4 is a Monte Carlo average, and no MC SE is given for mean_se_conventional. The analytic upper bound 0.1932 supports the claim, so this is a reporting gap only.\",\"suggested_fix\":\"Report the MC SE of mean_se_conventional (sd(se_conv)/sqrt(R)).\"}\n],\n\"summary\":\"I re-ran analysis.py in a scratch copy and got a byte-identical results.json, so the output is deterministic with a fixed seed. All required keys are present with correct types. The code uses only data/design.csv and redraws only epsilon, never x or sigma. The conventional and HC1 SEs are checked against statsmodels. The exact SD formula sqrt(sum w_i^2 sigma_i^2) is correct, and so is the analytic E[s^2] based on (1-h_ii). The undercoverage rule follows the task. The claims are supported: conventional coverage is 0.844, about 22 MC SEs below 0.95; HC1 coverage is 0.902, about 10 MC SEs below. The report includes the baseline, MC SEs and limitations. Only minor documentation issues remain.\"}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok"}}]}}31{"type":"api-request","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","id":"65a42d28-dc14-4542-a2a3-53b3efda49cb","timestamp":"2026-10-30T04:16:40.034Z","version":"2.1.314","querySource":"sdk","shapeHash":"d79ca2fe63fd65b75c7832bac2805c6639fa2d5442e00a9a7ef1763668474b77","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"190a2803-8504-4246-ab33-e45cc23961a6","keep":8,"tail":["9a0ec9b0ada86490a3548d78aedac073d90586f2363bba4731bbc4224eb53ab8","6c8a2d1dcc9a3a28ceae0a6e767f9aeb1fc26adca3ff45fdffa72c2a5fd8e188"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5994a48aed96b4ae91a0fd48d666dc3b23edc3280b83e222d561c5e41709de8b"}32{"parentUuid":"d1c52135-fdf9-43a4-9621-f66f3f3fd148","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmutoF9gosxAQ2uNdTQ","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1070,"cache_read_input_tokens":9247,"output_tokens":763,"output_tokens_details":{"thinking_tokens":35},"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":1070},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6203,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmutXcpe5ZFPU9VZQpv","requestRef":"65a42d28-dc14-4542-a2a3-53b3efda49cb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"884b2346-4899-42d1-8ea5-f1dca3cf2769","timestamp":"2026-10-30T04:16:47.451Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0050","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","version":"2.1.314","gitBranch":"HEAD"}33{"parentUuid":"884b2346-4899-42d1-8ea5-f1dca3cf2769","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmutoF9gosxAQ2uNdTQ","type":"message","role":"assistant","content":[{"type":"text","text":"**Verdict: accept.** I found three minor issues and nothing critical or major. The review is written to `out/review/review.json`.\n\n**What I checked:**\n- **Reproducibility:** I re-ran `out/analysis.py` in a scratch copy using only `data/`. It produced a byte-identical `results.json`, so the output is fixed by the seed (20261030).\n- **Required outputs:** every required key is present with the right type, `n_replications` is 2000 and `n_obs_per_replication` is 30.\n- **Information rules:** the code reads only `design.csv`, never redraws x or σ, and uses only the given constants and the t(28) critical value.\n- **Calculations:** the conventional and HC1 standard errors are checked against statsmodels inside the script. The exact SD formula, sqrt(Σ w_i² σ_i²), is correct, and so is the analytic E[s²] = Σ σ_i²(1 − h_ii)/(n − 2).\n- **Claims:** the undercoverage rule follows the task. The numbers back the claims:\n - Conventional coverage is 0.844, about 22 Monte Carlo standard errors below 0.95.\n - HC1 coverage is 0.902, about 10 standard errors below 0.95.\n - The simulated SD of β̂ (0.2696) agrees with the exact value (0.2680).\n - The mean conventional SE (0.190) is about 29% below the true SD, which is consistent with the analytic value of 0.193.\n- **Report:** it includes the naïve baseline, the Monte Carlo standard errors and honest limitations, and calling the work \"complete\" is justified.\n\n**Minor issues:**\n1. **Claim C2's linked results:** C2 compares HC1 with the conventional interval, but doesn't list the conventional coverage keys among its supporting results.\n2. **Which standard error the undercoverage test uses:** the task doesn't say which proportion to base it on. The code uses the value at 0.95; using the estimated coverage instead gives the same conclusion, but the report doesn't say so.\n3. **Mean SE has no uncertainty attached:** the report gives no Monte Carlo standard error for the average conventional SE."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1070,"cache_read_input_tokens":9247,"output_tokens":763,"output_tokens_details":{"thinking_tokens":35},"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":1070},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmutXcpe5ZFPU9VZQpv","requestRef":"65a42d28-dc14-4542-a2a3-53b3efda49cb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b419849a-075f-4bce-9623-f2f5bbe8e9f1","timestamp":"2026-10-30T04:16:47.455Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0050","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","version":"2.1.314","gitBranch":"HEAD"}34{"type":"last-prompt","lastPrompt":"Review","leafUuid":"b419849a-075f-4bce-9623-f2f5bbe8e9f1","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4"}35{"type":"cost-state","sessionId":"d808a1a3-e1e7-4130-910d-71d9935291d4","totalCostUSD":0.0996466,"totalAPIDuration":26867,"totalAPIDurationWithoutRetries":26862,"totalToolDuration":1458,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":28497,"startTime":1793333778969,"modelUsage":{"claude-opus-5-5":{"inputTokens":12,"outputTokens":2510,"thinkingTokens":360,"cacheReadInputTokens":26668,"cacheCreationInputTokens":8813,"cacheCreationEphemeral5mInputTokens":8813,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.0996466}},"hasUnknownModelCost":false}36