1{"type":"atis-latch","atis":"","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:01:40.855Z","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:01:40.860Z","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca"}4{"parentUuid":null,"isSidechain":false,"promptId":"2d8c293d-d814-4110-85d3-c0a929c36671","type":"user","message":{"role":"user","content":"Begin"},"uuid":"ba11ecf7-2130-440e-8e61-8c7f9dfb3bd5","timestamp":"2026-10-30T05:01:40.864Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","shapeHash":"20be7962999c92ef9e1c6de3002f528972aba0b18ace953850ebe20697d81819","timestamp":"2026-10-30T05:01:40.878Z","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/RC0213\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. 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. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. 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\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\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":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","hash":"d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9","message":{"role":"system","content":[],"output_config":{"effort":"medium"}}}7{"type":"api-request","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","id":"a81d656b-144d-4a8e-91a5-ec75e1959ff2","timestamp":"2026-10-30T05:01:40.878Z","version":"2.1.314","querySource":"sdk","shapeHash":"20be7962999c92ef9e1c6de3002f528972aba0b18ace953850ebe20697d81819","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":["252097215a6af3e04abe3f3c5a1cce73f5f388825c554cef2907d381aaf4102e","d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",0,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"06c05bddbd88614da64dc5fb86febe6849fef63aa84c87275e5905dbc8c39b8e"}8{"parentUuid":"ba11ecf7-2130-440e-8e61-8c7f9dfb3bd5","isSidechain":false,"attachment":{"type":"session_context","context":{}},"type":"attachment","uuid":"3dccabdf-9cf2-43f2-ba18-2688575610ca","timestamp":"2026-10-30T05:01:40.869Z","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}9{"parentUuid":"3dccabdf-9cf2-43f2-ba18-2688575610ca","isSidechain":false,"attachment":{"type":"date","date":"2026-10-30"},"type":"attachment","uuid":"a17349e9-365f-49ff-b1fc-0d33046f3b48","timestamp":"2026-10-30T05:01:40.869Z","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/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}10{"parentUuid":"a17349e9-365f-49ff-b1fc-0d33046f3b48","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":"84c1c347-6c38-452f-9e74-72d8a88cd07e","timestamp":"2026-10-30T05:01:40.870Z","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/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca"}12{"parentUuid":"84c1c347-6c38-452f-9e74-72d8a88cd07e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqMCg4x8tRVAuBiVusD","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":4,"cache_creation_input_tokens":2603,"cache_read_input_tokens":1504,"output_tokens":134,"output_tokens_details":{"thinking_tokens":19},"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":2603},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":418,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqMCQCnrYnY4BvGbpcK","requestRef":"a81d656b-144d-4a8e-91a5-ec75e1959ff2","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8ae1010d-eee1-4fab-acc7-e58b964c5e60","timestamp":"2026-10-30T05:01:45.184Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"8ae1010d-eee1-4fab-acc7-e58b964c5e60","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqMCg4x8tRVAuBiVusD","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014TRsXhgQsLQKWP81F6xkFa","name":"Bash","input":{"command":"cat TASK.md; ls -R | head -50; ls out/state 2>/dev/null && cat out/state/log.md","description":"Read task and list workspace"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2603,"cache_read_input_tokens":1504,"output_tokens":134,"output_tokens_details":{"thinking_tokens":19},"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":2603},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqMCQCnrYnY4BvGbpcK","requestRef":"a81d656b-144d-4a8e-91a5-ec75e1959ff2","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"35640961-d379-4cdb-b1f9-43fb7d9ad4fe","timestamp":"2026-10-30T05:01:45.815Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}14{"parentUuid":"35640961-d379-4cdb-b1f9-43fb7d9ad4fe","isSidechain":false,"promptId":"2d8c293d-d814-4110-85d3-c0a929c36671","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\n# F3-conf-01 — Forecasting Canadian monthly GDP growth under publication lags\n\n## Scientific question\nAt the end of each month, how well can the year-over-year growth of Canadian monthly real GDP one and three months ahead be forecast from the information that is actually published at that time, and does a model that also uses the (more timely) Labour Force Survey employment growth beat the naive forecast? The estimands are the out-of-sample RMSE and MAE, over a fixed set of 48 rolling (expanding-window) origins and horizons h ∈ {1, 3}, of (a) the naive forecast and (b) one model of your choice, and the relative improvement 1 − RMSE_model / RMSE_naive per horizon.\n\n## Inputs\n- `data/canada_gdp_employment.csv` — observed data (Statistics Canada, current vintage, retrieved 2026-10-30 UTC via the Web Data Service; see `data/README.md` for vectors, URL, SHA-256 and licence). Columns: `month` (YYYY-MM-01), `gdp_level` (monthly real GDP at basic prices, chained 2017 dollars, SA, millions), `gdp_yoy` (target: year-over-year growth of `gdp_level`, percent, 4 decimals), `emp_level` (LFS employment, SA, thousands), `emp_yoy` (predictor: year-over-year employment growth, percent, 4 decimals). 345 monthly rows, 1998-01 to 2026-09; GDP columns are empty after the last published GDP month (2026-07), employment runs to 2026-09.\n\n## Information-availability rules\n- Forecast origins: the 48 months t = 2022-05-01, 2022-06-01, …, 2026-04-01 (identified by their `month` value). The origin is the **end** of month t. Horizons h ∈ {1, 3}; the target of origin t at horizon h is `gdp_yoy` of month t + h.\n- Publication lags (documented in `data/README.md`): monthly GDP for a reference month is published about two months after the end of that month; LFS employment for a reference month is published in the first/second week of the following month. Therefore, at origin t, **the GDP columns are available only for months ≤ t − 2 and the employment columns only for months ≤ t − 1**. A forecast made at origin t (its estimation sample, its model selection, its transformations and the values plugged into the forecast) may use only these observations. Training targets must also be observed at the origin.\n- Naive baseline: for both horizons, the forecast is the most recent GDP growth value available at the origin, i.e. `gdp_yoy` of month t − 2.\n- The errors are computed on the data as given (current vintage); the exercise simulates publication timing, not historical first-release values — do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — the forecast ledger, one row per (origin, horizon, model), columns: `origin_date` (YYYY-MM-DD, the `month` of the origin), `target_date`, `horizon` (integer), `forecast`, `actual` (the `gdp_yoy` value of the target month), `model` (`naive` or the value of `model_name`), `last_target_date_used` (YYYY-MM-DD: the latest `gdp_yoy` month entering the forecast, estimation included), `last_predictor_date_used` (YYYY-MM-DD: the latest `emp_yoy` month used, or empty if the model does not use employment). Expected: 48 × 2 × 2 = 192 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-conf-01\" |\n| `model_name` | string | — | short name of your model, used in the `model` column |\n| `model_description` | string | — | one-sentence specification (lags, predictors, estimation sample, method) |\n| `n_origins` | integer | — | 48 |\n| `first_origin`, `last_origin` | string | date | first and last origin dates |\n| `last_target_date_available` | string | date | latest `gdp_yoy` month available at the last origin (2026-04-01) under the rules |\n| `last_predictor_date_available` | string | date | latest `emp_yoy` month available at the last origin under the rules |\n| `rmse_naive_h1`, `mae_naive_h1`, `rmse_naive_h3`, `mae_naive_h3` | number | percentage points | naive errors per horizon |\n| `rmse_model_h1`, `mae_model_h1`, `rmse_model_h3`, `mae_model_h3` | number | percentage points | your model's errors per horizon |\n| `rel_improvement_rmse_h1`, `rel_improvement_rmse_h3` | number | fraction | 1 − rmse_model / rmse_naive |\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 | — | {\"canada_gdp_employment.csv\": number of data rows read, excluding the header} |\n| `n_analysed` | integer | — | observations in the last estimation sample |\n| `random_seed` | integer or null | — | seed used, if any |\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` and `out/forecasts.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\ncanada_gdp_employment.csv\nREADME.md","is_error":true,"tool_use_id":"toolu_014TRsXhgQsLQKWP81F6xkFa"}]},"uuid":"c9d4db37-7cde-4fdf-9315-66737f2a0b42","timestamp":"2026-10-30T05:01:45.920Z","toolUseResult":"Error: Exit code 1\n# F3-conf-01 — Forecasting Canadian monthly GDP growth under publication lags\n\n## Scientific question\nAt the end of each month, how well can the year-over-year growth of Canadian monthly real GDP one and three months ahead be forecast from the information that is actually published at that time, and does a model that also uses the (more timely) Labour Force Survey employment growth beat the naive forecast? The estimands are the out-of-sample RMSE and MAE, over a fixed set of 48 rolling (expanding-window) origins and horizons h ∈ {1, 3}, of (a) the naive forecast and (b) one model of your choice, and the relative improvement 1 − RMSE_model / RMSE_naive per horizon.\n\n## Inputs\n- `data/canada_gdp_employment.csv` — observed data (Statistics Canada, current vintage, retrieved 2026-10-30 UTC via the Web Data Service; see `data/README.md` for vectors, URL, SHA-256 and licence). Columns: `month` (YYYY-MM-01), `gdp_level` (monthly real GDP at basic prices, chained 2017 dollars, SA, millions), `gdp_yoy` (target: year-over-year growth of `gdp_level`, percent, 4 decimals), `emp_level` (LFS employment, SA, thousands), `emp_yoy` (predictor: year-over-year employment growth, percent, 4 decimals). 345 monthly rows, 1998-01 to 2026-09; GDP columns are empty after the last published GDP month (2026-07), employment runs to 2026-09.\n\n## Information-availability rules\n- Forecast origins: the 48 months t = 2022-05-01, 2022-06-01, …, 2026-04-01 (identified by their `month` value). The origin is the **end** of month t. Horizons h ∈ {1, 3}; the target of origin t at horizon h is `gdp_yoy` of month t + h.\n- Publication lags (documented in `data/README.md`): monthly GDP for a reference month is published about two months after the end of that month; LFS employment for a reference month is published in the first/second week of the following month. Therefore, at origin t, **the GDP columns are available only for months ≤ t − 2 and the employment columns only for months ≤ t − 1**. A forecast made at origin t (its estimation sample, its model selection, its transformations and the values plugged into the forecast) may use only these observations. Training targets must also be observed at the origin.\n- Naive baseline: for both horizons, the forecast is the most recent GDP growth value available at the origin, i.e. `gdp_yoy` of month t − 2.\n- The errors are computed on the data as given (current vintage); the exercise simulates publication timing, not historical first-release values — do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — the forecast ledger, one row per (origin, horizon, model), columns: `origin_date` (YYYY-MM-DD, the `month` of the origin), `target_date`, `horizon` (integer), `forecast`, `actual` (the `gdp_yoy` value of the target month), `model` (`naive` or the value of `model_name`), `last_target_date_used` (YYYY-MM-DD: the latest `gdp_yoy` month entering the forecast, estimation included), `last_predictor_date_used` (YYYY-MM-DD: the latest `emp_yoy` month used, or empty if the model does not use employment). Expected: 48 × 2 × 2 = 192 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-conf-01\" |\n| `model_name` | string | — | short name of your model, used in the `model` column |\n| `model_description` | string | — | one-sentence specification (lags, predictors, estimation sample, method) |\n| `n_origins` | integer | — | 48 |\n| `first_origin`, `last_origin` | string | date | first and last origin dates |\n| `last_target_date_available` | string | date | latest `gdp_yoy` month available at the last origin (2026-04-01) under the rules |\n| `last_predictor_date_available` | string | date | latest `emp_yoy` month available at the last origin under the rules |\n| `rmse_naive_h1`, `mae_naive_h1`, `rmse_naive_h3`, `mae_naive_h3` | number | percentage points | naive errors per horizon |\n| `rmse_model_h1`, `mae_model_h1`, `rmse_model_h3`, `mae_model_h3` | number | percentage points | your model's errors per horizon |\n| `rel_improvement_rmse_h1`, `rel_improvement_rmse_h3` | number | fraction | 1 − rmse_model / rmse_naive |\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 | — | {\"canada_gdp_employment.csv\": number of data rows read, excluding the header} |\n| `n_analysed` | integer | — | observations in the last estimation sample |\n| `random_seed` | integer or null | — | seed used, if any |\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` and `out/forecasts.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\ncanada_gdp_employment.csv\nREADME.md","sandboxed":false,"sourceToolAssistantUUID":"35640961-d379-4cdb-b1f9-43fb7d9ad4fe","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","hash":"ffa0065a95a3d3b8e72e52d555404e13e40e03e099ccdcc852c057fdb9669647","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_014TRsXhgQsLQKWP81F6xkFa","name":"Bash","input":{"command":"cat TASK.md; ls -R | head -50; ls out/state 2>/dev/null && cat out/state/log.md","description":"Read task and list workspace"}}]}}16{"type":"api-request","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","id":"881f2200-4cd0-4ab1-bb29-e6d0c346b543","timestamp":"2026-10-30T05:01:45.924Z","version":"2.1.314","querySource":"sdk","shapeHash":"20be7962999c92ef9e1c6de3002f528972aba0b18ace953850ebe20697d81819","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":"a81d656b-144d-4a8e-91a5-ec75e1959ff2","keep":2,"tail":["ffa0065a95a3d3b8e72e52d555404e13e40e03e099ccdcc852c057fdb9669647","0d187921e7678827f1766f8df5bda9917b8640996035183b5110b3cb77c4d8c9"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"0b05fd6ef8dd07470040ab42981d8d3c801ddce7eff4a14eae2f30694a5c1113"}17{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"c9d4db37-7cde-4fdf-9315-66737f2a0b42","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca"}18{"type":"atis-latch","atis":"","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca"}19{"parentUuid":"c9d4db37-7cde-4fdf-9315-66737f2a0b42","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqMMWteXXDzN7QorWDc","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01X1FjZ1R33JrpqTszqMno93","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json | head -80; head -5 data/canada_gdp_employment.csv; tail -5 data/canada_gdp_employment.csv; wc -l data/*.csv","description":"Read README, gate spec, data samples"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2284,"cache_read_input_tokens":4107,"output_tokens":149,"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":2284},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqMMEWnWLDB2sgwy2zi","requestRef":"881f2200-4cd0-4ab1-bb29-e6d0c346b543","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"287a3b74-2453-447d-a2ad-d16ac5433722","timestamp":"2026-10-30T05:01:47.769Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}20{"parentUuid":"287a3b74-2453-447d-a2ad-d16ac5433722","isSidechain":false,"promptId":"2d8c293d-d814-4110-85d3-c0a929c36671","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01X1FjZ1R33JrpqTszqMno93","type":"tool_result","content":"# data/README.md — F3-conf-01\n\n## canada_gdp_employment.csv\nOne row per month (`month`, YYYY-MM-01), 1998-01 to 2026-09-01 (345 data rows), built from two Statistics Canada vectors retrieved through the Web Data Service:\n\n- **Endpoint**: `POST https://www150.statcan.gc.ca/t1/wds/rest/getDataFromVectorsAndLatestNPeriods` with body `[{\"vectorId\":65201210,\"latestN\":400},{\"vectorId\":2062811,\"latestN\":400}]`\n- **Retrieved (UTC)**: 2026-10-30T03:25:10Z\n- **SHA-256 of this file**: f4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed\n- **License**: Statistics Canada Open Licence (https://www.statcan.gc.ca/en/reference/licence).\n\nColumns:\n- `gdp_level` — vector v65201210, table 36-10-0434-01: Gross domestic product at basic prices, all industries, chained (2017) dollars, seasonally adjusted at annual rates, millions of dollars, monthly. Last available month: 2026-07 (the value for a reference month is published by Statistics Canada about two months after the end of that month — e.g. July 2026 GDP was published on 2026-09-30).\n- `gdp_yoy` — target series: year-over-year growth of `gdp_level`, 100 × (level_m / level_{m-12} − 1), rounded to 4 decimals (percent). Empty for the first 12 months.\n- `emp_level` — vector v2062811, table 14-10-0287-01: Employment, both sexes, 15 years and over, seasonally adjusted, thousands of persons, Labour Force Survey, monthly. Last available month: 2026-09 (published in the first or second week of the following month).\n- `emp_yoy` — predictor series: year-over-year growth of `emp_level`, same formula, percent, 4 decimals.\n\n**Publication lags (information rule used in TASK.md)**: at the end of month t, `gdp_*` is known for months ≤ t − 2 and `emp_*` for months ≤ t − 1. Both series are the current vintage as of retrieval (monthly GDP and LFS employment are revised after first publication); the file is therefore a final-vintage dataset and the exercise simulates only the publication *timing*, not the historical first-release values. No real-time (vintage) validity is claimed.\n{\n \"task_id\": \"F3-conf-01\",\n \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/forecasts.csv\"],\n \"results_keys\": {\n \"task_id\": {\"type\": \"string\", \"nullable\": false},\n \"model_name\": {\"type\": \"string\", \"nullable\": false},\n \"model_description\": {\"type\": \"string\", \"nullable\": false},\n \"n_origins\": {\"type\": \"integer\", \"nullable\": false},\n \"first_origin\": {\"type\": \"string\", \"nullable\": false},\n \"last_origin\": {\"type\": \"string\", \"nullable\": false},\n \"last_target_date_available\": {\"type\": \"string\", \"nullable\": false},\n \"last_predictor_date_available\": {\"type\": \"string\", \"nullable\": false},\n \"rmse_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n \"mae_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_naive_h3\": {\"type\": \"number\", \"nullable\": false},\n \"mae_naive_h3\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_model_h1\": {\"type\": \"number\", \"nullable\": false},\n \"mae_model_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_model_h3\": {\"type\": \"number\", \"nullable\": false},\n \"mae_model_h3\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_rmse_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_rmse_h3\": {\"type\": \"number\", \"nullable\": false},\n \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n \"n_analysed\": {\"type\": \"integer\", \"nullable\": true},\n \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n },\n \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n \"family_checks\": [\"chronology\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n \"ci_pairs\": [],\n \"positive_keys\": [\"rmse_naive_h1\", \"rmse_naive_h3\", \"rmse_model_h1\", \"rmse_model_h3\"],\n \"chronology_pairs\": [[\"first_origin\", \"last_origin\"], [\"last_target_date_available\", \"last_origin\"], [\"last_predictor_date_available\", \"last_origin\"]],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-6\n}\nmonth,gdp_level,gdp_yoy,emp_level,emp_yoy\n1998-01-01,1251276.0,4.2359,13864.8,2.4298\n1998-02-01,1263285.0,4.5780,13914.5,2.7803\n1998-03-01,1267784.0,4.9633,13949.4,2.7974\n1998-04-01,1268479.0,4.3134,13977.6,2.9946\n2026-05-01,2363071.0,1.6466,21121.5,0.7013\n2026-06-01,2371886.0,2.0909,21139.7,0.4705\n2026-07-01,2371771.0,1.4203,21214.8,0.9349\n2026-08-01,,,21173.1,1.0331\n2026-09-01,,,21104.8,0.4517\n 346 data/canada_gdp_employment.csv","is_error":false}]},"uuid":"fec1b76f-0a1d-43c7-b205-e9f4290cfd99","timestamp":"2026-10-30T05:01:47.819Z","toolUseResult":{"stdout":"# data/README.md — F3-conf-01\n\n## canada_gdp_employment.csv\nOne row per month (`month`, YYYY-MM-01), 1998-01 to 2026-09-01 (345 data rows), built from two Statistics Canada vectors retrieved through the Web Data Service:\n\n- **Endpoint**: `POST https://www150.statcan.gc.ca/t1/wds/rest/getDataFromVectorsAndLatestNPeriods` with body `[{\"vectorId\":65201210,\"latestN\":400},{\"vectorId\":2062811,\"latestN\":400}]`\n- **Retrieved (UTC)**: 2026-10-30T03:25:10Z\n- **SHA-256 of this file**: f4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed\n- **License**: Statistics Canada Open Licence (https://www.statcan.gc.ca/en/reference/licence).\n\nColumns:\n- `gdp_level` — vector v65201210, table 36-10-0434-01: Gross domestic product at basic prices, all industries, chained (2017) dollars, seasonally adjusted at annual rates, millions of dollars, monthly. Last available month: 2026-07 (the value for a reference month is published by Statistics Canada about two months after the end of that month — e.g. July 2026 GDP was published on 2026-09-30).\n- `gdp_yoy` — target series: year-over-year growth of `gdp_level`, 100 × (level_m / level_{m-12} − 1), rounded to 4 decimals (percent). Empty for the first 12 months.\n- `emp_level` — vector v2062811, table 14-10-0287-01: Employment, both sexes, 15 years and over, seasonally adjusted, thousands of persons, Labour Force Survey, monthly. Last available month: 2026-09 (published in the first or second week of the following month).\n- `emp_yoy` — predictor series: year-over-year growth of `emp_level`, same formula, percent, 4 decimals.\n\n**Publication lags (information rule used in TASK.md)**: at the end of month t, `gdp_*` is known for months ≤ t − 2 and `emp_*` for months ≤ t − 1. Both series are the current vintage as of retrieval (monthly GDP and LFS employment are revised after first publication); the file is therefore a final-vintage dataset and the exercise simulates only the publication *timing*, not the historical first-release values. No real-time (vintage) validity is claimed.\n{\n \"task_id\": \"F3-conf-01\",\n \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/forecasts.csv\"],\n \"results_keys\": {\n \"task_id\": {\"type\": \"string\", \"nullable\": false},\n \"model_name\": {\"type\": \"string\", \"nullable\": false},\n \"model_description\": {\"type\": \"string\", \"nullable\": false},\n \"n_origins\": {\"type\": \"integer\", \"nullable\": false},\n \"first_origin\": {\"type\": \"string\", \"nullable\": false},\n \"last_origin\": {\"type\": \"string\", \"nullable\": false},\n \"last_target_date_available\": {\"type\": \"string\", \"nullable\": false},\n \"last_predictor_date_available\": {\"type\": \"string\", \"nullable\": false},\n \"rmse_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n \"mae_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_naive_h3\": {\"type\": \"number\", \"nullable\": false},\n \"mae_naive_h3\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_model_h1\": {\"type\": \"number\", \"nullable\": false},\n \"mae_model_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_model_h3\": {\"type\": \"number\", \"nullable\": false},\n \"mae_model_h3\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_rmse_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_rmse_h3\": {\"type\": \"number\", \"nullable\": false},\n \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n \"n_analysed\": {\"type\": \"integer\", \"nullable\": true},\n \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n },\n \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n \"family_checks\": [\"chronology\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n \"ci_pairs\": [],\n \"positive_keys\": [\"rmse_naive_h1\", \"rmse_naive_h3\", \"rmse_model_h1\", \"rmse_model_h3\"],\n \"chronology_pairs\": [[\"first_origin\", \"last_origin\"], [\"last_target_date_available\", \"last_origin\"], [\"last_predictor_date_available\", \"last_origin\"]],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-6\n}\nmonth,gdp_level,gdp_yoy,emp_level,emp_yoy\n1998-01-01,1251276.0,4.2359,13864.8,2.4298\n1998-02-01,1263285.0,4.5780,13914.5,2.7803\n1998-03-01,1267784.0,4.9633,13949.4,2.7974\n1998-04-01,1268479.0,4.3134,13977.6,2.9946\n2026-05-01,2363071.0,1.6466,21121.5,0.7013\n2026-06-01,2371886.0,2.0909,21139.7,0.4705\n2026-07-01,2371771.0,1.4203,21214.8,0.9349\n2026-08-01,,,21173.1,1.0331\n2026-09-01,,,21104.8,0.4517\n 346 data/canada_gdp_employment.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"287a3b74-2453-447d-a2ad-d16ac5433722","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","hash":"4619890d86b7d61d5390a4ade21523c89ef3d67a7af7a4ddaca5a4a2b0dbcd18","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01X1FjZ1R33JrpqTszqMno93","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json | head -80; head -5 data/canada_gdp_employment.csv; tail -5 data/canada_gdp_employment.csv; wc -l data/*.csv","description":"Read README, gate spec, data samples"}}]}}22{"type":"api-request","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","id":"a8374e6f-8d90-48b2-a6ec-1d5b2b160ba6","timestamp":"2026-10-30T05:01:47.822Z","version":"2.1.314","querySource":"sdk","shapeHash":"20be7962999c92ef9e1c6de3002f528972aba0b18ace953850ebe20697d81819","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":"881f2200-4cd0-4ab1-bb29-e6d0c346b543","keep":4,"tail":["4619890d86b7d61d5390a4ade21523c89ef3d67a7af7a4ddaca5a4a2b0dbcd18","01a8bafbdde3b076763e7ae539a47aac04a20c6979d222b122a67c702340281e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"89d25825ba696984191ba26918b68299c48eb08c5fe43ceb6b43382925cfe0f9"}23{"parentUuid":"fec1b76f-0a1d-43c7-b205-e9f4290cfd99","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqMVe346hkrPDQFRdPY","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":2225,"cache_read_input_tokens":6391,"output_tokens":842,"output_tokens_details":{"thinking_tokens":443},"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":2225},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":4180,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqMVMgKSHD1iSG3tHgx","requestRef":"a8374e6f-8d90-48b2-a6ec-1d5b2b160ba6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"543fdf3f-745c-4373-915e-c8ad77705649","timestamp":"2026-10-30T05:01:53.232Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"543fdf3f-745c-4373-915e-c8ad77705649","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqMVe346hkrPDQFRdPY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013HTWehMXatgEmQFCEGNdce","name":"Bash","input":{"command":"mkdir -p out/state; shasum -a 256 data/canada_gdp_employment.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd,numpy as np\nd=pd.read_csv('data/canada_gdp_employment.csv',parse_dates=['month'])\nprint(d.shape,d.dtypes.to_dict());print(d.isna().sum());print(d.month.duplicated().sum(), (d.month.diff().dropna().dt.days>31).sum())\n# check yoy consistency\nfor a,b in [('gdp_level','gdp_yoy'),('emp_level','emp_yoy')]:\n r=(100*(d[a]/d[a].shift(12)-1)).round(4); print(b,(r-d[b]).abs().max())\nprint(d.describe())\nprint(d[(d.month>='2020-01-01')&(d.month<='2021-12-01')][['month','gdp_yoy','emp_yoy']].to_string())\n\"","description":"Audit data file"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2225,"cache_read_input_tokens":6391,"output_tokens":842,"output_tokens_details":{"thinking_tokens":443},"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":2225},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqMVMgKSHD1iSG3tHgx","requestRef":"a8374e6f-8d90-48b2-a6ec-1d5b2b160ba6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1725b111-46dc-4c82-8439-5eb17976bb8a","timestamp":"2026-10-30T05:01:56.074Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"1725b111-46dc-4c82-8439-5eb17976bb8a","isSidechain":false,"promptId":"2d8c293d-d814-4110-85d3-c0a929c36671","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013HTWehMXatgEmQFCEGNdce","type":"tool_result","content":"f4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed data/canada_gdp_employment.csv\n(345, 5) {'month': dtype('<M8[ns]'), 'gdp_level': dtype('float64'), 'gdp_yoy': dtype('float64'), 'emp_level': dtype('float64'), 'emp_yoy': dtype('float64')}\nmonth 0\ngdp_level 2\ngdp_yoy 2\nemp_level 0\nemp_yoy 0\ndtype: int64\n0 0\ngdp_yoy 0.0\nemp_yoy 0.0\n month gdp_level ... emp_level emp_yoy\ncount 345 3.430000e+02 ... 345.000000 345.000000\nmean 2012-05-01 10:09:23.478260992 1.813643e+06 ... 17454.385797 1.540610\nmin 1998-01-01 00:00:00 1.251276e+06 ... 13864.800000 -15.576500\n25% 2005-03-01 00:00:00 1.586989e+06 ... 16047.100000 1.028600\n50% 2012-05-01 00:00:00 1.788771e+06 ... 17338.600000 1.679100\n75% 2019-07-01 00:00:00 2.056191e+06 ... 18673.800000 2.322000\nmax 2026-09-01 00:00:00 2.371886e+06 ... 21214.800000 16.088700\nstd NaN 3.003681e+05 ... 1919.265534 2.263768\n\n[8 rows x 5 columns]\n month gdp_yoy emp_yoy\n264 2020-01-01 2.1209 1.5021\n265 2020-02-01 2.6658 1.2507\n266 2020-03-01 -5.6070 -4.6200\n267 2020-04-01 -16.2055 -15.5765\n268 2020-05-01 -12.6253 -13.9328\n269 2020-06-01 -7.6173 -8.4346\n270 2020-07-01 -5.2884 -6.1707\n271 2020-08-01 -4.3527 -5.2504\n272 2020-09-01 -3.5236 -3.0659\n273 2020-10-01 -3.0057 -2.6894\n274 2020-11-01 -2.3377 -2.3397\n275 2020-12-01 -2.5523 -3.1001\n276 2021-01-01 -2.0402 -4.2313\n277 2021-02-01 -2.0966 -2.9730\n278 2021-03-01 7.0112 4.5121\n279 2021-04-01 18.5426 16.0887\n280 2021-05-01 13.1156 13.4845\n281 2021-06-01 7.7967 8.1571\n282 2021-07-01 5.8090 6.2641\n283 2021-08-01 5.6440 5.2534\n284 2021-09-01 5.1656 3.7465\n285 2021-10-01 5.3946 3.6081\n286 2021-11-01 5.1683 4.2214\n287 2021-12-01 5.3176 4.9328","is_error":false}]},"uuid":"2070831e-8803-40aa-93d4-cd014b78f6eb","timestamp":"2026-10-30T05:01:56.375Z","toolUseResult":{"stdout":"f4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed data/canada_gdp_employment.csv\n(345, 5) {'month': dtype('<M8[ns]'), 'gdp_level': dtype('float64'), 'gdp_yoy': dtype('float64'), 'emp_level': dtype('float64'), 'emp_yoy': dtype('float64')}\nmonth 0\ngdp_level 2\ngdp_yoy 2\nemp_level 0\nemp_yoy 0\ndtype: int64\n0 0\ngdp_yoy 0.0\nemp_yoy 0.0\n month gdp_level ... emp_level emp_yoy\ncount 345 3.430000e+02 ... 345.000000 345.000000\nmean 2012-05-01 10:09:23.478260992 1.813643e+06 ... 17454.385797 1.540610\nmin 1998-01-01 00:00:00 1.251276e+06 ... 13864.800000 -15.576500\n25% 2005-03-01 00:00:00 1.586989e+06 ... 16047.100000 1.028600\n50% 2012-05-01 00:00:00 1.788771e+06 ... 17338.600000 1.679100\n75% 2019-07-01 00:00:00 2.056191e+06 ... 18673.800000 2.322000\nmax 2026-09-01 00:00:00 2.371886e+06 ... 21214.800000 16.088700\nstd NaN 3.003681e+05 ... 1919.265534 2.263768\n\n[8 rows x 5 columns]\n month gdp_yoy emp_yoy\n264 2020-01-01 2.1209 1.5021\n265 2020-02-01 2.6658 1.2507\n266 2020-03-01 -5.6070 -4.6200\n267 2020-04-01 -16.2055 -15.5765\n268 2020-05-01 -12.6253 -13.9328\n269 2020-06-01 -7.6173 -8.4346\n270 2020-07-01 -5.2884 -6.1707\n271 2020-08-01 -4.3527 -5.2504\n272 2020-09-01 -3.5236 -3.0659\n273 2020-10-01 -3.0057 -2.6894\n274 2020-11-01 -2.3377 -2.3397\n275 2020-12-01 -2.5523 -3.1001\n276 2021-01-01 -2.0402 -4.2313\n277 2021-02-01 -2.0966 -2.9730\n278 2021-03-01 7.0112 4.5121\n279 2021-04-01 18.5426 16.0887\n280 2021-05-01 13.1156 13.4845\n281 2021-06-01 7.7967 8.1571\n282 2021-07-01 5.8090 6.2641\n283 2021-08-01 5.6440 5.2534\n284 2021-09-01 5.1656 3.7465\n285 2021-10-01 5.3946 3.6081\n286 2021-11-01 5.1683 4.2214\n287 2021-12-01 5.3176 4.9328","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"1725b111-46dc-4c82-8439-5eb17976bb8a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}26{"type":"api-request-blob","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","hash":"c7918ff311baa4ce045a0cb32de76ce73d88f87acf6741dc921140c9fac93aa8","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_013HTWehMXatgEmQFCEGNdce","name":"Bash","input":{"command":"mkdir -p out/state; shasum -a 256 data/canada_gdp_employment.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd,numpy as np\nd=pd.read_csv('data/canada_gdp_employment.csv',parse_dates=['month'])\nprint(d.shape,d.dtypes.to_dict());print(d.isna().sum());print(d.month.duplicated().sum(), (d.month.diff().dropna().dt.days>31).sum())\n# check yoy consistency\nfor a,b in [('gdp_level','gdp_yoy'),('emp_level','emp_yoy')]:\n r=(100*(d[a]/d[a].shift(12)-1)).round(4); print(b,(r-d[b]).abs().max())\nprint(d.describe())\nprint(d[(d.month>='2020-01-01')&(d.month<='2021-12-01')][['month','gdp_yoy','emp_yoy']].to_string())\n\"","description":"Audit data file"}}]}}27{"type":"api-request","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","id":"02da55b5-a873-4e2c-8b2d-d441c78c071a","timestamp":"2026-10-30T05:01:56.379Z","version":"2.1.314","querySource":"sdk","shapeHash":"20be7962999c92ef9e1c6de3002f528972aba0b18ace953850ebe20697d81819","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":"a8374e6f-8d90-48b2-a6ec-1d5b2b160ba6","keep":6,"tail":["c7918ff311baa4ce045a0cb32de76ce73d88f87acf6741dc921140c9fac93aa8","5f453817fb671aa6fd1b59e2aeb9c2bf653e600d10cc90738bd96de18726e6f9"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"b82bd1dc3b18de39c24ba0e9f3602717066af8182de107db4ada5ad14880dc3f"}28{"parentUuid":"2070831e-8803-40aa-93d4-cd014b78f6eb","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqN8Dic9yDvEQrwdf1Q","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":1892,"cache_read_input_tokens":8616,"output_tokens":1235,"output_tokens_details":{"thinking_tokens":228},"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":1892},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2031,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqN7wqj6gXXo59b8fVi","requestRef":"02da55b5-a873-4e2c-8b2d-d441c78c071a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b234638c-29dc-4202-adb2-3e35de93940a","timestamp":"2026-10-30T05:01:59.647Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"b234638c-29dc-4202-adb2-3e35de93940a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqN8Dic9yDvEQrwdf1Q","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PhYwrrT7buroCJha72FHh1","name":"Bash","input":{"command":"mkdir -p out/state && cat > out/state/contract.md <<'EOF'\n# Contract (P0)\n**Question.** At the end of each month t (48 origins 2022-05..2026-04), forecast Canadian monthly real GDP y/y growth for months t+1 and t+3 using only data published by then; compare a naive forecast with one model that also uses LFS employment y/y growth.\n\n**Estimands.** Out-of-sample RMSE and MAE (pp) per horizon h∈{1,3} over the 48 origins for (a) naive = gdp_yoy(t-2), (b) my model; relative improvement 1 − RMSE_model/RMSE_naive. Meaningful under: current-vintage data (timing simulation only, not real-time vintages), fixed origin set, squared/absolute loss.\n\n**Information rules.** At origin t: gdp columns only months ≤ t−2, emp columns only months ≤ t−1. Estimation samples, transforms, selection and plug-in values restricted accordingly; training targets must be observed (≤ t−2). Targets gdp_yoy(t+h); the latest target is 2026-07 (origin 2026-04, h=3) — available in file.\n\n**Outputs.** out/forecasts.csv (192 rows: origin_date,target_date,horizon,forecast,actual,model,last_target_date_used,last_predictor_date_used); out/results.json (task_id, model_name, model_description, n_origins, first_origin, last_origin, last_target_date_available, last_predictor_date_available, rmse/mae naive & model h1/h3, rel_improvement_rmse_h1/h3, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py; out/claims.json; plus state files and out/exclusions.csv.\n\n**Invalidating conditions.** Any use of data after t−2 (GDP) / t−1 (emp) at origin t; missing targets for any origin/horizon; claiming real-time validity.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md","description":"Write contract, phase ledger, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1892,"cache_read_input_tokens":8616,"output_tokens":1235,"output_tokens_details":{"thinking_tokens":228},"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":1892},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqN7wqj6gXXo59b8fVi","requestRef":"02da55b5-a873-4e2c-8b2d-d441c78c071a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"51e15ccc-7ce0-42e7-97aa-11287e776cf8","timestamp":"2026-10-30T05:02:07.276Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}30{"parentUuid":"51e15ccc-7ce0-42e7-97aa-11287e776cf8","isSidechain":false,"promptId":"2d8c293d-d814-4110-85d3-c0a929c36671","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PhYwrrT7buroCJha72FHh1","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"58643f10-8407-4f59-8a58-4820d263aebf","timestamp":"2026-10-30T05:02:07.325Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"51e15ccc-7ce0-42e7-97aa-11287e776cf8","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}31{"type":"api-request-blob","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","hash":"4ca45871aeb4916cf00a0d10d9f424c9dbbd08411a26a5860545cccfa449e839","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01PhYwrrT7buroCJha72FHh1","name":"Bash","input":{"command":"mkdir -p out/state && cat > out/state/contract.md <<'EOF'\n# Contract (P0)\n**Question.** At the end of each month t (48 origins 2022-05..2026-04), forecast Canadian monthly real GDP y/y growth for months t+1 and t+3 using only data published by then; compare a naive forecast with one model that also uses LFS employment y/y growth.\n\n**Estimands.** Out-of-sample RMSE and MAE (pp) per horizon h∈{1,3} over the 48 origins for (a) naive = gdp_yoy(t-2), (b) my model; relative improvement 1 − RMSE_model/RMSE_naive. Meaningful under: current-vintage data (timing simulation only, not real-time vintages), fixed origin set, squared/absolute loss.\n\n**Information rules.** At origin t: gdp columns only months ≤ t−2, emp columns only months ≤ t−1. Estimation samples, transforms, selection and plug-in values restricted accordingly; training targets must be observed (≤ t−2). Targets gdp_yoy(t+h); the latest target is 2026-07 (origin 2026-04, h=3) — available in file.\n\n**Outputs.** out/forecasts.csv (192 rows: origin_date,target_date,horizon,forecast,actual,model,last_target_date_used,last_predictor_date_used); out/results.json (task_id, model_name, model_description, n_origins, first_origin, last_origin, last_target_date_available, last_predictor_date_available, rmse/mae naive & model h1/h3, rel_improvement_rmse_h1/h3, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py; out/claims.json; plus state files and out/exclusions.csv.\n\n**Invalidating conditions.** Any use of data after t−2 (GDP) / t−1 (emp) at origin t; missing targets for any origin/horizon; claiming real-time validity.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md","description":"Write contract, phase ledger, log"}}]}}32{"type":"api-request","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","id":"869b1ff4-2263-4b68-b380-d6696b8e69f9","timestamp":"2026-10-30T05:02:07.328Z","version":"2.1.314","querySource":"sdk","shapeHash":"20be7962999c92ef9e1c6de3002f528972aba0b18ace953850ebe20697d81819","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":"02da55b5-a873-4e2c-8b2d-d441c78c071a","keep":8,"tail":["4ca45871aeb4916cf00a0d10d9f424c9dbbd08411a26a5860545cccfa449e839","8a67565b7fd424eaed2fb7d8f195184c24d3f6e2502a301d8510bbcc3a2b473b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"16c472279b94b556d9b4d78f3c09ec841739a862585750009311b2adf2532dfd"}33{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"58643f10-8407-4f59-8a58-4820d263aebf","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca"}34{"type":"atis-latch","atis":"","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca"}35{"parentUuid":"58643f10-8407-4f59-8a58-4820d263aebf","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqNw4HRmcyAXF7UG3hP","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0158jBNryRaaqAEpFTtfzKxF","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\nFile: data/canada_gdp_employment.csv\n- SHA-256: f4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed (matches data/README.md)\n- 345 data rows + header, 5 columns: month (date, YYYY-MM-01), gdp_level (float, M chained 2017 CAD, SAAR), gdp_yoy (float, %), emp_level (float, thousands), emp_yoy (float, %)\n- Date range 1998-01-01 .. 2026-09-01, contiguous monthly, 0 duplicate months.\n- Missing: gdp_level and gdp_yoy missing for 2026-08 and 2026-09 only (not yet published, as documented). emp complete.\n- Consistency: recomputed 100*(L_m/L_{m-12}-1) rounded to 4 dp equals the given yoy exactly (max abs diff 0.0) for both series wherever the 12-month lag is in the file (from 1999-01). The 1998 yoy values depend on 1997 levels not in the file and cannot be verified; accepted as given.\n- Anomalies: COVID-19 shock 2020-03..2021-12: gdp_yoy ranges −16.2 to +18.5, emp_yoy −15.6 to +16.1 (base effects in 2021). Real, not errors, but extreme leverage for linear regressions.\n- Information availability: the file is a current vintage (revised data); timing is simulated by restricting rows per origin (GDP ≤ t−2, emp ≤ t−1). No input column violates rules once that filter is applied; the post-origin rows are used only as actuals.\n- Handling decisions: (1) no row deleted from the file; (2) per-origin information filter; (3) the model's estimation sample excludes regression rows whose target month or any regressor month falls in 2020-03..2021-12 (pandemic window; decided before computing any out-of-sample error). Logged in out/exclusions.csv for the last estimation sample.\nEOF\necho \"- 2026-10-30 P1: audit done; decision to exclude 2020-03..2021-12 from model estimation rows (pre-specified).\" >> out/state/log.md","description":"Write data audit"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1250,"cache_read_input_tokens":10508,"output_tokens":845,"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":1250},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqNvkRtTA6rAPmcmbgx","requestRef":"869b1ff4-2263-4b68-b380-d6696b8e69f9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0f3c5af0-df86-4de0-acc9-502c9250b9d7","timestamp":"2026-10-30T05:02:15.820Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"0f3c5af0-df86-4de0-acc9-502c9250b9d7","isSidechain":false,"promptId":"2d8c293d-d814-4110-85d3-c0a929c36671","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0158jBNryRaaqAEpFTtfzKxF","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"2e251b3d-9d0d-4a39-b103-95b738642e97","timestamp":"2026-10-30T05:02:15.868Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"0f3c5af0-df86-4de0-acc9-502c9250b9d7","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}37{"type":"api-request-blob","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","hash":"12e6e10fefe4bb293bf07b081eb8e64b556ba39d60d3f2ef5a0d3fd8f69f85e1","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_0158jBNryRaaqAEpFTtfzKxF","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\nFile: data/canada_gdp_employment.csv\n- SHA-256: f4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed (matches data/README.md)\n- 345 data rows + header, 5 columns: month (date, YYYY-MM-01), gdp_level (float, M chained 2017 CAD, SAAR), gdp_yoy (float, %), emp_level (float, thousands), emp_yoy (float, %)\n- Date range 1998-01-01 .. 2026-09-01, contiguous monthly, 0 duplicate months.\n- Missing: gdp_level and gdp_yoy missing for 2026-08 and 2026-09 only (not yet published, as documented). emp complete.\n- Consistency: recomputed 100*(L_m/L_{m-12}-1) rounded to 4 dp equals the given yoy exactly (max abs diff 0.0) for both series wherever the 12-month lag is in the file (from 1999-01). The 1998 yoy values depend on 1997 levels not in the file and cannot be verified; accepted as given.\n- Anomalies: COVID-19 shock 2020-03..2021-12: gdp_yoy ranges −16.2 to +18.5, emp_yoy −15.6 to +16.1 (base effects in 2021). Real, not errors, but extreme leverage for linear regressions.\n- Information availability: the file is a current vintage (revised data); timing is simulated by restricting rows per origin (GDP ≤ t−2, emp ≤ t−1). No input column violates rules once that filter is applied; the post-origin rows are used only as actuals.\n- Handling decisions: (1) no row deleted from the file; (2) per-origin information filter; (3) the model's estimation sample excludes regression rows whose target month or any regressor month falls in 2020-03..2021-12 (pandemic window; decided before computing any out-of-sample error). Logged in out/exclusions.csv for the last estimation sample.\nEOF\necho \"- 2026-10-30 P1: audit done; decision to exclude 2020-03..2021-12 from model estimation rows (pre-specified).\" >> out/state/log.md","description":"Write data audit"}}]}}38{"type":"api-request","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","id":"9c14a5fe-1ec4-4b7f-a544-b6bc3694b532","timestamp":"2026-10-30T05:02:15.872Z","version":"2.1.314","querySource":"sdk","shapeHash":"20be7962999c92ef9e1c6de3002f528972aba0b18ace953850ebe20697d81819","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":"869b1ff4-2263-4b68-b380-d6696b8e69f9","keep":10,"tail":["12e6e10fefe4bb293bf07b081eb8e64b556ba39d60d3f2ef5a0d3fd8f69f85e1","bff706af8a723e4760f77868834a98268cb8f69dd39be2b7a76eacabf3c3c5c2"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"066949142e891d753139a07c60bb752bbf2025028c8f33727cebfdb616ca1e96"}39{"parentUuid":"2e251b3d-9d0d-4a39-b103-95b738642e97","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqPZaj283WY4hAAkUzr","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":860,"cache_read_input_tokens":11758,"output_tokens":3341,"output_tokens_details":{"thinking_tokens":254},"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":860},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2152,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqPZJ7W4FmBrUrY5bt4","requestRef":"9c14a5fe-1ec4-4b7f-a544-b6bc3694b532","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9e07e3a3-e17c-44de-8a64-2c310b003fc5","timestamp":"2026-10-30T05:02:19.242Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"9e07e3a3-e17c-44de-8a64-2c310b003fc5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqPZaj283WY4hAAkUzr","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LtjRmVFMAzTQdJTiUQNxp1","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-01: GDP y/y forecasts under publication lags. Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\nReads only data/, writes out/results.json, out/forecasts.csv, out/exclusions.csv. Deterministic (no randomness).\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\n\nDATA = \"data/canada_gdp_employment.csv\"\nOUT = \"out\"\nMODEL = \"dir_ols_emp\"\nCOVID = (pd.Timestamp(\"2020-03-01\"), pd.Timestamp(\"2021-12-01\"))\n\nd = pd.read_csv(DATA, parse_dates=[\"month\"]).sort_values(\"month\").reset_index(drop=True)\nn_raw = len(d)\nassert d.month.is_unique\nd = d.set_index(\"month\")\nM = lambda t, k: t + pd.DateOffset(months=k)\ndef ds(t): return t.strftime(\"%Y-%m-%d\")\ndef in_covid(m): return (m >= COVID[0]) & (m <= COVID[1])\n\norigins = pd.date_range(\"2022-05-01\", \"2026-04-01\", freq=\"MS\")\nassert len(origins) == 48\n\ndef build_rows(t, h):\n \"\"\"Direct regression rows y_s ~ 1 + y_{s-h-2} + x_{s-h-1} using only info at origin t.\"\"\"\n gdp = d.loc[d.index <= M(t, -2), \"gdp_yoy\"].dropna() # GDP known up to t-2\n emp = d.loc[d.index <= M(t, -1), \"emp_yoy\"].dropna() # emp known up to t-1\n s = gdp.index\n df = pd.DataFrame({\"y\": gdp.values,\n \"ylag\": gdp.reindex(s - pd.DateOffset(months=h + 2)).values,\n \"xlag\": emp.reindex(s - pd.DateOffset(months=h + 1)).values}, index=s)\n n_avail = len(df)\n df = df.dropna()\n n_lagged = len(df)\n bad = in_covid(df.index) | in_covid(df.index - pd.DateOffset(months=h + 2)) | in_covid(df.index - pd.DateOffset(months=h + 1))\n df = df[~bad]\n return df, gdp, emp, n_avail, n_lagged\n\nrows = []\nlast_info = {}\nfor t in origins:\n for h in (1, 3):\n tgt = M(t, h)\n actual = float(d.loc[tgt, \"gdp_yoy\"])\n df, gdp, emp, n_avail, n_lagged = build_rows(t, h)\n y_last, x_last = gdp.index.max(), emp.index.max()\n assert y_last <= M(t, -2) and x_last <= M(t, -1)\n # naive\n rows.append(dict(origin_date=ds(t), target_date=ds(tgt), horizon=h, forecast=float(gdp.loc[M(t, -2)]),\n actual=actual, model=\"naive\", last_target_date_used=ds(M(t, -2)), last_predictor_date_used=\"\"))\n # model\n X = np.column_stack([np.ones(len(df)), df.ylag.values, df.xlag.values])\n beta, *_ = np.linalg.lstsq(X, df.y.values, rcond=None)\n xnew = np.array([1.0, gdp.loc[M(t, -2)], emp.loc[M(t, -1)]])\n fc = float(xnew @ beta)\n rows.append(dict(origin_date=ds(t), target_date=ds(tgt), horizon=h, forecast=fc, actual=actual, model=MODEL,\n last_target_date_used=ds(max(df.index.max(), M(t, -2))),\n last_predictor_date_used=ds(M(t, -1))))\n last_info[h] = dict(n=len(df), n_avail=n_avail, n_lagged=n_lagged, beta=beta.tolist())\n\nfc = pd.DataFrame(rows)\nfc.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.6f\")\n\ndef err(model, h):\n e = fc[(fc.model == model) & (fc.horizon == h)]\n e = e.forecast - e.actual\n assert len(e) == 48\n return float(np.sqrt(np.mean(e ** 2))), float(np.mean(np.abs(e)))\n\nres = {\"task_id\": \"F3-conf-01\", \"model_name\": MODEL,\n \"model_description\": (\"Direct h-step OLS of gdp_yoy(s) on a constant, gdp_yoy(s-h-2) and emp_yoy(s-h-1), \"\n \"re-estimated at each origin on an expanding window from 1998 with targets <= t-2, \"\n \"excluding rows whose target or regressor month lies in 2020-03..2021-12; \"\n \"forecast plugs in gdp_yoy(t-2) and emp_yoy(t-1).\"),\n \"n_origins\": int(len(origins)), \"first_origin\": ds(origins[0]), \"last_origin\": ds(origins[-1]),\n \"last_target_date_available\": ds(M(origins[-1], -2)),\n \"last_predictor_date_available\": ds(M(origins[-1], -1))}\nfor m, tag in ((\"naive\", \"naive\"), (MODEL, \"model\")):\n for h in (1, 3):\n r, a = err(m, h)\n res[f\"rmse_{tag}_h{h}\"], res[f\"mae_{tag}_h{h}\"] = r, a\nfor h in (1, 3):\n res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\nres.update({\"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": \"gdp_level/gdp_yoy missing for 2026-08 and 2026-09 (not yet published)\",\n \"handling\": \"Not needed: latest target is 2026-07; rows never used as GDP information.\"},\n {\"issue\": \"gdp_yoy and emp_yoy for 1998 depend on 1997 levels absent from the file and cannot be re-verified (all later yoy values match levels exactly)\",\n \"handling\": \"Accepted as given.\"},\n {\"issue\": \"COVID-19 shock 2020-03..2021-12 with yoy values between -16 and +19 percent (extreme leverage)\",\n \"handling\": \"Regression rows touching this window excluded from model estimation (pre-specified); naive unaffected.\"},\n {\"issue\": \"Current-vintage (revised) data, not historical first releases\",\n \"handling\": \"Only publication timing simulated; no real-time validity claimed.\"}],\n \"n_input_rows\": {\"canada_gdp_employment.csv\": int(n_raw)},\n \"n_analysed\": int(last_info[1][\"n\"]), \"random_seed\": None})\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\n\n# exclusion ledger for the last estimation sample (origin 2026-04, h=1)\nli = last_info[1]\nn_info = int((d.index <= M(origins[-1], -2)).sum())\nsteps = [(\"info_filter\", \"keep months <= t-2 (GDP published) at last origin 2026-04-01\", n_raw, n_info,\n \"publication-lag rule\"),\n (\"lag_availability\", \"drop rows lacking gdp_yoy(s-3) or emp_yoy(s-2) (start of sample)\", n_info, li[\"n_lagged\"],\n \"regressors not in file\"),\n (\"covid_window\", \"drop rows with target or regressor month in 2020-03..2021-12\", li[\"n_lagged\"], li[\"n\"],\n \"pandemic outliers; pre-specified\")]\nassert li[\"n_avail\"] == n_info\npd.DataFrame([dict(step=s, rule=r, n_before=a, n_after=b, n_removed=a - b, rationale=q) for s, r, a, b, q in steps]\n ).to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\nprint(json.dumps({k: v for k, v in res.items() if k.startswith((\"rmse\", \"mae\", \"rel\"))}, indent=1))\nprint(\"last-origin coefficients [const, gdp lag, emp lag]: h1\", np.round(last_info[1][\"beta\"], 4).tolist(),\n \"h3\", np.round(last_info[3][\"beta\"], 4).tolist())\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && head -5 out/forecasts.csv && wc -l out/forecasts.csv","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":860,"cache_read_input_tokens":11758,"output_tokens":3341,"output_tokens_details":{"thinking_tokens":254},"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":860},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqPZJ7W4FmBrUrY5bt4","requestRef":"9c14a5fe-1ec4-4b7f-a544-b6bc3694b532","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7343a679-c4ef-49e0-8adf-d095062de5e7","timestamp":"2026-10-30T05:02:41.768Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"7343a679-c4ef-49e0-8adf-d095062de5e7","isSidechain":false,"promptId":"2d8c293d-d814-4110-85d3-c0a929c36671","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LtjRmVFMAzTQdJTiUQNxp1","type":"tool_result","content":"{\n \"rmse_naive_h1\": 0.813620914876824,\n \"mae_naive_h1\": 0.6421395833333333,\n \"rmse_naive_h3\": 1.1399827160604965,\n \"mae_naive_h3\": 0.8715583333333333,\n \"rmse_model_h1\": 0.7429391714223801,\n \"mae_model_h1\": 0.6039917578066155,\n \"rmse_model_h3\": 0.9023000239646665,\n \"mae_model_h3\": 0.7534546734385598,\n \"rel_improvement_rmse_h1\": 0.08687306602134803,\n \"rel_improvement_rmse_h3\": 0.20849675065004825\n}\nlast-origin coefficients [const, gdp lag, emp lag]: h1 [0.2985, 0.8161, 0.0581] h3 [0.6859, 0.6597, 0.0322]\nstep,rule,n_before,n_after,n_removed,rationale\ninfo_filter,keep months <= t-2 (GDP published) at last origin 2026-04-01,345,338,7,publication-lag rule\nlag_availability,drop rows lacking gdp_yoy(s-3) or emp_yoy(s-2) (start of sample),338,335,3,regressors not in file\ncovid_window,drop rows with target or regressor month in 2020-03..2021-12,335,310,25,pandemic outliers; pre-specified\norigin_date,target_date,horizon,forecast,actual,model,last_target_date_used,last_predictor_date_used\n2022-05-01,2022-06-01,1,4.933500,5.957100,naive,2022-03-01,\n2022-05-01,2022-06-01,1,4.713444,5.957100,dir_ols_emp,2022-03-01,2022-04-01\n2022-05-01,2022-08-01,3,4.933500,4.831700,naive,2022-03-01,\n2022-05-01,2022-08-01,3,4.392103,4.831700,dir_ols_emp,2022-03-01,2022-04-01\n 193 out/forecasts.csv","is_error":false}]},"uuid":"4556ccea-5c85-4430-bb84-5daa3517b272","timestamp":"2026-10-30T05:02:42.147Z","toolUseResult":{"stdout":"{\n \"rmse_naive_h1\": 0.813620914876824,\n \"mae_naive_h1\": 0.6421395833333333,\n \"rmse_naive_h3\": 1.1399827160604965,\n \"mae_naive_h3\": 0.8715583333333333,\n \"rmse_model_h1\": 0.7429391714223801,\n \"mae_model_h1\": 0.6039917578066155,\n \"rmse_model_h3\": 0.9023000239646665,\n \"mae_model_h3\": 0.7534546734385598,\n \"rel_improvement_rmse_h1\": 0.08687306602134803,\n \"rel_improvement_rmse_h3\": 0.20849675065004825\n}\nlast-origin coefficients [const, gdp lag, emp lag]: h1 [0.2985, 0.8161, 0.0581] h3 [0.6859, 0.6597, 0.0322]\nstep,rule,n_before,n_after,n_removed,rationale\ninfo_filter,keep months <= t-2 (GDP published) at last origin 2026-04-01,345,338,7,publication-lag rule\nlag_availability,drop rows lacking gdp_yoy(s-3) or emp_yoy(s-2) (start of sample),338,335,3,regressors not in file\ncovid_window,drop rows with target or regressor month in 2020-03..2021-12,335,310,25,pandemic outliers; pre-specified\norigin_date,target_date,horizon,forecast,actual,model,last_target_date_used,last_predictor_date_used\n2022-05-01,2022-06-01,1,4.933500,5.957100,naive,2022-03-01,\n2022-05-01,2022-06-01,1,4.713444,5.957100,dir_ols_emp,2022-03-01,2022-04-01\n2022-05-01,2022-08-01,3,4.933500,4.831700,naive,2022-03-01,\n2022-05-01,2022-08-01,3,4.392103,4.831700,dir_ols_emp,2022-03-01,2022-04-01\n 193 out/forecasts.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"7343a679-c4ef-49e0-8adf-d095062de5e7","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}42{"type":"api-request-blob","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","hash":"5c2ec2c3661624dcc8559ebc84098edf6777fc4d23139e51e48b73fcc269977d","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01LtjRmVFMAzTQdJTiUQNxp1","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-01: GDP y/y forecasts under publication lags. Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\nReads only data/, writes out/results.json, out/forecasts.csv, out/exclusions.csv. Deterministic (no randomness).\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\n\nDATA = \"data/canada_gdp_employment.csv\"\nOUT = \"out\"\nMODEL = \"dir_ols_emp\"\nCOVID = (pd.Timestamp(\"2020-03-01\"), pd.Timestamp(\"2021-12-01\"))\n\nd = pd.read_csv(DATA, parse_dates=[\"month\"]).sort_values(\"month\").reset_index(drop=True)\nn_raw = len(d)\nassert d.month.is_unique\nd = d.set_index(\"month\")\nM = lambda t, k: t + pd.DateOffset(months=k)\ndef ds(t): return t.strftime(\"%Y-%m-%d\")\ndef in_covid(m): return (m >= COVID[0]) & (m <= COVID[1])\n\norigins = pd.date_range(\"2022-05-01\", \"2026-04-01\", freq=\"MS\")\nassert len(origins) == 48\n\ndef build_rows(t, h):\n \"\"\"Direct regression rows y_s ~ 1 + y_{s-h-2} + x_{s-h-1} using only info at origin t.\"\"\"\n gdp = d.loc[d.index <= M(t, -2), \"gdp_yoy\"].dropna() # GDP known up to t-2\n emp = d.loc[d.index <= M(t, -1), \"emp_yoy\"].dropna() # emp known up to t-1\n s = gdp.index\n df = pd.DataFrame({\"y\": gdp.values,\n \"ylag\": gdp.reindex(s - pd.DateOffset(months=h + 2)).values,\n \"xlag\": emp.reindex(s - pd.DateOffset(months=h + 1)).values}, index=s)\n n_avail = len(df)\n df = df.dropna()\n n_lagged = len(df)\n bad = in_covid(df.index) | in_covid(df.index - pd.DateOffset(months=h + 2)) | in_covid(df.index - pd.DateOffset(months=h + 1))\n df = df[~bad]\n return df, gdp, emp, n_avail, n_lagged\n\nrows = []\nlast_info = {}\nfor t in origins:\n for h in (1, 3):\n tgt = M(t, h)\n actual = float(d.loc[tgt, \"gdp_yoy\"])\n df, gdp, emp, n_avail, n_lagged = build_rows(t, h)\n y_last, x_last = gdp.index.max(), emp.index.max()\n assert y_last <= M(t, -2) and x_last <= M(t, -1)\n # naive\n rows.append(dict(origin_date=ds(t), target_date=ds(tgt), horizon=h, forecast=float(gdp.loc[M(t, -2)]),\n actual=actual, model=\"naive\", last_target_date_used=ds(M(t, -2)), last_predictor_date_used=\"\"))\n # model\n X = np.column_stack([np.ones(len(df)), df.ylag.values, df.xlag.values])\n beta, *_ = np.linalg.lstsq(X, df.y.values, rcond=None)\n xnew = np.array([1.0, gdp.loc[M(t, -2)], emp.loc[M(t, -1)]])\n fc = float(xnew @ beta)\n rows.append(dict(origin_date=ds(t), target_date=ds(tgt), horizon=h, forecast=fc, actual=actual, model=MODEL,\n last_target_date_used=ds(max(df.index.max(), M(t, -2))),\n last_predictor_date_used=ds(M(t, -1))))\n last_info[h] = dict(n=len(df), n_avail=n_avail, n_lagged=n_lagged, beta=beta.tolist())\n\nfc = pd.DataFrame(rows)\nfc.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.6f\")\n\ndef err(model, h):\n e = fc[(fc.model == model) & (fc.horizon == h)]\n e = e.forecast - e.actual\n assert len(e) == 48\n return float(np.sqrt(np.mean(e ** 2))), float(np.mean(np.abs(e)))\n\nres = {\"task_id\": \"F3-conf-01\", \"model_name\": MODEL,\n \"model_description\": (\"Direct h-step OLS of gdp_yoy(s) on a constant, gdp_yoy(s-h-2) and emp_yoy(s-h-1), \"\n \"re-estimated at each origin on an expanding window from 1998 with targets <= t-2, \"\n \"excluding rows whose target or regressor month lies in 2020-03..2021-12; \"\n \"forecast plugs in gdp_yoy(t-2) and emp_yoy(t-1).\"),\n \"n_origins\": int(len(origins)), \"first_origin\": ds(origins[0]), \"last_origin\": ds(origins[-1]),\n \"last_target_date_available\": ds(M(origins[-1], -2)),\n \"last_predictor_date_available\": ds(M(origins[-1], -1))}\nfor m, tag in ((\"naive\", \"naive\"), (MODEL, \"model\")):\n for h in (1, 3):\n r, a = err(m, h)\n res[f\"rmse_{tag}_h{h}\"], res[f\"mae_{tag}_h{h}\"] = r, a\nfor h in (1, 3):\n res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\nres.update({\"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": \"gdp_level/gdp_yoy missing for 2026-08 and 2026-09 (not yet published)\",\n \"handling\": \"Not needed: latest target is 2026-07; rows never used as GDP information.\"},\n {\"issue\": \"gdp_yoy and emp_yoy for 1998 depend on 1997 levels absent from the file and cannot be re-verified (all later yoy values match levels exactly)\",\n \"handling\": \"Accepted as given.\"},\n {\"issue\": \"COVID-19 shock 2020-03..2021-12 with yoy values between -16 and +19 percent (extreme leverage)\",\n \"handling\": \"Regression rows touching this window excluded from model estimation (pre-specified); naive unaffected.\"},\n {\"issue\": \"Current-vintage (revised) data, not historical first releases\",\n \"handling\": \"Only publication timing simulated; no real-time validity claimed.\"}],\n \"n_input_rows\": {\"canada_gdp_employment.csv\": int(n_raw)},\n \"n_analysed\": int(last_info[1][\"n\"]), \"random_seed\": None})\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\n\n# exclusion ledger for the last estimation sample (origin 2026-04, h=1)\nli = last_info[1]\nn_info = int((d.index <= M(origins[-1], -2)).sum())\nsteps = [(\"info_filter\", \"keep months <= t-2 (GDP published) at last origin 2026-04-01\", n_raw, n_info,\n \"publication-lag rule\"),\n (\"lag_availability\", \"drop rows lacking gdp_yoy(s-3) or emp_yoy(s-2) (start of sample)\", n_info, li[\"n_lagged\"],\n \"regressors not in file\"),\n (\"covid_window\", \"drop rows with target or regressor month in 2020-03..2021-12\", li[\"n_lagged\"], li[\"n\"],\n \"pandemic outliers; pre-specified\")]\nassert li[\"n_avail\"] == n_info\npd.DataFrame([dict(step=s, rule=r, n_before=a, n_after=b, n_removed=a - b, rationale=q) for s, r, a, b, q in steps]\n ).to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\nprint(json.dumps({k: v for k, v in res.items() if k.startswith((\"rmse\", \"mae\", \"rel\"))}, indent=1))\nprint(\"last-origin coefficients [const, gdp lag, emp lag]: h1\", np.round(last_info[1][\"beta\"], 4).tolist(),\n \"h3\", np.round(last_info[3][\"beta\"], 4).tolist())\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && head -5 out/forecasts.csv && wc -l out/forecasts.csv","description":"Write and run analysis script"}}]}}43{"type":"api-request","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","id":"eede5372-5530-49d0-9944-ccdb53aa0f79","timestamp":"2026-10-30T05:02:42.150Z","version":"2.1.314","querySource":"sdk","shapeHash":"20be7962999c92ef9e1c6de3002f528972aba0b18ace953850ebe20697d81819","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":"9c14a5fe-1ec4-4b7f-a544-b6bc3694b532","keep":12,"tail":["5c2ec2c3661624dcc8559ebc84098edf6777fc4d23139e51e48b73fcc269977d","155b5274e0534fb33c76c86c10330ab8ae15f6443afbe3a4c603c88091afdb10"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"2ae529ce8fff791ed544f4e601f7e9bfde6e4539f680e4fff0d211b657959504"}44{"parentUuid":"4556ccea-5c85-4430-bb84-5daa3517b272","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqRVv2K5qpLZUbEVGEz","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":4040,"cache_read_input_tokens":12618,"output_tokens":1158,"output_tokens_details":{"thinking_tokens":244},"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":4040},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2381,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqRVd9xkVzZhd6pJasx","requestRef":"eede5372-5530-49d0-9944-ccdb53aa0f79","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1c7487af-8749-4bc2-978e-efa9bd50da6c","timestamp":"2026-10-30T05:02:45.958Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}45{"parentUuid":"1c7487af-8749-4bc2-978e-efa9bd50da6c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqRVv2K5qpLZUbEVGEz","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SToWvtWjyJ84hS1BQnxX9X","name":"Bash","input":{"command":"cd out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace('''with open(os.path.join(OUT, \"results.json\"), \"w\") as f:''','''# Uncertainty: Diebold-Mariano (squared loss, HAC variance with h-1 Newey-West lags, Harvey et al. small-sample correction)\nfrom scipy import stats\nfor h in (1, 3):\n a = fc[(fc.model == \"naive\") & (fc.horizon == h)].sort_values(\"origin_date\")\n b = fc[(fc.model == MODEL) & (fc.horizon == h)].sort_values(\"origin_date\")\n dd = (a.forecast.values - a.actual.values) ** 2 - (b.forecast.values - b.actual.values) ** 2\n n = len(dd); dm = dd - dd.mean()\n lrv = np.mean(dm ** 2) + 2 * sum((1 - k / h) * np.mean(dm[k:] * dm[:-k]) for k in range(1, h))\n stat = dd.mean() / np.sqrt(lrv / n) * np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n res[f\"dm_stat_h{h}\"] = float(stat)\n res[f\"dm_pvalue_two_sided_h{h}\"] = float(2 * stats.t.sf(abs(stat), df=n - 1))\n# Ablation (diagnostic only): same direct regression without the employment regressor\nfor h in (1, 3):\n e = []\n for t in origins:\n df, gdp, emp, *_ = build_rows(t, h)\n X = np.column_stack([np.ones(len(df)), df.ylag.values])\n beta, *_ = np.linalg.lstsq(X, df.y.values, rcond=None)\n e.append(beta[0] + beta[1] * gdp.loc[M(t, -2)] - d.loc[M(t, h), \"gdp_yoy\"])\n res[f\"rmse_ablation_no_emp_h{h}\"] = float(np.sqrt(np.mean(np.square(e))))\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:''')\nopen('analysis.py','w').write(s)\nEOF\ncd .. && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cat out/results.json | grep -E \"dm_|ablation\"","description":"Add DM test and ablation, rerun"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4040,"cache_read_input_tokens":12618,"output_tokens":1158,"output_tokens_details":{"thinking_tokens":244},"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":4040},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqRVd9xkVzZhd6pJasx","requestRef":"eede5372-5530-49d0-9944-ccdb53aa0f79","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"78b94451-1fe3-4f67-ac4a-85c64ea121ef","timestamp":"2026-10-30T05:02:52.213Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}46{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"78b94451-1fe3-4f67-ac4a-85c64ea121ef","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca"}47{"type":"atis-latch","atis":"","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca"}48{"parentUuid":"78b94451-1fe3-4f67-ac4a-85c64ea121ef","isSidechain":false,"promptId":"2d8c293d-d814-4110-85d3-c0a929c36671","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SToWvtWjyJ84hS1BQnxX9X","type":"tool_result","content":" \"dm_stat_h1\": 2.0256610645246287,\n \"dm_pvalue_two_sided_h1\": 0.048498197529364746,\n \"dm_stat_h3\": 1.6396991600561308,\n \"dm_pvalue_two_sided_h3\": 0.10774494757001676,\n \"rmse_ablation_no_emp_h1\": 0.7153894841379895,\n \"rmse_ablation_no_emp_h3\": 0.8640294076084106","is_error":false}]},"uuid":"fa05c7f5-a94d-4731-88c8-f74650327b98","timestamp":"2026-10-30T05:02:53.103Z","toolUseResult":{"stdout":" \"dm_stat_h1\": 2.0256610645246287,\n \"dm_pvalue_two_sided_h1\": 0.048498197529364746,\n \"dm_stat_h3\": 1.6396991600561308,\n \"dm_pvalue_two_sided_h3\": 0.10774494757001676,\n \"rmse_ablation_no_emp_h1\": 0.7153894841379895,\n \"rmse_ablation_no_emp_h3\": 0.8640294076084106","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"78b94451-1fe3-4f67-ac4a-85c64ea121ef","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}49{"type":"api-request-blob","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","hash":"72dd467874522f9a5bc797b997bb352ed6980fe26a79ee59a38705895a6a977c","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01SToWvtWjyJ84hS1BQnxX9X","name":"Bash","input":{"command":"cd out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace('''with open(os.path.join(OUT, \"results.json\"), \"w\") as f:''','''# Uncertainty: Diebold-Mariano (squared loss, HAC variance with h-1 Newey-West lags, Harvey et al. small-sample correction)\nfrom scipy import stats\nfor h in (1, 3):\n a = fc[(fc.model == \"naive\") & (fc.horizon == h)].sort_values(\"origin_date\")\n b = fc[(fc.model == MODEL) & (fc.horizon == h)].sort_values(\"origin_date\")\n dd = (a.forecast.values - a.actual.values) ** 2 - (b.forecast.values - b.actual.values) ** 2\n n = len(dd); dm = dd - dd.mean()\n lrv = np.mean(dm ** 2) + 2 * sum((1 - k / h) * np.mean(dm[k:] * dm[:-k]) for k in range(1, h))\n stat = dd.mean() / np.sqrt(lrv / n) * np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n res[f\"dm_stat_h{h}\"] = float(stat)\n res[f\"dm_pvalue_two_sided_h{h}\"] = float(2 * stats.t.sf(abs(stat), df=n - 1))\n# Ablation (diagnostic only): same direct regression without the employment regressor\nfor h in (1, 3):\n e = []\n for t in origins:\n df, gdp, emp, *_ = build_rows(t, h)\n X = np.column_stack([np.ones(len(df)), df.ylag.values])\n beta, *_ = np.linalg.lstsq(X, df.y.values, rcond=None)\n e.append(beta[0] + beta[1] * gdp.loc[M(t, -2)] - d.loc[M(t, h), \"gdp_yoy\"])\n res[f\"rmse_ablation_no_emp_h{h}\"] = float(np.sqrt(np.mean(np.square(e))))\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:''')\nopen('analysis.py','w').write(s)\nEOF\ncd .. && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cat out/results.json | grep -E \"dm_|ablation\"","description":"Add DM test and ablation, rerun"}}]}}50{"type":"api-request","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","id":"58cab12c-f0c2-4c77-9fa6-30784ea95eb0","timestamp":"2026-10-30T05:02:53.106Z","version":"2.1.314","querySource":"sdk","shapeHash":"20be7962999c92ef9e1c6de3002f528972aba0b18ace953850ebe20697d81819","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":"eede5372-5530-49d0-9944-ccdb53aa0f79","keep":14,"tail":["72dd467874522f9a5bc797b997bb352ed6980fe26a79ee59a38705895a6a977c","63153a00014bdb49c07d99cb7ffacf0e91543e6d44499d967806b33fbefbaec0"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"6d8aadba350cdd6cd518f2b8c0728d6ac94d60fa0515916b7cbc32a922437675"}51{"parentUuid":"fa05c7f5-a94d-4731-88c8-f74650327b98","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqSJjbGy4SsRb1pNjoP","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":1321,"cache_read_input_tokens":16658,"output_tokens":1015,"output_tokens_details":{"thinking_tokens":56},"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":1321},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":404,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqSJUiPbK4GoFowGn3E","requestRef":"58cab12c-f0c2-4c77-9fa6-30784ea95eb0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c74f2647-6138-4049-9f94-05d47b0190e3","timestamp":"2026-10-30T05:02:55.418Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}52{"parentUuid":"c74f2647-6138-4049-9f94-05d47b0190e3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqSJjbGy4SsRb1pNjoP","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012g9iTpY9qvafnLBXbeN2LB","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; rm out/results.json out/forecasts.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp out/results.json /tmp/r1.json && cmp out/forecasts.csv /tmp/f1.csv && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, json\nf=pd.read_csv('out/forecasts.csv',parse_dates=['origin_date','target_date','last_target_date_used','last_predictor_date_used'])\nr=json.load(open('out/results.json'))\nprint('rows',len(f), f.groupby(['model','horizon']).size().to_dict())\noff=lambda k: f.origin_date+pd.DateOffset(months=k)\nprint('target chronology ok', (f.last_target_date_used<=off(-2)).all())\nm=f.model!='naive'\nprint('pred chronology ok', (f.last_predictor_date_used[m]<=off(-1)[m]).all(), f.last_predictor_date_used[~m].isna().all())\nprint('target date = origin+h', (f.target_date==f.origin_date+pd.to_timedelta(0,'D')).sum()==0 and all(f.target_date[i]==f.origin_date[i]+pd.DateOffset(months=int(f.horizon[i])) for i in range(len(f))))\nd=pd.read_csv('data/canada_gdp_employment.csv',parse_dates=['month']).set_index('month')\nprint('actual matches', (abs(f.actual.values-d.loc[f.target_date,'gdp_yoy'].values)<1e-6).all())\nprint('naive = y(t-2)', (abs(f[~m].forecast.values-d.loc[off(-2)[~m],'gdp_yoy'].values)<1e-6).all())\ne=pd.read_csv('out/exclusions.csv'); print('excl sums', 345-e.n_removed.sum()==r['n_analysed'], r['n_analysed'])\nprint('rmse>=mae', all(r[f'rmse_{a}_h{h}']>=r[f'mae_{a}_h{h}']>0 for a in ['naive','model'] for h in [1,3]))\nprint('rel', [abs(r[f'rel_improvement_rmse_h{h}']-(1-r[f'rmse_model_h{h}']/r[f'rmse_naive_h{h}']))<1e-12 for h in [1,3]])\nEOF","description":"Reproducibility and consistency checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1321,"cache_read_input_tokens":16658,"output_tokens":1015,"output_tokens_details":{"thinking_tokens":56},"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":1321},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqSJUiPbK4GoFowGn3E","requestRef":"58cab12c-f0c2-4c77-9fa6-30784ea95eb0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6d619651-7370-481e-bcfe-d06aa183cf8c","timestamp":"2026-10-30T05:03:02.088Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}53{"parentUuid":"6d619651-7370-481e-bcfe-d06aa183cf8c","isSidechain":false,"promptId":"2d8c293d-d814-4110-85d3-c0a929c36671","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012g9iTpY9qvafnLBXbeN2LB","type":"tool_result","content":"IDENTICAL\nrows 192 {('dir_ols_emp', 1): 48, ('dir_ols_emp', 3): 48, ('naive', 1): 48, ('naive', 3): 48}\ntarget chronology ok True\npred chronology ok True True\ntarget date = origin+h True\nactual matches True\nnaive = y(t-2) True\nexcl sums True 310\nrmse>=mae True\nrel [True, True]","is_error":false}]},"uuid":"4815f715-0386-40e1-9d57-cee1325e5dbb","timestamp":"2026-10-30T05:03:03.256Z","toolUseResult":{"stdout":"IDENTICAL\nrows 192 {('dir_ols_emp', 1): 48, ('dir_ols_emp', 3): 48, ('naive', 1): 48, ('naive', 3): 48}\ntarget chronology ok True\npred chronology ok True True\ntarget date = origin+h True\nactual matches True\nnaive = y(t-2) True\nexcl sums True 310\nrmse>=mae True\nrel [True, True]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"6d619651-7370-481e-bcfe-d06aa183cf8c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}54{"type":"api-request-blob","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","hash":"6258aa0c237ae6c7e725a7ee26a445acdee5362d23c0756839f5ef33815d30ce","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_012g9iTpY9qvafnLBXbeN2LB","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; rm out/results.json out/forecasts.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp out/results.json /tmp/r1.json && cmp out/forecasts.csv /tmp/f1.csv && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, json\nf=pd.read_csv('out/forecasts.csv',parse_dates=['origin_date','target_date','last_target_date_used','last_predictor_date_used'])\nr=json.load(open('out/results.json'))\nprint('rows',len(f), f.groupby(['model','horizon']).size().to_dict())\noff=lambda k: f.origin_date+pd.DateOffset(months=k)\nprint('target chronology ok', (f.last_target_date_used<=off(-2)).all())\nm=f.model!='naive'\nprint('pred chronology ok', (f.last_predictor_date_used[m]<=off(-1)[m]).all(), f.last_predictor_date_used[~m].isna().all())\nprint('target date = origin+h', (f.target_date==f.origin_date+pd.to_timedelta(0,'D')).sum()==0 and all(f.target_date[i]==f.origin_date[i]+pd.DateOffset(months=int(f.horizon[i])) for i in range(len(f))))\nd=pd.read_csv('data/canada_gdp_employment.csv',parse_dates=['month']).set_index('month')\nprint('actual matches', (abs(f.actual.values-d.loc[f.target_date,'gdp_yoy'].values)<1e-6).all())\nprint('naive = y(t-2)', (abs(f[~m].forecast.values-d.loc[off(-2)[~m],'gdp_yoy'].values)<1e-6).all())\ne=pd.read_csv('out/exclusions.csv'); print('excl sums', 345-e.n_removed.sum()==r['n_analysed'], r['n_analysed'])\nprint('rmse>=mae', all(r[f'rmse_{a}_h{h}']>=r[f'mae_{a}_h{h}']>0 for a in ['naive','model'] for h in [1,3]))\nprint('rel', [abs(r[f'rel_improvement_rmse_h{h}']-(1-r[f'rmse_model_h{h}']/r[f'rmse_naive_h{h}']))<1e-12 for h in [1,3]])\nEOF","description":"Reproducibility and consistency checks"}}]}}55{"type":"api-request","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","id":"27d8fada-4e54-4979-bb55-0869f925ea06","timestamp":"2026-10-30T05:03:03.259Z","version":"2.1.314","querySource":"sdk","shapeHash":"20be7962999c92ef9e1c6de3002f528972aba0b18ace953850ebe20697d81819","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":"58cab12c-f0c2-4c77-9fa6-30784ea95eb0","keep":16,"tail":["6258aa0c237ae6c7e725a7ee26a445acdee5362d23c0756839f5ef33815d30ce","03d8e5e243a49673813e229bcfd871765415473933c95234c3ceb0502a27219a"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a6924c52e7e2a3fc3aa1e7ac0e3ed6b61f15bed099d41cf48f412cb1d0f5cebf"}56{"parentUuid":"4815f715-0386-40e1-9d57-cee1325e5dbb","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqT4DzGpfy5JRNnd8Rg","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011Dmt8Md6PC6zJzPjP1Vbmg","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (deleted outputs, re-ran analysis.py): results.json and forecasts.csv byte-identical | PASS |\n| forecasts.csv has 192 rows, 48 per (model, horizon) | PASS |\n| last_target_date_used <= origin−2 months for all rows | PASS |\n| last_predictor_date_used <= origin−1 month for model rows; empty for naive | PASS |\n| target_date = origin + h months | PASS |\n| actual equals gdp_yoy of target month in data | PASS |\n| naive forecast equals gdp_yoy(t−2) | PASS |\n| exclusion ledger: 345 − 35 removed = 310 = n_analysed | PASS |\n| RMSE ≥ MAE > 0 for all four error pairs | PASS |\n| rel_improvement = 1 − rmse_model/rmse_naive | PASS |\n| In-script assertions (48 origins, info cut-offs, 48 errors per series) | PASS |\n| Magnitudes: model RMSE within ~10–20% of naive; naive h3 > naive h1 as expected | PASS |\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"sha256 matches README\",\"schema/missing/duplicates/yoy recomputation checked\",\"exclusions.csv written and sums\"],\"notes\":\"\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"naive = gdp_yoy(t-2) computed for 48 origins x 2 horizons; rmse/mae_naive keys written\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads data/ only, deterministic (no randomness), writes results/forecasts/exclusions\"],\"notes\":\"\"}\np['P4']={\"status\":\"running\",\"checks\":[\"clean rerun identical\",\"see out/state/checks.md: all PASS\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P2/P3 passed: naive and dir_ols_emp computed; added DM test and no-emp ablation (diagnostic). P4 checks pass.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Record checks and run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1158,"cache_read_input_tokens":17979,"output_tokens":919,"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":1158},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqT3x8SskLRteHAY1qU","requestRef":"27d8fada-4e54-4979-bb55-0869f925ea06","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c95f2a3c-4661-44f2-8ac6-eae42d2d8522","timestamp":"2026-10-30T05:03:11.794Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}57{"parentUuid":"c95f2a3c-4661-44f2-8ac6-eae42d2d8522","isSidechain":false,"promptId":"2d8c293d-d814-4110-85d3-c0a929c36671","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (10 of 43 checks failed)\n [ok] file:out/results.json: present\n [FAIL] file:out/report.md: missing or empty\n [ok] file:out/analysis.py: present\n [FAIL] file:out/claims.json: missing or empty\n [ok] file:out/forecasts.csv: present\n [ok] results_json: parsed\n [ok] key:task_id: ok\n [ok] key:model_name: ok\n [ok] key:model_description: ok\n [ok] key:n_origins: ok\n [ok] key:first_origin: ok\n [ok] key:last_origin: ok\n [ok] key:last_target_date_available: ok\n [ok] key:last_predictor_date_available: ok\n [ok] key:rmse_naive_h1: ok\n [ok] key:mae_naive_h1: ok\n [ok] key:rmse_naive_h3: ok\n [ok] key:mae_naive_h3: ok\n [ok] key:rmse_model_h1: ok\n [ok] key:mae_model_h1: ok\n [ok] key:rmse_model_h3: ok\n [ok] key:mae_model_h3: ok\n [ok] key:rel_improvement_rmse_h1: ok\n [ok] key:rel_improvement_rmse_h3: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:n_analysed: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n [ok] exclusion_ledger: 3 steps consistent\n [ok] chronology:first_origin<=last_origin: 2022-05-01 vs 2026-04-01\n [ok] chronology:last_target_date_available<=last_origin: 2026-02-01 vs 2026-04-01\n [ok] chronology:last_predictor_date_available<=last_origin: 2026-03-01 vs 2026-04-01\n [ok] reproducibility: regenerated in 0.9s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0213/out/state/gate_receipts/20261030T050312.748569Z.json","is_error":true,"tool_use_id":"toolu_011Dmt8Md6PC6zJzPjP1Vbmg"}]},"uuid":"20bf0cc5-05f0-46a0-8747-8cfd63a1d149","timestamp":"2026-10-30T05:03:12.755Z","toolUseResult":"Error: Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (10 of 43 checks failed)\n [ok] file:out/results.json: present\n [FAIL] file:out/report.md: missing or empty\n [ok] file:out/analysis.py: present\n [FAIL] file:out/claims.json: missing or empty\n [ok] file:out/forecasts.csv: present\n [ok] results_json: parsed\n [ok] key:task_id: ok\n [ok] key:model_name: ok\n [ok] key:model_description: ok\n [ok] key:n_origins: ok\n [ok] key:first_origin: ok\n [ok] key:last_origin: ok\n [ok] key:last_target_date_available: ok\n [ok] key:last_predictor_date_available: ok\n [ok] key:rmse_naive_h1: ok\n [ok] key:mae_naive_h1: ok\n [ok] key:rmse_naive_h3: ok\n [ok] key:mae_naive_h3: ok\n [ok] key:rmse_model_h1: ok\n [ok] key:mae_model_h1: ok\n [ok] key:rmse_model_h3: ok\n [ok] key:mae_model_h3: ok\n [ok] key:rel_improvement_rmse_h1: ok\n [ok] key:rel_improvement_rmse_h3: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:n_analysed: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n [ok] exclusion_ledger: 3 steps consistent\n [ok] chronology:first_origin<=last_origin: 2022-05-01 vs 2026-04-01\n [ok] chronology:last_target_date_available<=last_origin: 2026-02-01 vs 2026-04-01\n [ok] chronology:last_predictor_date_available<=last_origin: 2026-03-01 vs 2026-04-01\n [ok] reproducibility: regenerated in 0.9s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0213/out/state/gate_receipts/20261030T050312.748569Z.json","sandboxed":false,"sourceToolAssistantUUID":"c95f2a3c-4661-44f2-8ac6-eae42d2d8522","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}58{"type":"api-request-blob","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","hash":"69dc0e5d8b50239f539b4053d1fd368d3d60ee49097878d7ca1aad3340705529","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_011Dmt8Md6PC6zJzPjP1Vbmg","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (deleted outputs, re-ran analysis.py): results.json and forecasts.csv byte-identical | PASS |\n| forecasts.csv has 192 rows, 48 per (model, horizon) | PASS |\n| last_target_date_used <= origin−2 months for all rows | PASS |\n| last_predictor_date_used <= origin−1 month for model rows; empty for naive | PASS |\n| target_date = origin + h months | PASS |\n| actual equals gdp_yoy of target month in data | PASS |\n| naive forecast equals gdp_yoy(t−2) | PASS |\n| exclusion ledger: 345 − 35 removed = 310 = n_analysed | PASS |\n| RMSE ≥ MAE > 0 for all four error pairs | PASS |\n| rel_improvement = 1 − rmse_model/rmse_naive | PASS |\n| In-script assertions (48 origins, info cut-offs, 48 errors per series) | PASS |\n| Magnitudes: model RMSE within ~10–20% of naive; naive h3 > naive h1 as expected | PASS |\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"sha256 matches README\",\"schema/missing/duplicates/yoy recomputation checked\",\"exclusions.csv written and sums\"],\"notes\":\"\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"naive = gdp_yoy(t-2) computed for 48 origins x 2 horizons; rmse/mae_naive keys written\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads data/ only, deterministic (no randomness), writes results/forecasts/exclusions\"],\"notes\":\"\"}\np['P4']={\"status\":\"running\",\"checks\":[\"clean rerun identical\",\"see out/state/checks.md: all PASS\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P2/P3 passed: naive and dir_ols_emp computed; added DM test and no-emp ablation (diagnostic). P4 checks pass.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Record checks and run gate"}}]}}59{"type":"api-request","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","id":"d9008d4a-513d-4e13-8b0b-dfae7baaf2a8","timestamp":"2026-10-30T05:03:12.758Z","version":"2.1.314","querySource":"sdk","shapeHash":"20be7962999c92ef9e1c6de3002f528972aba0b18ace953850ebe20697d81819","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":"27d8fada-4e54-4979-bb55-0869f925ea06","keep":18,"tail":["69dc0e5d8b50239f539b4053d1fd368d3d60ee49097878d7ca1aad3340705529","b6c4a377e1fbefcd19c835b7a94bb2a20aa3dbb57e99c8cae679b8da32ff07b2"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"d1f579b60ec5bc0b56e67443eefa6118a135a8b6c51915e4bd8816353f4a6c11"}60{"parentUuid":"20bf0cc5-05f0-46a0-8747-8cfd63a1d149","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqTkqmEES2xga19viBt","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":1900,"cache_read_input_tokens":19137,"output_tokens":437,"output_tokens_details":{"thinking_tokens":47},"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":1900},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":340,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqTkZ9D3NyQFE7RxrWw","requestRef":"d9008d4a-513d-4e13-8b0b-dfae7baaf2a8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"09c0723b-b36e-4dbc-b270-eb93063833c6","timestamp":"2026-10-30T05:03:14.836Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}61{"parentUuid":"09c0723b-b36e-4dbc-b270-eb93063833c6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqTkqmEES2xga19viBt","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RNN6n53D6PUgrfv89DgMow","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']={\"status\":\"passed\",\"checks\":[\"clean rerun identical\",\"out/state/checks.md all PASS\",\"gate run 20261030T050312.748569Z: all contract keys, exclusion ledger, chronology, reproducibility ok; only P5 deliverables (report, claims) and non-terminal phases failed, as expected before P5\"],\"notes\":\"gate_receipt: out/state/gate_receipts/20261030T050312.748569Z.json\"}\np['P5'][\"status\"]=\"running\"\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 20261030T050312.748569Z; remaining failures are P5 deliverables).\" >> out/state/log.md\ncat out/results.json | head -30","description":"Update ledger after P4 gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1900,"cache_read_input_tokens":19137,"output_tokens":437,"output_tokens_details":{"thinking_tokens":47},"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":1900},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqTkZ9D3NyQFE7RxrWw","requestRef":"d9008d4a-513d-4e13-8b0b-dfae7baaf2a8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4adbc287-c3a0-416d-acf8-8a8bcffaceaf","timestamp":"2026-10-30T05:03:17.575Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}62{"parentUuid":"4adbc287-c3a0-416d-acf8-8a8bcffaceaf","isSidechain":false,"promptId":"2d8c293d-d814-4110-85d3-c0a929c36671","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RNN6n53D6PUgrfv89DgMow","type":"tool_result","content":"{\n \"task_id\": \"F3-conf-01\",\n \"model_name\": \"dir_ols_emp\",\n \"model_description\": \"Direct h-step OLS of gdp_yoy(s) on a constant, gdp_yoy(s-h-2) and emp_yoy(s-h-1), re-estimated at each origin on an expanding window from 1998 with targets <= t-2, excluding rows whose target or regressor month lies in 2020-03..2021-12; forecast plugs in gdp_yoy(t-2) and emp_yoy(t-1).\",\n \"n_origins\": 48,\n \"first_origin\": \"2022-05-01\",\n \"last_origin\": \"2026-04-01\",\n \"last_target_date_available\": \"2026-02-01\",\n \"last_predictor_date_available\": \"2026-03-01\",\n \"rmse_naive_h1\": 0.813620914876824,\n \"mae_naive_h1\": 0.6421395833333333,\n \"rmse_naive_h3\": 1.1399827160604965,\n \"mae_naive_h3\": 0.8715583333333333,\n \"rmse_model_h1\": 0.7429391714223801,\n \"mae_model_h1\": 0.6039917578066155,\n \"rmse_model_h3\": 0.9023000239646665,\n \"mae_model_h3\": 0.7534546734385598,\n \"rel_improvement_rmse_h1\": 0.08687306602134803,\n \"rel_improvement_rmse_h3\": 0.20849675065004825,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"gdp_level/gdp_yoy missing for 2026-08 and 2026-09 (not yet published)\",\n \"handling\": \"Not needed: latest target is 2026-07; rows never used as GDP information.\"\n },\n {\n \"issue\": \"gdp_yoy and emp_yoy for 1998 depend on 1997 levels absent from the file and cannot be re-verified (all later yoy values match levels exactly)\",\n \"handling\": \"Accepted as given.\"\n },","is_error":false}]},"uuid":"3a8696a6-1445-473a-8e55-fc871cff8ff3","timestamp":"2026-10-30T05:03:17.637Z","toolUseResult":{"stdout":"{\n \"task_id\": \"F3-conf-01\",\n \"model_name\": \"dir_ols_emp\",\n \"model_description\": \"Direct h-step OLS of gdp_yoy(s) on a constant, gdp_yoy(s-h-2) and emp_yoy(s-h-1), re-estimated at each origin on an expanding window from 1998 with targets <= t-2, excluding rows whose target or regressor month lies in 2020-03..2021-12; forecast plugs in gdp_yoy(t-2) and emp_yoy(t-1).\",\n \"n_origins\": 48,\n \"first_origin\": \"2022-05-01\",\n \"last_origin\": \"2026-04-01\",\n \"last_target_date_available\": \"2026-02-01\",\n \"last_predictor_date_available\": \"2026-03-01\",\n \"rmse_naive_h1\": 0.813620914876824,\n \"mae_naive_h1\": 0.6421395833333333,\n \"rmse_naive_h3\": 1.1399827160604965,\n \"mae_naive_h3\": 0.8715583333333333,\n \"rmse_model_h1\": 0.7429391714223801,\n \"mae_model_h1\": 0.6039917578066155,\n \"rmse_model_h3\": 0.9023000239646665,\n \"mae_model_h3\": 0.7534546734385598,\n \"rel_improvement_rmse_h1\": 0.08687306602134803,\n \"rel_improvement_rmse_h3\": 0.20849675065004825,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"gdp_level/gdp_yoy missing for 2026-08 and 2026-09 (not yet published)\",\n \"handling\": \"Not needed: latest target is 2026-07; rows never used as GDP information.\"\n },\n {\n \"issue\": \"gdp_yoy and emp_yoy for 1998 depend on 1997 levels absent from the file and cannot be re-verified (all later yoy values match levels exactly)\",\n \"handling\": \"Accepted as given.\"\n },","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4adbc287-c3a0-416d-acf8-8a8bcffaceaf","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}63{"type":"api-request-blob","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","hash":"59bf1f4697fa7d38ca4d69932a69bf9171192a83943c5236718ec1553a060134","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSgwcKEAgTGAI4AUIIdGhpbmtpbmcSDMSpxeIQLqwvE/3aahoMwU0zN3qTvxpRL7LgIjAZLUbw0kGKWF7mM8Vuwgu7LUSm/bRVci9j+bUI2XTb/Egwpy6A98r7gVlO89Ww/swqoAaW91i2z3KvN4vnlSADswOVYKwNxz6Q4nCAQupFW5tII8OSLqyXzF9/FT/aINQR9P3OohOPB/pV+HoJdE6v+QxfftVhq+ad2Fpm4XO2oxIBTYeyhfdVzBDWyff5/THHLueibi10RUAhXAZyzDMeYLCuz4K3kUIDererU2XCmud2A54hzsz7mboxbRbH0YtcznAi7mkuFQ5I2Y+rzmaWOSTQJO4QsQCEEaXeBKvVZz5JnW0FQoFTgc5zRP6wuwFrhNkRkAPmgJ5KaQJW4FeBTGwLWc9/EJm0U0WBYGkRjjbcWargQErckXalhq7rMX6t5fYEstZfqsahPJBbzjz4ri7YdMLDt7h2Hlxi7m9aTtpj5oFmTX+6pDlMai4NDbxZleC1O0ss1z8MuhN+/mFaohNTkyvnFIc9r04bXDSP9hl/f8NrarWNd8W4mc1po07VhpMbMBXsiwkZtAw/p0QE9IbgM3v7INQwdUhnWK9TbM1KPNcBdO3v3To++P4/iA3NmuDpC4n5DAwiY/DaFzVh4NMtYIV1J8H/P+PA4G0MzMSh/R4cwMQCSFPb2mQHeK61FSHxV2zxrD/wGUEF/ejV6DmU+MAdyc3IWwN/m9zIIv68OvZ79oMF3/fOaEIrrvCJx82mddWb5dr81ETOe9V7tjq06u6FTRzco+fVw8pEGX1pKINb25U56fIADxGe/h3wGs8Yy8e7mAQIe79oMSFBl6m20/PGyJkuLNPtJojvQbnzubQ4iUVXEU75Hf41dq6Uepl+OY2hZ84qUGdIEaSdak2URl69HxWk+wm8cyX23msVNn1mcFOVcRnPASKYgWcBqz3XqVQnjLrsKY02UDganbzHATbRDfYe/IcPpbqPb7UggbqhnbQwR66Ksv1h/A5T2+Q+DBFS/gb8IaiOizfoTdMDeRZlskuWiRFnSsaAIMfSuCZBaEpX1BqhrldH5boFpmnuC6Z6PGG4N3ApsXIC4DjK71IUDn4dAXo5nY55FMECmC+l9MJEYqnj7Vb8VtNDEt3vX47gC8RzQaBu8jdr8MsbIlQ//ZDL2azI1PmIrP9jThgB"},{"type":"tool_use","id":"toolu_01RNN6n53D6PUgrfv89DgMow","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']={\"status\":\"passed\",\"checks\":[\"clean rerun identical\",\"out/state/checks.md all PASS\",\"gate run 20261030T050312.748569Z: all contract keys, exclusion ledger, chronology, reproducibility ok; only P5 deliverables (report, claims) and non-terminal phases failed, as expected before P5\"],\"notes\":\"gate_receipt: out/state/gate_receipts/20261030T050312.748569Z.json\"}\np['P5'][\"status\"]=\"running\"\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 20261030T050312.748569Z; remaining failures are P5 deliverables).\" >> out/state/log.md\ncat out/results.json | head -30","description":"Update ledger after P4 gate"}}]}}64{"type":"api-request","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","id":"302d5b5b-eb9f-4d62-a7f8-0b3992f73b26","timestamp":"2026-10-30T05:03:17.640Z","version":"2.1.314","querySource":"sdk","shapeHash":"20be7962999c92ef9e1c6de3002f528972aba0b18ace953850ebe20697d81819","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":"d9008d4a-513d-4e13-8b0b-dfae7baaf2a8","keep":20,"tail":["59bf1f4697fa7d38ca4d69932a69bf9171192a83943c5236718ec1553a060134","2e623281876e9c7ace263f347473332bcd7d4388a775777d88741a4065be0480"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"2695a2d139962353445a4f24c3e1bd189b4183cfdfdb0a677921150db43ba631"}65{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"3a8696a6-1445-473a-8e55-fc871cff8ff3","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca"}66{"type":"atis-latch","atis":"","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca"}67{"parentUuid":"3a8696a6-1445-473a-8e55-fc871cff8ff3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqU7eeYYVTdLxVkvUXm","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014Y1CsT2LDorj576LLVkJAz","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-conf-01 — Forecasting Canadian monthly GDP y/y growth under publication lags\n\n## Question\nAt the end of each month t (48 origins, `first_origin` = 2022-05-01 to `last_origin` = 2026-04-01; `n_origins`), how accurately can year-over-year growth of Canadian monthly real GDP be forecast for months t+1 and t+3 using only data published by then, and does a model that also uses the timelier LFS employment growth beat the naive forecast (latest published GDP growth, month t−2)?\n\n## Data and exclusions\n- One file, `data/canada_gdp_employment.csv`, 345 data rows (`n_input_rows`), 1998-01 to 2026-09; SHA-256 matches `data/README.md`. Audit details are in `out/state/data_audit.md`.\n- Publication-lag rule: at origin t, GDP is used only up to t−2 and employment only up to t−1. At the last origin, that means GDP up to 2026-02-01 (`last_target_date_available`) and employment up to 2026-03-01 (`last_predictor_date_available`). Every row of `out/forecasts.csv` records the latest dates it actually used, and all of them satisfy the rule (`out/state/checks.md`).\n- No data row was deleted. When estimating the model, I dropped regression rows whose target or regressor month falls in the COVID window 2020-03..2021-12, where y/y values swing between −16 % and +19 %. I made this choice before computing any out-of-sample error. Exclusion ledger for the last estimation sample (origin 2026-04, h=1), in `out/exclusions.csv`: 345 rows → 338 known at the origin → 335 with lagged regressors available → 310 after the COVID exclusion (`n_analysed`).\n- Data issues and how I handled them are listed in `data_issues`. The main ones: GDP for 2026-08/09 is not yet published (not needed); the 1998 y/y values cannot be checked against levels (all later values match the levels exactly); the data are the current vintage.\n\n## Method\n- **Naive:** forecast = `gdp_yoy`(t−2) for both horizons.\n- **Model `dir_ols_emp`** (`model_name`, `model_description`): a direct h-step OLS of gdp_yoy(s) on a constant, gdp_yoy(s−h−2) and emp_yoy(s−h−1). This mirrors the information gaps at the origin (GDP lags the origin by 2 months, employment by 1). It is re-estimated at every origin on an expanding window starting in 1998, using only training targets ≤ t−2. The forecast plugs in gdp_yoy(t−2) and emp_yoy(t−1).\n- **Evaluation:** RMSE and MAE in percentage points over the 48 origins per horizon, against `gdp_yoy` of month t+h as given in the file.\n- **Diagnostics beyond the required outputs:**\n - A Diebold–Mariano test on squared-error loss, with an HAC variance (h−1 lags) and the Harvey–Leybourne–Newbold correction (`dm_stat_h*`, `dm_pvalue_two_sided_h*`).\n - An ablation: the same regression without employment (`rmse_ablation_no_emp_h*`).\n- Nothing is random (`random_seed` = null). `out/analysis.py` regenerates every output.\n\n## Results\n| horizon | RMSE naive | MAE naive | RMSE model | MAE model | 1 − RMSE ratio |\n|---|---|---|---|---|---|\n| h=1 | 0.814 (`rmse_naive_h1`) | 0.642 (`mae_naive_h1`) | 0.743 (`rmse_model_h1`) | 0.604 (`mae_model_h1`) | 0.087 (`rel_improvement_rmse_h1`) |\n| h=3 | 1.140 (`rmse_naive_h3`) | 0.872 (`mae_naive_h3`) | 0.902 (`rmse_model_h3`) | 0.753 (`mae_model_h3`) | 0.208 (`rel_improvement_rmse_h3`) |\n\n- On both RMSE and MAE, the model beats the naive forecast at both horizons. The RMSE reduction is about 9 % at h=1 and 21 % at h=3.\n- **Employment is not what drives the gain.** The same regression without employment does slightly better: RMSE 0.715 at h=1 (`rmse_ablation_no_emp_h1`) and 0.864 at h=3 (`rmse_ablation_no_emp_h3`). The improvement over naive comes from the autoregressive mean reversion (constant plus a lag coefficient below 1), not from LFS employment growth.\n\n## Uncertainty\n- The Diebold–Mariano test of equal squared-error accuracy (model vs naive) gives:\n - h=1: statistic 2.03, p = 0.048 (`dm_stat_h1`, `dm_pvalue_two_sided_h1`)\n - h=3: statistic 1.64, p = 0.108 (`dm_stat_h3`, `dm_pvalue_two_sided_h3`)\n- The evidence that the model beats naive is therefore borderline at h=1 and not significant at the 5 % level at h=3.\n- With only 48 overlapping, serially correlated errors over a single disinflation and slowdown episode (2022–2026), the RMSE estimates are imprecise. I computed no confidence intervals for the RMSEs themselves.\n- I did not test whether the employment-augmented model differs significantly from the no-employment ablation.\n\n## Limitations and open issues\n- **Timing only, not real-time:** the data are the current vintage (revised). The exercise simulates publication timing but not the historical first-release values, so no real-time validity is claimed. Errors against first releases would probably be larger.\n- **COVID exclusion:** dropping 2020-03..2021-12 from estimation is a modelling choice. Other ways of handling that period (dummies, robust estimation) were not tried.\n- **Short evaluation window:** the 48 origins cover one macro episode, and the ranking of methods may not generalise.\n- **One specification:** only one model specification was evaluated, beyond the diagnostic ablation, and no model selection was done.\n- All parts of the task were completed (`feasibility` = \"complete\").\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The direct OLS model with employment has lower out-of-sample RMSE and MAE than the naive forecast at h=1 (RMSE 0.743 vs 0.814, ~9% reduction).\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"mae_model_h1\",\"mae_naive_h1\",\"rel_improvement_rmse_h1\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data; 48 origins only.\"},\n {\"id\":\"C2\",\"text\":\"At h=3 the model reduces RMSE by ~21% relative to naive (0.902 vs 1.140) and also has lower MAE.\",\"results_keys\":[\"rmse_model_h3\",\"rmse_naive_h3\",\"mae_model_h3\",\"mae_naive_h3\",\"rel_improvement_rmse_h3\"],\"status\":\"supported\",\"caveat\":\"Not significant at 5% by Diebold-Mariano.\"},\n {\"id\":\"C3\",\"text\":\"The model's advantage over naive is borderline significant at h=1 (DM p=0.048) and not significant at h=3 (p=0.108).\",\"results_keys\":[\"dm_stat_h1\",\"dm_pvalue_two_sided_h1\",\"dm_stat_h3\",\"dm_pvalue_two_sided_h3\"],\"status\":\"supported\",\"caveat\":\"Small-sample DM test with HAC variance; 48 overlapping errors.\"},\n {\"id\":\"C4\",\"text\":\"LFS employment growth does not drive the gain: the same regression without employment has slightly lower RMSE at both horizons.\",\"results_keys\":[\"rmse_ablation_no_emp_h1\",\"rmse_ablation_no_emp_h3\",\"rmse_model_h1\",\"rmse_model_h3\"],\"status\":\"supported\",\"caveat\":\"Difference not tested for significance; single specification.\"},\n {\"id\":\"C5\",\"text\":\"At the last origin (2026-04-01) the latest usable GDP month is 2026-02 and the latest usable employment month is 2026-03; all forecasts respect these lags.\",\"results_keys\":[\"last_target_date_available\",\"last_predictor_date_available\",\"last_origin\"],\"status\":\"supported\",\"caveat\":\"Timing simulation only, not real-time vintages.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 required sections, all numbers cite results keys\",\"claims.json written; all keys exist in results.json\",\"final gate run (see notes)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -vE \"^\\s+\\[ok\\]\"","description":"Write report, claims, run final gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1136,"cache_read_input_tokens":21037,"output_tokens":3190,"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":1136},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqU7P2t9KCPyrv8Ktyq","requestRef":"302d5b5b-eb9f-4d62-a7f8-0b3992f73b26","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"76591508-34b6-4e3f-899e-e1d5fd7d570e","timestamp":"2026-10-30T05:03:44.883Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}68{"parentUuid":"76591508-34b6-4e3f-899e-e1d5fd7d570e","isSidechain":false,"promptId":"2d8c293d-d814-4110-85d3-c0a929c36671","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014Y1CsT2LDorj576LLVkJAz","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 43 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0213/out/state/gate_receipts/20261030T050345.768023Z.json","is_error":false}]},"uuid":"3a3c0d70-8d5e-4162-9aa0-0f2ba9abde8e","timestamp":"2026-10-30T05:03:45.776Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 43 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0213/out/state/gate_receipts/20261030T050345.768023Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"76591508-34b6-4e3f-899e-e1d5fd7d570e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}69{"type":"api-request-blob","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","hash":"9591e4c59f0030648dec1a63a82c74fa37dcd971d01f760b8febe15df74f149e","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_014Y1CsT2LDorj576LLVkJAz","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-conf-01 — Forecasting Canadian monthly GDP y/y growth under publication lags\n\n## Question\nAt the end of each month t (48 origins, `first_origin` = 2022-05-01 to `last_origin` = 2026-04-01; `n_origins`), how accurately can year-over-year growth of Canadian monthly real GDP be forecast for months t+1 and t+3 using only data published by then, and does a model that also uses the timelier LFS employment growth beat the naive forecast (latest published GDP growth, month t−2)?\n\n## Data and exclusions\n- One file, `data/canada_gdp_employment.csv`, 345 data rows (`n_input_rows`), 1998-01 to 2026-09; SHA-256 matches `data/README.md`. Audit details are in `out/state/data_audit.md`.\n- Publication-lag rule: at origin t, GDP is used only up to t−2 and employment only up to t−1. At the last origin, that means GDP up to 2026-02-01 (`last_target_date_available`) and employment up to 2026-03-01 (`last_predictor_date_available`). Every row of `out/forecasts.csv` records the latest dates it actually used, and all of them satisfy the rule (`out/state/checks.md`).\n- No data row was deleted. When estimating the model, I dropped regression rows whose target or regressor month falls in the COVID window 2020-03..2021-12, where y/y values swing between −16 % and +19 %. I made this choice before computing any out-of-sample error. Exclusion ledger for the last estimation sample (origin 2026-04, h=1), in `out/exclusions.csv`: 345 rows → 338 known at the origin → 335 with lagged regressors available → 310 after the COVID exclusion (`n_analysed`).\n- Data issues and how I handled them are listed in `data_issues`. The main ones: GDP for 2026-08/09 is not yet published (not needed); the 1998 y/y values cannot be checked against levels (all later values match the levels exactly); the data are the current vintage.\n\n## Method\n- **Naive:** forecast = `gdp_yoy`(t−2) for both horizons.\n- **Model `dir_ols_emp`** (`model_name`, `model_description`): a direct h-step OLS of gdp_yoy(s) on a constant, gdp_yoy(s−h−2) and emp_yoy(s−h−1). This mirrors the information gaps at the origin (GDP lags the origin by 2 months, employment by 1). It is re-estimated at every origin on an expanding window starting in 1998, using only training targets ≤ t−2. The forecast plugs in gdp_yoy(t−2) and emp_yoy(t−1).\n- **Evaluation:** RMSE and MAE in percentage points over the 48 origins per horizon, against `gdp_yoy` of month t+h as given in the file.\n- **Diagnostics beyond the required outputs:**\n - A Diebold–Mariano test on squared-error loss, with an HAC variance (h−1 lags) and the Harvey–Leybourne–Newbold correction (`dm_stat_h*`, `dm_pvalue_two_sided_h*`).\n - An ablation: the same regression without employment (`rmse_ablation_no_emp_h*`).\n- Nothing is random (`random_seed` = null). `out/analysis.py` regenerates every output.\n\n## Results\n| horizon | RMSE naive | MAE naive | RMSE model | MAE model | 1 − RMSE ratio |\n|---|---|---|---|---|---|\n| h=1 | 0.814 (`rmse_naive_h1`) | 0.642 (`mae_naive_h1`) | 0.743 (`rmse_model_h1`) | 0.604 (`mae_model_h1`) | 0.087 (`rel_improvement_rmse_h1`) |\n| h=3 | 1.140 (`rmse_naive_h3`) | 0.872 (`mae_naive_h3`) | 0.902 (`rmse_model_h3`) | 0.753 (`mae_model_h3`) | 0.208 (`rel_improvement_rmse_h3`) |\n\n- On both RMSE and MAE, the model beats the naive forecast at both horizons. The RMSE reduction is about 9 % at h=1 and 21 % at h=3.\n- **Employment is not what drives the gain.** The same regression without employment does slightly better: RMSE 0.715 at h=1 (`rmse_ablation_no_emp_h1`) and 0.864 at h=3 (`rmse_ablation_no_emp_h3`). The improvement over naive comes from the autoregressive mean reversion (constant plus a lag coefficient below 1), not from LFS employment growth.\n\n## Uncertainty\n- The Diebold–Mariano test of equal squared-error accuracy (model vs naive) gives:\n - h=1: statistic 2.03, p = 0.048 (`dm_stat_h1`, `dm_pvalue_two_sided_h1`)\n - h=3: statistic 1.64, p = 0.108 (`dm_stat_h3`, `dm_pvalue_two_sided_h3`)\n- The evidence that the model beats naive is therefore borderline at h=1 and not significant at the 5 % level at h=3.\n- With only 48 overlapping, serially correlated errors over a single disinflation and slowdown episode (2022–2026), the RMSE estimates are imprecise. I computed no confidence intervals for the RMSEs themselves.\n- I did not test whether the employment-augmented model differs significantly from the no-employment ablation.\n\n## Limitations and open issues\n- **Timing only, not real-time:** the data are the current vintage (revised). The exercise simulates publication timing but not the historical first-release values, so no real-time validity is claimed. Errors against first releases would probably be larger.\n- **COVID exclusion:** dropping 2020-03..2021-12 from estimation is a modelling choice. Other ways of handling that period (dummies, robust estimation) were not tried.\n- **Short evaluation window:** the 48 origins cover one macro episode, and the ranking of methods may not generalise.\n- **One specification:** only one model specification was evaluated, beyond the diagnostic ablation, and no model selection was done.\n- All parts of the task were completed (`feasibility` = \"complete\").\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The direct OLS model with employment has lower out-of-sample RMSE and MAE than the naive forecast at h=1 (RMSE 0.743 vs 0.814, ~9% reduction).\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"mae_model_h1\",\"mae_naive_h1\",\"rel_improvement_rmse_h1\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data; 48 origins only.\"},\n {\"id\":\"C2\",\"text\":\"At h=3 the model reduces RMSE by ~21% relative to naive (0.902 vs 1.140) and also has lower MAE.\",\"results_keys\":[\"rmse_model_h3\",\"rmse_naive_h3\",\"mae_model_h3\",\"mae_naive_h3\",\"rel_improvement_rmse_h3\"],\"status\":\"supported\",\"caveat\":\"Not significant at 5% by Diebold-Mariano.\"},\n {\"id\":\"C3\",\"text\":\"The model's advantage over naive is borderline significant at h=1 (DM p=0.048) and not significant at h=3 (p=0.108).\",\"results_keys\":[\"dm_stat_h1\",\"dm_pvalue_two_sided_h1\",\"dm_stat_h3\",\"dm_pvalue_two_sided_h3\"],\"status\":\"supported\",\"caveat\":\"Small-sample DM test with HAC variance; 48 overlapping errors.\"},\n {\"id\":\"C4\",\"text\":\"LFS employment growth does not drive the gain: the same regression without employment has slightly lower RMSE at both horizons.\",\"results_keys\":[\"rmse_ablation_no_emp_h1\",\"rmse_ablation_no_emp_h3\",\"rmse_model_h1\",\"rmse_model_h3\"],\"status\":\"supported\",\"caveat\":\"Difference not tested for significance; single specification.\"},\n {\"id\":\"C5\",\"text\":\"At the last origin (2026-04-01) the latest usable GDP month is 2026-02 and the latest usable employment month is 2026-03; all forecasts respect these lags.\",\"results_keys\":[\"last_target_date_available\",\"last_predictor_date_available\",\"last_origin\"],\"status\":\"supported\",\"caveat\":\"Timing simulation only, not real-time vintages.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 required sections, all numbers cite results keys\",\"claims.json written; all keys exist in results.json\",\"final gate run (see notes)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -vE \"^\\s+\\[ok\\]\"","description":"Write report, claims, run final gate"}}]}}70{"type":"api-request","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","id":"bbdeba7f-f10d-412d-b49e-aab8808d3f4f","timestamp":"2026-10-30T05:03:45.780Z","version":"2.1.314","querySource":"sdk","shapeHash":"20be7962999c92ef9e1c6de3002f528972aba0b18ace953850ebe20697d81819","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":"302d5b5b-eb9f-4d62-a7f8-0b3992f73b26","keep":22,"tail":["9591e4c59f0030648dec1a63a82c74fa37dcd971d01f760b8febe15df74f149e","2c3a699527da2ef5ae298af01490daf911553a9c067ee314c004fb15d4761015"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",23,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"fc7c4d5a0d2ef1d98eaa706408804eac55a0ab68b4ecd00d353689dfe65b250c"}71{"parentUuid":"3a3c0d70-8d5e-4162-9aa0-0f2ba9abde8e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqWBwfefSe3LJ2SXquw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015tY7RwSmyzBYH83fcV7U58","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P5']['notes']=\"final gate_receipt: out/state/gate_receipts/20261030T050345.768023Z.json (PASSED, 0/43 failed)\"\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P5 passed: report and claims written; final gate PASSED (20261030T050345.768023Z). Task complete.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1","description":"Record final receipt and recheck gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3285,"cache_read_input_tokens":22173,"output_tokens":306,"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":3285},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqWBfZ9UN2vvSwh5GcA","requestRef":"bbdeba7f-f10d-412d-b49e-aab8808d3f4f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8d7a218e-ba10-4cd5-ba23-0e6b00b19214","timestamp":"2026-10-30T05:03:49.503Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}72{"parentUuid":"8d7a218e-ba10-4cd5-ba23-0e6b00b19214","isSidechain":false,"promptId":"2d8c293d-d814-4110-85d3-c0a929c36671","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015tY7RwSmyzBYH83fcV7U58","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 43 checks failed)","is_error":false}]},"uuid":"3ad4a13d-8dc8-46db-9474-8a47276f956e","timestamp":"2026-10-30T05:03:50.471Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 43 checks failed)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"8d7a218e-ba10-4cd5-ba23-0e6b00b19214","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}73{"type":"api-request-blob","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","hash":"3ee8d18335e458caee2f4e15184f65bf4c783d340d28433d1be6757388036468","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_015tY7RwSmyzBYH83fcV7U58","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P5']['notes']=\"final gate_receipt: out/state/gate_receipts/20261030T050345.768023Z.json (PASSED, 0/43 failed)\"\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P5 passed: report and claims written; final gate PASSED (20261030T050345.768023Z). Task complete.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1","description":"Record final receipt and recheck gate"}}]}}74{"type":"api-request","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","id":"2cf8db98-c66a-4b36-b16f-b32ffece9dba","timestamp":"2026-10-30T05:03:50.475Z","version":"2.1.314","querySource":"sdk","shapeHash":"20be7962999c92ef9e1c6de3002f528972aba0b18ace953850ebe20697d81819","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":"bbdeba7f-f10d-412d-b49e-aab8808d3f4f","keep":24,"tail":["3ee8d18335e458caee2f4e15184f65bf4c783d340d28433d1be6757388036468","78f73e572bbc57cb793f59e210cfa3b534e144c33417b591410a5e48811b5f86"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",25,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"215e6a48095b8fe7a925d82b90ebb696576fc681f156681e88af24a976c54bfe"}75{"parentUuid":"3ad4a13d-8dc8-46db-9474-8a47276f956e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqWY3Qhwmkvdu3tpPGS","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":346,"cache_read_input_tokens":25458,"output_tokens":865,"output_tokens_details":{"thinking_tokens":18},"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":346},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6893,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqWXkHaCwX2ujm9QkWg","requestRef":"2cf8db98-c66a-4b36-b16f-b32ffece9dba","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ddf7b61a-3c17-45e3-9fd5-6d6eb348c235","timestamp":"2026-10-30T05:03:58.559Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}76{"parentUuid":"ddf7b61a-3c17-45e3-9fd5-6d6eb348c235","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqWY3Qhwmkvdu3tpPGS","type":"message","role":"assistant","content":[{"type":"text","text":"The task is done, and the answer to the main question is mixed. My model beats the naive forecast at both horizons, but employment growth isn't the reason: the same model without employment does slightly better. All protocol phases (P0–P5) are marked passed, and the final gate run passed with 0 of 43 checks failed.\n\n**Approach.** At each of the 48 month-ends (2022-05 to 2026-04), the model forecasts GDP growth for one and three months ahead. It regresses GDP growth on its own latest published value (month t−2) and the latest published employment growth (month t−1). It is re-fitted at every month on all earlier data that would have been published by then, starting in 1998. I dropped the COVID months (2020-03 to 2021-12) from the fitting data, and decided this before looking at any forecast errors. The last fitting sample has 310 observations.\n\n| horizon | naive error (RMSE / MAE, pp) | model error (RMSE / MAE, pp) | RMSE reduction | model without employment (RMSE) |\n|---|---|---|---|---|\n| 1 month | 0.814 / 0.642 | 0.743 / 0.604 | 8.7 % | 0.715 |\n| 3 months | 1.140 / 0.872 | 0.902 / 0.753 | 20.8 % | 0.864 |\n\n- **Where the gain comes from:** the forecast pulls GDP growth back toward its long-run average, which the naive forecast doesn't do. Employment adds nothing.\n- **How reliable the gain is:** a standard test of whether two forecasts are equally accurate (Diebold–Mariano) gives p = 0.048 at one month, which is borderline. At three months p = 0.108, so the improvement is not significant at the 5 % level.\n- **Information rules:** every forecast row records the latest GDP and employment months it used. A check confirms none uses GDP later than t−2 or employment later than t−1, and the script reproduces identical outputs on a clean re-run.\n\n**Limitations:**\n- The data are the current, revised figures, so this simulates only the publication timing, not the numbers people actually saw at the time.\n- 48 forecasts from one economic period (2022–2026) make the error estimates imprecise.\n- Dropping the COVID months is a judgement call; I didn't try other ways of handling that period.\n- I didn't test whether the gap between the model and the version without employment is statistically significant.\n\nFiles are in `out/`:\n- results.json\n- forecasts.csv\n- report.md\n- claims.json\n- analysis.py\n- exclusions.csv\n- state/"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":346,"cache_read_input_tokens":25458,"output_tokens":865,"output_tokens_details":{"thinking_tokens":18},"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":346},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqWXkHaCwX2ujm9QkWg","requestRef":"2cf8db98-c66a-4b36-b16f-b32ffece9dba","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"73bbabc9-5dd0-4db1-98a7-fd9b612ddbd7","timestamp":"2026-10-30T05:03:59.589Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0213","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","version":"2.1.314","gitBranch":"HEAD"}77{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"73bbabc9-5dd0-4db1-98a7-fd9b612ddbd7","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca"}78{"type":"cost-state","sessionId":"d316ad1f-a7f3-4b37-ae63-91e3d2f63bca","totalCostUSD":0.4459208,"totalAPIDuration":132923,"totalAPIDurationWithoutRetries":132917,"totalToolDuration":5775,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":138892,"startTime":1793336500705,"modelUsage":{"claude-opus-5-5":{"inputTokens":28,"outputTokens":14436,"thinkingTokens":1309,"cacheReadInputTokens":177944,"cacheCreationInputTokens":24300,"cacheCreationEphemeral5mInputTokens":24300,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.4459208}},"hasUnknownModelCost":false}79