spb/wp29_uqo

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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\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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Using twenty years of daily data (2000–2019) on the 10-year US Treasury constant-maturity yield, determine whether the day of the week has any predictive content for the one-day change in the yield. The estimands are: (i) the differences in the conditional mean of the one-day change Δy_t (basis points) across the five weekdays, tested jointly; (ii) the \"Monday contrast\", the mean change on Mondays minus the mean change on the other four weekdays, with a 95% interval; (iii) the out-of-sample predictive value of weekday-specific means relative to a zero-change (random-walk) baseline. This is a complete, bounded mini-project: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. Report what the data show, whatever that is.\n\n## Inputs\n- `data/DGS10.csv` — observed data, FRED series DGS10 (Board of Governors, H.15), daily, percent; raw FRED CSV restricted to 2000-01-01 … 2019-12-31 (provenance, URL, retrieval date, terms and SHA-256 in `data/README.md`). Columns: `observation_date` (YYYY-MM-DD, every weekday is listed) and `DGS10` (percent; the field is empty on days when no yield was published, i.e. US market holidays).\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- First drop the rows with an empty `DGS10` field; then define the one-day change as Δy_t = 100·(y_t − y_{t−1}) in basis points between consecutive *available* observations. The change is dated t and attributed to the weekday of t (Monday … Friday). The first available observation has no change.\n- The estimation period is the set of changes dated on or before 2009-12-31; the evaluation period is the set of changes dated on or after 2010-01-01. Any weekday-specific quantity used to forecast an evaluation-period change must be estimated from the estimation period only.\n- The joint test is the classical one-way (equal-variance) analysis-of-variance F-test of equal weekday means over all changes (both periods together). The Monday contrast and its interval may use any valid two-sample method (Welch, pooled, or a regression on a Monday indicator with heteroskedasticity-robust SE); state the choice.\n- Use only the file listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `n_missing_rows` | integer | rows | rows of `DGS10.csv` with an empty `DGS10` field |\n| `n_train`, `n_test` | integer | changes | number of daily changes in the estimation and evaluation periods |\n| `anova_f_stat` | number | — | one-way ANOVA F statistic for equal weekday means (all changes) |\n| `anova_p_value` | number | — | its p-value (F(4, n−5)) |\n| `effect_estimate` | number | basis points | Monday contrast: mean Monday change minus mean change on the other weekdays (all changes) |\n| `effect_ci_low`, `effect_ci_high` | number | basis points | 95% confidence interval for the Monday contrast |\n| `baseline_metric` | number | basis points | RMSE of the zero-change forecast over the evaluation period |\n| `model_metric` | number | basis points | RMSE over the evaluation period of the forecast that predicts each change by the estimation-period mean change of its weekday |\n| `conclusion` | string | — | one of the codes below (decided by the ANOVA at the 5% level; if it rejects, the sign is given by the Monday-contrast interval) |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = number of daily changes used in the ANOVA.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — mean change by weekday with 95% confidence bars (all changes), produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per weekday with columns `weekday`, `n`, `mean_bp`, `se_bp` (header + five data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\ntotal 32\ndrwxr-xr-x    6 simon-pierreboucher  staff   192 oct.  29 23:42 .\ndrwxr-xr-x  154 simon-pierreboucher  staff  4928 oct.  30 01:20 ..\n-rw-r--r--    1 simon-pierreboucher  staff   509 oct.  29 23:42 bundle.json\ndrwxr-xr-x    4 simon-pierreboucher  staff   128 oct.  29 23:27 data\n-rw-r--r--    1 simon-pierreboucher  staff  2209 oct.  29 23:35 gate_spec.json\n-rw-r--r--    1 simon-pierreboucher  staff  6307 oct.  29 23:35 TASK.md\ndata:\nDGS10.csv\nREADME.md","is_error":false}]},"uuid":"7c11310f-3251-4d3a-8ea7-8cb916aacb5f","timestamp":"2026-10-30T05:20:50.539Z","toolUseResult":{"stdout":"# F6-conf-01 — Does the day of the week carry predictive content for daily changes in the 10-year Treasury yield?\n\n## Scientific question\nFinancial folklore attributes systematic \"Monday\" or \"Friday\" patterns to daily asset-price changes. Using twenty years of daily data (2000–2019) on the 10-year US Treasury constant-maturity yield, determine whether the day of the week has any predictive content for the one-day change in the yield. The estimands are: (i) the differences in the conditional mean of the one-day change Δy_t (basis points) across the five weekdays, tested jointly; (ii) the \"Monday contrast\", the mean change on Mondays minus the mean change on the other four weekdays, with a 95% interval; (iii) the out-of-sample predictive value of weekday-specific means relative to a zero-change (random-walk) baseline. This is a complete, bounded mini-project: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report. Report what the data show, whatever that is.\n\n## Inputs\n- `data/DGS10.csv` — observed data, FRED series DGS10 (Board of Governors, H.15), daily, percent; raw FRED CSV restricted to 2000-01-01 … 2019-12-31 (provenance, URL, retrieval date, terms and SHA-256 in `data/README.md`). Columns: `observation_date` (YYYY-MM-DD, every weekday is listed) and `DGS10` (percent; the field is empty on days when no yield was published, i.e. US market holidays).\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- First drop the rows with an empty `DGS10` field; then define the one-day change as Δy_t = 100·(y_t − y_{t−1}) in basis points between consecutive *available* observations. The change is dated t and attributed to the weekday of t (Monday … Friday). The first available observation has no change.\n- The estimation period is the set of changes dated on or before 2009-12-31; the evaluation period is the set of changes dated on or after 2010-01-01. Any weekday-specific quantity used to forecast an evaluation-period change must be estimated from the estimation period only.\n- The joint test is the classical one-way (equal-variance) analysis-of-variance F-test of equal weekday means over all changes (both periods together). The Monday contrast and its interval may use any valid two-sample method (Welch, pooled, or a regression on a Monday indicator with heteroskedasticity-robust SE); state the choice.\n- Use only the file listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `n_missing_rows` | integer | rows | rows of `DGS10.csv` with an empty `DGS10` field |\n| `n_train`, `n_test` | integer | changes | number of daily changes in the estimation and evaluation periods |\n| `anova_f_stat` | number | — | one-way ANOVA F statistic for equal weekday means (all changes) |\n| `anova_p_value` | number | — | its p-value (F(4, n−5)) |\n| `effect_estimate` | number | basis points | Monday contrast: mean Monday change minus mean change on the other weekdays (all changes) |\n| `effect_ci_low`, `effect_ci_high` | number | basis points | 95% confidence interval for the Monday contrast |\n| `baseline_metric` | number | basis points | RMSE of the zero-change forecast over the evaluation period |\n| `model_metric` | number | basis points | RMSE over the evaluation period of the forecast that predicts each change by the estimation-period mean change of its weekday |\n| `conclusion` | string | — | one of the codes below (decided by the ANOVA at the 5% level; if it rejects, the sign is given by the Monday-contrast interval) |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = number of daily changes used in the ANOVA.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — mean change by weekday with 95% confidence bars (all changes), produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per weekday with columns `weekday`, `n`, `mean_bp`, `se_bp` (header + five data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. 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Source: Board of Governors of the Federal Reserve System (US), H.15 Selected Interest Rates, distributed by FRED, Federal Reserve Bank of St. Louis.\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=DGS10` (full history; raw file SHA-256 `f3b6b3956f888551c21cfbae007f157bb6433b4db8f557bd7a08b9eb8fd5a97b`), retrieved 2026-10-30T03:23:28Z.\n- The bundle file is the raw FRED CSV restricted to observation dates from 2000-01-01 to 2019-12-31 inclusive; the FRED format is kept unchanged: columns `observation_date` (YYYY-MM-DD) and `DGS10` (percent). FRED lists every weekday; on days when no yield is published (US market holidays) the `DGS10` field is empty.\n- Terms: FRED terms of use, https://fred.stlouisfed.org/legal/ ; the underlying H.15 data are produced by a US government agency and are in the public domain.\n- SHA-256 of `DGS10.csv`: `8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7`\n{\n  \"task_id\": \"F6-conf-01\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\",\n    \"out/figure1.png\",\n    \"out/table1.csv\"\n  ],\n  \"results_keys\": {\n    \"task_id\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility_reason\": {\n      \"type\": \"string\",\n      \"nullable\": true\n    },\n    \"data_issues\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"n_input_rows\": {\n      \"type\": \"object\",\n      \"nullable\": false\n    },\n    \"n_analysed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"random_seed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"n_missing_rows\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"n_train\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"n_test\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"anova_f_stat\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"anova_p_value\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_estimate\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_ci_low\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_ci_high\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"baseline_metric\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"model_metric\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"conclusion\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    }\n  },\n  \"report_sections\": [\n    \"Question\",\n    \"Data and exclusions\",\n    \"Method\",\n    \"Results\",\n    \"Uncertainty\",\n    \"Limitations and open issues\"\n  ],\n  \"family_checks\": [\n    \"ci_contains_estimate\",\n    \"positive_se\",\n    \"claims_reference_existing_keys\",\n    \"phase_ledger_consistent\",\n    \"exclusion_ledger_sums\"\n  ],\n  \"ci_pairs\": [\n    [\n      \"effect_estimate\",\n      \"effect_ci_low\",\n      \"effect_ci_high\"\n    ]\n  ],\n  \"positive_keys\": [\n    \"anova_f_stat\",\n    \"baseline_metric\",\n    \"model_metric\"\n  ],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-06\n}{\n  \"task_id\": \"F6-conf-01\",\n  \"family\": \"F6\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:42:41.724508+00:00\",\n  \"files\": {\n    \"TASK.md\": \"2283a94e1a3aa085d881cacc8bc6b793b6334b6193373788a992080c86a2d89e\",\n    \"data/DGS10.csv\": \"8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7\",\n    \"data/README.md\": \"32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a\",\n    \"gate_spec.json\": \"d53691940951460bcc45cb08c958917841ae1bfbc8d242d37ded795a3e998eb7\"\n  }\n}observation_date,DGS10\n2000-01-03,6.58\n2000-01-04,6.49\n2000-01-05,6.62\n2000-01-06,6.57\n    5218 data/DGS10.csv\n8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7  data/DGS10.csv","is_error":false}]},"uuid":"98f2a0a8-e092-42b1-82db-7007397de926","timestamp":"2026-10-30T05:20:52.499Z","toolUseResult":{"stdout":"# Data provenance — F6-conf-01\n\n## `DGS10.csv` (5217 rows)\nObserved data: *Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis* (FRED series `DGS10`, daily, percent, not seasonally adjusted). Source: Board of Governors of the Federal Reserve System (US), H.15 Selected Interest Rates, distributed by FRED, Federal Reserve Bank of St. Louis.\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=DGS10` (full history; raw file SHA-256 `f3b6b3956f888551c21cfbae007f157bb6433b4db8f557bd7a08b9eb8fd5a97b`), retrieved 2026-10-30T03:23:28Z.\n- The bundle file is the raw FRED CSV restricted to observation dates from 2000-01-01 to 2019-12-31 inclusive; the FRED format is kept unchanged: columns `observation_date` (YYYY-MM-DD) and `DGS10` (percent). FRED lists every weekday; on days when no yield is published (US market holidays) the `DGS10` field is empty.\n- Terms: FRED terms of use, https://fred.stlouisfed.org/legal/ ; the underlying H.15 data are produced by a US government agency and are in the public domain.\n- SHA-256 of `DGS10.csv`: `8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7`\n{\n  \"task_id\": \"F6-conf-01\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\",\n    \"out/figure1.png\",\n    \"out/table1.csv\"\n  ],\n  \"results_keys\": {\n    \"task_id\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility_reason\": {\n      \"type\": \"string\",\n      \"nullable\": true\n    },\n    \"data_issues\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"n_input_rows\": {\n      \"type\": \"object\",\n      \"nullable\": false\n    },\n    \"n_analysed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"random_seed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"n_missing_rows\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"n_train\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"n_test\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"anova_f_stat\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"anova_p_value\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_estimate\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_ci_low\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_ci_high\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"baseline_metric\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"model_metric\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"conclusion\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    }\n  },\n  \"report_sections\": [\n    \"Question\",\n    \"Data and exclusions\",\n    \"Method\",\n    \"Results\",\n    \"Uncertainty\",\n    \"Limitations and open issues\"\n  ],\n  \"family_checks\": [\n    \"ci_contains_estimate\",\n    \"positive_se\",\n    \"claims_reference_existing_keys\",\n    \"phase_ledger_consistent\",\n    \"exclusion_ledger_sums\"\n  ],\n  \"ci_pairs\": [\n    [\n      \"effect_estimate\",\n      \"effect_ci_low\",\n      \"effect_ci_high\"\n    ]\n  ],\n  \"positive_keys\": [\n    \"anova_f_stat\",\n    \"baseline_metric\",\n    \"model_metric\"\n  ],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-06\n}{\n  \"task_id\": \"F6-conf-01\",\n  \"family\": \"F6\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:42:41.724508+00:00\",\n  \"files\": {\n    \"TASK.md\": \"2283a94e1a3aa085d881cacc8bc6b793b6334b6193373788a992080c86a2d89e\",\n    \"data/DGS10.csv\": \"8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7\",\n    \"data/README.md\": \"32b825f02387ef3d552df6def82c585d7f248980667a28f30e58506e472e561a\",\n    \"gate_spec.json\": \"d53691940951460bcc45cb08c958917841ae1bfbc8d242d37ded795a3e998eb7\"\n  }\n}observation_date,DGS10\n2000-01-03,6.58\n2000-01-04,6.49\n2000-01-05,6.62\n2000-01-06,6.57\n    5218 data/DGS10.csv\n8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7  data/DGS10.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b9c55913-fdcf-4296-a8c1-2a40709f5b8e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0276","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","hash":"31bc5db51f9b495a112bc2bc21df8fff24005c5785f259c0ce4c69f867f902c2","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_015bpWQzpx5W9rTxgdBjnuzT","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json; cat bundle.json; head -5 data/DGS10.csv; wc -l data/DGS10.csv; shasum -a 256 data/DGS10.csv","description":"Read provenance, gate spec, data head"}}]}}22{"type":"api-request","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","id":"6cd66ca9-0549-4e55-b591-ec0da4ae79b1","timestamp":"2026-10-30T05:20:52.501Z","version":"2.1.314","querySource":"sdk","shapeHash":"b9a73ceab22551b11bb09c0124a131fa9aead2b833ffe6a1499883c6a2077da4","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":"82df0e39-781b-4326-b716-a2564bfc5d0f","keep":4,"tail":["31bc5db51f9b495a112bc2bc21df8fff24005c5785f259c0ce4c69f867f902c2","2a5a212ca55b02ce4543241dc3664cbfdfb33209b886ec961d5e25072a3cc1c7"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"8c9be79f8e53788a6eeb9446b42a6f5a72624d0876c3d70a5229072a3283ad7f"}23{"parentUuid":"98f2a0a8-e092-42b1-82db-7007397de926","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrosak7kuBARfBY3HZK","type":"message","role":"assistant","content":[{"type":"text","text":"P0: contract and ledger."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2044,"cache_read_input_tokens":6982,"output_tokens":1088,"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":2044},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrosPpq2X21YFvADkhw","requestRef":"6cd66ca9-0549-4e55-b591-ec0da4ae79b1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0e82c0ae-43c1-4b2d-9019-6a0393cd3b7d","timestamp":"2026-10-30T05:20:53.660Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0276","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"0e82c0ae-43c1-4b2d-9019-6a0393cd3b7d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrosak7kuBARfBY3HZK","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01X97hQpU6vegkaMZVTxHVkP","name":"Bash","input":{"command":"mkdir -p out/state && cat > out/state/contract.md <<'EOF'\n# Contract — F6-conf-01\n**Question.** Does weekday carry predictive content for the one-day change in the 10y Treasury CMT yield (DGS10), 2000–2019?\n**Estimands.** (i) joint equality of the five weekday conditional means of Δy_t (bp), classical one-way equal-variance ANOVA F-test on all changes; (ii) Monday contrast = mean(Mon) − mean(Tue–Fri), all changes, with 95% CI (I choose Welch two-sample); (iii) OOS RMSE over 2010–2019 of weekday-mean forecast (weekday means from changes dated ≤2009-12-31) vs zero-change forecast.\nAssumptions: changes approx. independent with finite variance; ANOVA additionally assumes equal variances (stated in task as the test to use); stationarity of weekday means across periods for (iii) to be meaningful.\n**Data rules.** Drop empty DGS10 rows first; Δy_t = 100(y_t − y_{t−1}) between consecutive available observations, dated t, weekday of t; first obs has no change. Train: changes dated ≤ 2009-12-31 (note: the first 2010 change uses the last 2009 level — allowed, since it is dated 2010). Test: ≥ 2010-01-01. Only DGS10.csv.\n**Outputs.** out/results.json keys: n_missing_rows, n_train, n_test, anova_f_stat, anova_p_value, effect_estimate, effect_ci_low, effect_ci_high, baseline_metric, model_metric, conclusion, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed. Also figure1.png, table1.csv (weekday,n,mean_bp,se_bp), report.md (6 sections), analysis.py, claims.json.\n**Conclusion rule.** ANOVA p≥0.05 → no_detectable_effect; else sign from Monday CI (positive/negative; if CI covers 0 despite ANOVA rejection → no_detectable_effect for the Monday contrast, documented).\n**Invalidity.** Missing/unparsable data making changes uncomputable; using test-period info in forecasts.\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\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"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":2044,"cache_read_input_tokens":6982,"output_tokens":1088,"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":2044},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrosPpq2X21YFvADkhw","requestRef":"6cd66ca9-0549-4e55-b591-ec0da4ae79b1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"964ef602-ec8b-4d2a-a7d6-72ff5579c594","timestamp":"2026-10-30T05:21:02.385Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0276","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"964ef602-ec8b-4d2a-a7d6-72ff5579c594","isSidechain":false,"promptId":"ed6567c1-cc92-485e-be39-4424a6b85424","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01X97hQpU6vegkaMZVTxHVkP","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"7fa80eb1-e21a-4b2f-92f0-e223edd43e3b","timestamp":"2026-10-30T05:21:02.420Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"964ef602-ec8b-4d2a-a7d6-72ff5579c594","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0276","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","version":"2.1.314","gitBranch":"HEAD"}26{"type":"api-request-blob","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","hash":"ca01604914494594d7009a9bcce764ee84032e9a020b5ebe7fa0bfcad5c517ca","message":{"role":"assistant","content":[{"type":"text","text":"P0: contract and ledger."},{"type":"tool_use","id":"toolu_01X97hQpU6vegkaMZVTxHVkP","name":"Bash","input":{"command":"mkdir -p out/state && cat > out/state/contract.md <<'EOF'\n# Contract — F6-conf-01\n**Question.** Does weekday carry predictive content for the one-day change in the 10y Treasury CMT yield (DGS10), 2000–2019?\n**Estimands.** (i) joint equality of the five weekday conditional means of Δy_t (bp), classical one-way equal-variance ANOVA F-test on all changes; (ii) Monday contrast = mean(Mon) − mean(Tue–Fri), all changes, with 95% CI (I choose Welch two-sample); (iii) OOS RMSE over 2010–2019 of weekday-mean forecast (weekday means from changes dated ≤2009-12-31) vs zero-change forecast.\nAssumptions: changes approx. independent with finite variance; ANOVA additionally assumes equal variances (stated in task as the test to use); stationarity of weekday means across periods for (iii) to be meaningful.\n**Data rules.** Drop empty DGS10 rows first; Δy_t = 100(y_t − y_{t−1}) between consecutive available observations, dated t, weekday of t; first obs has no change. Train: changes dated ≤ 2009-12-31 (note: the first 2010 change uses the last 2009 level — allowed, since it is dated 2010). Test: ≥ 2010-01-01. Only DGS10.csv.\n**Outputs.** out/results.json keys: n_missing_rows, n_train, n_test, anova_f_stat, anova_p_value, effect_estimate, effect_ci_low, effect_ci_high, baseline_metric, model_metric, conclusion, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed. Also figure1.png, table1.csv (weekday,n,mean_bp,se_bp), report.md (6 sections), analysis.py, claims.json.\n**Conclusion rule.** ANOVA p≥0.05 → no_detectable_effect; else sign from Monday CI (positive/negative; if CI covers 0 despite ANOVA rejection → no_detectable_effect for the Monday contrast, documented).\n**Invalidity.** Missing/unparsable data making changes uncomputable; using test-period info in forecasts.\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\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md","description":"Write contract, phase 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-c \"\nimport pandas as pd\nd=pd.read_csv('data/DGS10.csv',dtype=str,keep_default_na=False)\nprint(d.shape, d.columns.tolist())\nprint((d.DGS10=='').sum(), d.observation_date.duplicated().sum())\ndt=pd.to_datetime(d.observation_date); print(dt.min(),dt.max(), dt.dt.dayofweek.value_counts().sort_index().tolist(), dt.is_monotonic_increasing)\nv=pd.to_numeric(d.DGS10,errors='coerce'); print(v.isna().sum(), v.describe())\nprint(d[(v.isna())&(d.DGS10!='')])\nx=v.dropna(); dd=100*x.diff(); print(dd.describe()); print(d.loc[dd.abs().nlargest(5).index], dd.abs().nlargest(5))\ngaps=dt[v.notna()].diff().dt.days; print(gaps.value_counts())\n\"","description":"Audit data 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2) ['observation_date', 'DGS10']\n215 0\n2000-01-03 00:00:00 2019-12-31 00:00:00 [1044, 1044, 1043, 1043, 1043] True\n215 count    5002.000000\nmean        3.432167\nstd         1.239314\nmin         1.370000\n25%         2.340000\n50%         3.360000\n75%         4.410000\nmax         6.790000\nName: DGS10, dtype: float64\nEmpty DataFrame\nColumns: [observation_date, DGS10]\nIndex: []\ncount    5001.000000\nmean       -0.093181\nstd         5.723645\nmin       -51.000000\n25%        -3.000000\n50%         0.000000\n75%         3.000000\nmax        25.000000\nName: DGS10, dtype: float64\n     observation_date DGS10\n2402       2009-03-18  2.51\n2318       2008-11-20  3.10\n2270       2008-09-15  3.47\n488        2001-11-15  4.79\n2381       2009-02-17  2.64 2402    51.0\n2318    28.0\n2270    27.0\n488     25.0\n2381    25.0\nName: DGS10, dtype: float64\nobservation_date\n1.0    3899\n3.0     889\n4.0     155\n2.0      58\nName: count, dtype: 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> out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n## data/DGS10.csv\n- SHA-256 8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7 (matches data/README.md and bundle.json).\n- 5217 data rows + header; 2 columns: observation_date (YYYY-MM-DD), DGS10 (percent, 2 decimals → 1 bp resolution).\n- Dates 2000-01-03 … 2019-12-31, strictly increasing, no duplicate dates, weekdays only (Mon 1044, Tue 1044, Wed 1043, Thu 1043, Fri 1043).\n- Empty DGS10: 215 rows (holidays). No non-empty unparsable values. Non-missing levels: 5002, range 1.37–6.79 %.\n- Changes: 5001, mean −0.093 bp, sd 5.72 bp, min −51 bp (2009-03-18, FOMC QE1 announcement), max +25 bp. Extremes are genuine market events; kept.\n- Gaps between consecutive available obs (calendar days): 1 (3899), 3 (889, weekends), 4 (155, long weekends/holidays), 2 (58, mid-week holidays). Hence some changes span >1 trading day (post-holiday); by rule they are attributed to the weekday of t. Notably, after Monday holidays the Tuesday change spans Fri→Tue.\n- data/README.md: provenance text only, not analysed.\n## Information-availability\nNo input violates the rules. Only DGS10.csv used. Training weekday means use only changes dated ≤2009-12-31; the first 2010 change (dated 2010-01-04) uses the 2009-12-31 level, which is permitted because the change is dated in the evaluation period.\n## Exclusions\nOnly the rule-mandated drop of 215 empty rows plus the first available observation having no change (5217 → 5002 levels → 5001 changes).\nEOF\necho \"- 2026-10-30 P1: audit done; only rule-mandated exclusions.\" >> 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":855,"cache_read_input_tokens":10129,"output_tokens":977,"output_tokens_details":{"thinking_tokens":177},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":855},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrpuoy1gCVUpssUD9Jh","requestRef":"9daf3424-05e7-4276-87c9-7d11d35a486a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"872cc6b1-4234-4506-8e21-66493fea5799","timestamp":"2026-10-30T05:21:16.161Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0276","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","version":"2.1.314","gitBranch":"HEAD"}34{"parentUuid":"872cc6b1-4234-4506-8e21-66493fea5799","isSidechain":false,"promptId":"ed6567c1-cc92-485e-be39-4424a6b85424","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01G8ezUzYiuzJvaLovzFjZEL","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"4ea7e533-ee12-4ce7-9569-258e83c49c38","timestamp":"2026-10-30T05:21:16.200Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"872cc6b1-4234-4506-8e21-66493fea5799","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0276","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","version":"2.1.314","gitBranch":"HEAD"}35{"type":"api-request-blob","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","hash":"f89a9021ae6350ce6cec5185f0453a2146ac76129fe0ede7a6d4430d2917ba5c","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01G8ezUzYiuzJvaLovzFjZEL","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n## data/DGS10.csv\n- SHA-256 8187f89ab6523b3c964255e1736cfcf0e2ca854ed9e50343e04f4dc2945c5cd7 (matches data/README.md and bundle.json).\n- 5217 data rows + header; 2 columns: observation_date (YYYY-MM-DD), DGS10 (percent, 2 decimals → 1 bp resolution).\n- Dates 2000-01-03 … 2019-12-31, strictly increasing, no duplicate dates, weekdays only (Mon 1044, Tue 1044, Wed 1043, Thu 1043, Fri 1043).\n- Empty DGS10: 215 rows (holidays). No non-empty unparsable values. Non-missing levels: 5002, range 1.37–6.79 %.\n- Changes: 5001, mean −0.093 bp, sd 5.72 bp, min −51 bp (2009-03-18, FOMC QE1 announcement), max +25 bp. Extremes are genuine market events; kept.\n- Gaps between consecutive available obs (calendar days): 1 (3899), 3 (889, weekends), 4 (155, long weekends/holidays), 2 (58, mid-week holidays). Hence some changes span >1 trading day (post-holiday); by rule they are attributed to the weekday of t. Notably, after Monday holidays the Tuesday change spans Fri→Tue.\n- data/README.md: provenance text only, not analysed.\n## Information-availability\nNo input violates the rules. Only DGS10.csv used. Training weekday means use only changes dated ≤2009-12-31; the first 2010 change (dated 2010-01-04) uses the 2009-12-31 level, which is permitted because the change is dated in the evaluation period.\n## Exclusions\nOnly the rule-mandated drop of 215 empty rows plus the first available observation having no change (5217 → 5002 levels → 5001 changes).\nEOF\necho \"- 2026-10-30 P1: audit done; only rule-mandated exclusions.\" >> out/state/log.md","description":"Write data 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> out/analysis.py <<'EOF'\n\"\"\"F6-conf-01: weekday effects in daily changes of the 10y Treasury yield (DGS10).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/DGS10.csv; writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\nimport statsmodels.api as sm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"; os.makedirs(OUT, exist_ok=True)\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nd = raw[~empty].copy()\nd[\"date\"] = pd.to_datetime(d[\"observation_date\"], format=\"%Y-%m-%d\")\nd[\"y\"] = pd.to_numeric(d[\"DGS10\"], errors=\"raise\")\nassert d[\"date\"].is_monotonic_increasing and not d[\"date\"].duplicated().any()\nd[\"dy\"] = 100.0 * d[\"y\"].diff()\nd[\"dy\"] = d[\"dy\"].round(8)  # remove float noise; data have 1 bp resolution\nch = d.dropna(subset=[\"dy\"]).copy()\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nassert ch[\"wd\"].between(0, 4).all()\nch[\"weekday\"] = ch[\"wd\"].map(dict(enumerate(DAYS)))\n\npd.DataFrame([\n    dict(step=1, rule=\"drop rows with empty DGS10 field (holidays)\", n_before=n_raw,\n         n_after=n_raw - n_missing, n_removed=n_missing, rationale=\"TASK rule: no published yield\"),\n    dict(step=2, rule=\"first available observation has no change\", n_before=n_raw - n_missing,\n         n_after=len(ch), n_removed=n_raw - n_missing - len(ch), rationale=\"TASK rule: change needs y_{t-1}\"),\n]).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\ntrain = ch[ch[\"date\"] <= \"2009-12-31\"]; test = ch[ch[\"date\"] >= \"2010-01-01\"]\nassert len(train) + len(test) == len(ch)\n\n# (i) classical one-way ANOVA, all changes\ngroups = [ch.loc[ch[\"wd\"] == k, \"dy\"].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\nkw_H, kw_p = stats.kruskal(*groups)                       # sensitivity (rank-based)\nlev_W, lev_p = stats.levene(*groups, center=\"median\")     # equal-variance check\n\n# (ii) Monday contrast, Welch two-sample interval (primary)\nmon = ch.loc[ch[\"wd\"] == 0, \"dy\"].values; oth = ch.loc[ch[\"wd\"] != 0, \"dy\"].values\nest = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndf_w = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntq = stats.t.ppf(0.975, df_w)\nlo, hi = est - tq * se, est + tq * se\nwelch_p = 2 * stats.t.sf(abs(est / se), df_w)\n# sensitivity: OLS on Monday dummy with HAC (Newey-West, 5 lags) SE\nX = sm.add_constant((ch[\"wd\"] == 0).astype(float).values)\nhac = sm.OLS(ch[\"dy\"].values, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": 5})\n\n# (iii) out-of-sample\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\nf_model = test[\"wd\"].map(wmeans).values\ne0 = test[\"dy\"].values; e1 = e0 - f_model\nrmse0 = float(np.sqrt(np.mean(e0 ** 2))); rmse1 = float(np.sqrt(np.mean(e1 ** 2)))\ne_unc = e0 - train[\"dy\"].mean()\nrmse_unc = float(np.sqrt(np.mean(e_unc ** 2)))\n# Diebold-Mariano on squared-error loss differential (model - baseline), HAC 5 lags\ndl = e1 ** 2 - e0 ** 2\ndm = sm.OLS(dl, np.ones_like(dl)).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": 5})\n\nif p >= 0.05:\n    concl = \"no_detectable_effect\"\nelif lo > 0:\n    concl = \"positive_effect\"\nelif hi < 0:\n    concl = \"negative_effect\"\nelse:\n    concl = \"no_detectable_effect\"\n\n# table + figure\ntab = ch.groupby(\"wd\")[\"dy\"].agg(n=\"count\", mean_bp=\"mean\", sd=\"std\").reindex(range(5))\ntab[\"se_bp\"] = tab[\"sd\"] / np.sqrt(tab[\"n\"])\ntab.insert(0, \"weekday\", DAYS)\ntab[[\"weekday\", \"n\", \"mean_bp\", \"se_bp\"]].to_csv(f\"{OUT}/table1.csv\", index=False, float_format=\"%.6f\")\ncrit = stats.t.ppf(0.975, tab[\"n\"] - 1)\nfig, ax = plt.subplots(figsize=(6, 4))\nax.errorbar(range(5), tab[\"mean_bp\"], yerr=crit * tab[\"se_bp\"], fmt=\"o\", capsize=5, color=\"C0\")\nax.axhline(0, color=\"grey\", lw=0.8, ls=\"--\")\nax.set_xticks(range(5)); ax.set_xticklabels([s[:3] for s in DAYS])\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(f\"10y Treasury yield changes by weekday, 2000-2019 (n={len(ch)})\\n\"\n             f\"95% CI; one-way ANOVA F={F:.2f}, p={p:.3f}\", fontsize=9)\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=150); plt.close(fig)\n\nr = lambda x: float(round(float(x), 10))\nres = {\n    \"task_id\": \"F6-conf-01\", \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": [\n        {\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays)\",\n         \"handling\": \"dropped before differencing, per TASK rule\"},\n        {\"issue\": \"changes following a holiday span more than one trading day (e.g. Fri->Tue after Monday holidays)\",\n         \"handling\": \"kept and attributed to weekday of t, per TASK rule; noted as limitation\"},\n        {\"issue\": \"extreme changes (min -51 bp on 2009-03-18, QE1 announcement; max +25 bp); heavy tails\",\n         \"handling\": \"kept (genuine market events); rank-based Kruskal-Wallis reported as sensitivity\"},\n        {\"issue\": \"yields recorded to 0.01 pct, so changes are discretised at 1 bp\",\n         \"handling\": \"none needed; noted\"},\n    ],\n    \"n_input_rows\": {\"DGS10.csv\": int(n_raw)},\n    \"n_analysed\": int(len(ch)), \"random_seed\": None,\n    \"n_missing_rows\": n_missing, \"n_train\": int(len(train)), \"n_test\": int(len(test)),\n    \"anova_f_stat\": r(F), \"anova_p_value\": r(p),\n    \"effect_estimate\": r(est), \"effect_ci_low\": r(lo), \"effect_ci_high\": r(hi),\n    \"baseline_metric\": r(rmse0), \"model_metric\": r(rmse1),\n    \"conclusion\": concl,\n    # supplementary\n    \"effect_se_welch\": r(se), \"effect_df_welch\": r(df_w), \"effect_p_welch\": r(welch_p),\n    \"effect_ci_method\": \"Welch two-sample t interval (Monday vs Tue-Fri), all changes\",\n    \"sens_hac_effect_se\": r(hac.bse[1]), \"sens_hac_effect_p\": r(hac.pvalues[1]),\n    \"sens_kruskal_H\": r(kw_H), \"sens_kruskal_p\": r(kw_p),\n    \"sens_levene_W\": r(lev_W), \"sens_levene_p\": r(lev_p),\n    \"oos_rmse_ratio_model_over_baseline\": r(rmse1 / rmse0),\n    \"oos_rmse_unconditional_train_mean\": r(rmse_unc),\n    \"oos_dm_mean_loss_diff\": r(dm.params[0]), \"oos_dm_t\": r(dm.tvalues[0]), \"oos_dm_p\": r(dm.pvalues[0]),\n    \"train_weekday_means_bp\": {DAYS[k]: r(v) for k, v in wmeans.items()},\n    \"n_changes_total\": int(len(ch)),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_analysed\", \"anova_f_stat\", \"anova_p_value\", \"effect_estimate\",\n      \"effect_ci_low\", \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/table1.csv out/exclusions.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":992,"cache_read_input_tokens":10984,"output_tokens":3716,"output_tokens_details":{"thinking_tokens":160},"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":992},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrqco3Wpg4h9JYtNeaa","requestRef":"8a780638-4826-4462-8925-c6c894e84246","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"88b233a7-46c9-43ca-8d5e-8b753f8d1078","timestamp":"2026-10-30T05:21:45.011Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0276","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"88b233a7-46c9-43ca-8d5e-8b753f8d1078","isSidechain":false,"promptId":"ed6567c1-cc92-485e-be39-4424a6b85424","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01B8WwB51rzbqpt392dSqFeW","type":"tool_result","content":"{\n \"n_analysed\": 5001,\n \"anova_f_stat\": 0.2634657168,\n \"anova_p_value\": 0.9015060508,\n \"effect_estimate\": 0.0501700984,\n \"effect_ci_low\": -0.3193357757,\n \"effect_ci_high\": 0.4196759725,\n \"baseline_metric\": 4.8757041546,\n \"model_metric\": 4.8838415223,\n \"conclusion\": \"no_detectable_effect\"\n}\n{\n  \"task_id\": \"F6-conf-01\",\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"215 rows with empty DGS10 field (US market holidays)\",\n      \"handling\": \"dropped before differencing, per TASK rule\"\n    },\n    {\n      \"issue\": \"changes following a holiday span more than one trading day (e.g. Fri->Tue after Monday holidays)\",\n      \"handling\": \"kept and attributed to weekday of t, per TASK rule; noted as limitation\"\n    },\n    {\n      \"issue\": \"extreme changes (min -51 bp on 2009-03-18, QE1 announcement; max +25 bp); heavy tails\",\n      \"handling\": \"kept (genuine market events); rank-based Kruskal-Wallis reported as sensitivity\"\n    },\n    {\n      \"issue\": \"yields recorded to 0.01 pct, so changes are discretised at 1 bp\",\n      \"handling\": \"none needed; noted\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"DGS10.csv\": 5217\n  },\n  \"n_analysed\": 5001,\n  \"random_seed\": null,\n  \"n_missing_rows\": 215,\n  \"n_train\": 2500,\n  \"n_test\": 2501,\n  \"anova_f_stat\": 0.2634657168,\n  \"anova_p_value\": 0.9015060508,\n  \"effect_estimate\": 0.0501700984,\n  \"effect_ci_low\": -0.3193357757,\n  \"effect_ci_high\": 0.4196759725,\n  \"baseline_metric\": 4.8757041546,\n  \"model_metric\": 4.8838415223,\n  \"conclusion\": \"no_detectable_effect\",\n  \"effect_se_welch\": 0.1883795554,\n  \"effect_df_welch\": 1548.9581965108,\n  \"effect_p_welch\": 0.7900247218,\n  \"effect_ci_method\": \"Welch two-sample t interval (Monday vs Tue-Fri), all changes\",\n  \"sens_hac_effect_se\": 0.1936007292,\n  \"sens_hac_effect_p\": 0.7955256074,\n  \"sens_kruskal_H\": 3.2450803566,\n  \"sens_kruskal_p\": 0.5176806169,\n  \"sens_levene_W\": 9.588190973,\n  \"sens_levene_p\": 1.012e-07,\n  \"oos_rmse_ratio_model_over_baseline\": 1.0016689626,\n  \"oos_rmse_unconditional_train_mean\": 4.8751986529,\n  \"oos_dm_mean_loss_diff\": 0.0794170112,\n  \"oos_dm_t\": 1.8931519972,\n  \"oos_dm_p\": 0.058337659,\n  \"train_weekday_means_bp\": {\n    \"Monday\": -0.0543478261,\n    \"Tuesday\": -0.4054580897,\n    \"Wednesday\": -0.2054263566,\n    \"Thursday\": 0.0138339921,\n    \"Friday\": 0.1168316832\n  },\n  \"n_changes_total\": 5001\n}weekday,n,mean_bp,se_bp\nMonday,919,-0.052231,0.164386\nTuesday,1028,-0.219844,0.177007\nWednesday,1030,0.016505,0.182346\nThursday,1012,-0.059289,0.186918\nFriday,1012,-0.147233,0.189918\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10 field (holidays),5217,5002,215,TASK rule: no published yield\n2,first available observation has no change,5002,5001,1,TASK rule: change needs y_{t-1}","is_error":false}]},"uuid":"19530b04-e900-4f5f-8767-01038b22db00","timestamp":"2026-10-30T05:21:45.874Z","toolUseResult":{"stdout":"{\n \"n_analysed\": 5001,\n \"anova_f_stat\": 0.2634657168,\n \"anova_p_value\": 0.9015060508,\n \"effect_estimate\": 0.0501700984,\n \"effect_ci_low\": -0.3193357757,\n \"effect_ci_high\": 0.4196759725,\n \"baseline_metric\": 4.8757041546,\n \"model_metric\": 4.8838415223,\n \"conclusion\": \"no_detectable_effect\"\n}\n{\n  \"task_id\": \"F6-conf-01\",\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"215 rows with empty DGS10 field (US market holidays)\",\n      \"handling\": \"dropped before differencing, per TASK rule\"\n    },\n    {\n      \"issue\": \"changes following a holiday span more than one trading day (e.g. Fri->Tue after Monday holidays)\",\n      \"handling\": \"kept and attributed to weekday of t, per TASK rule; noted as limitation\"\n    },\n    {\n      \"issue\": \"extreme changes (min -51 bp on 2009-03-18, QE1 announcement; max +25 bp); heavy tails\",\n      \"handling\": \"kept (genuine market events); rank-based Kruskal-Wallis reported as sensitivity\"\n    },\n    {\n      \"issue\": \"yields recorded to 0.01 pct, so changes are discretised at 1 bp\",\n      \"handling\": \"none needed; noted\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"DGS10.csv\": 5217\n  },\n  \"n_analysed\": 5001,\n  \"random_seed\": null,\n  \"n_missing_rows\": 215,\n  \"n_train\": 2500,\n  \"n_test\": 2501,\n  \"anova_f_stat\": 0.2634657168,\n  \"anova_p_value\": 0.9015060508,\n  \"effect_estimate\": 0.0501700984,\n  \"effect_ci_low\": -0.3193357757,\n  \"effect_ci_high\": 0.4196759725,\n  \"baseline_metric\": 4.8757041546,\n  \"model_metric\": 4.8838415223,\n  \"conclusion\": \"no_detectable_effect\",\n  \"effect_se_welch\": 0.1883795554,\n  \"effect_df_welch\": 1548.9581965108,\n  \"effect_p_welch\": 0.7900247218,\n  \"effect_ci_method\": \"Welch two-sample t interval (Monday vs Tue-Fri), all changes\",\n  \"sens_hac_effect_se\": 0.1936007292,\n  \"sens_hac_effect_p\": 0.7955256074,\n  \"sens_kruskal_H\": 3.2450803566,\n  \"sens_kruskal_p\": 0.5176806169,\n  \"sens_levene_W\": 9.588190973,\n  \"sens_levene_p\": 1.012e-07,\n  \"oos_rmse_ratio_model_over_baseline\": 1.0016689626,\n  \"oos_rmse_unconditional_train_mean\": 4.8751986529,\n  \"oos_dm_mean_loss_diff\": 0.0794170112,\n  \"oos_dm_t\": 1.8931519972,\n  \"oos_dm_p\": 0.058337659,\n  \"train_weekday_means_bp\": {\n    \"Monday\": -0.0543478261,\n    \"Tuesday\": -0.4054580897,\n    \"Wednesday\": -0.2054263566,\n    \"Thursday\": 0.0138339921,\n    \"Friday\": 0.1168316832\n  },\n  \"n_changes_total\": 5001\n}weekday,n,mean_bp,se_bp\nMonday,919,-0.052231,0.164386\nTuesday,1028,-0.219844,0.177007\nWednesday,1030,0.016505,0.182346\nThursday,1012,-0.059289,0.186918\nFriday,1012,-0.147233,0.189918\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with empty DGS10 field (holidays),5217,5002,215,TASK rule: no published yield\n2,first available observation has no change,5002,5001,1,TASK rule: change needs y_{t-1}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"88b233a7-46c9-43ca-8d5e-8b753f8d1078","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0276","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","version":"2.1.314","gitBranch":"HEAD"}42{"type":"api-request-blob","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","hash":"9b37f4d7dc814b85fb9a3f2e25214bfd2e21c29718e57278ea01b336643dd030","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01B8WwB51rzbqpt392dSqFeW","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F6-conf-01: weekday effects in daily changes of the 10y Treasury yield (DGS10).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/DGS10.csv; writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\nimport statsmodels.api as sm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"; os.makedirs(OUT, exist_ok=True)\nDAYS = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n\nraw = pd.read_csv(\"data/DGS10.csv\", dtype=str, keep_default_na=False)\nn_raw = len(raw)\nempty = raw[\"DGS10\"].str.strip() == \"\"\nn_missing = int(empty.sum())\nd = raw[~empty].copy()\nd[\"date\"] = pd.to_datetime(d[\"observation_date\"], format=\"%Y-%m-%d\")\nd[\"y\"] = pd.to_numeric(d[\"DGS10\"], errors=\"raise\")\nassert d[\"date\"].is_monotonic_increasing and not d[\"date\"].duplicated().any()\nd[\"dy\"] = 100.0 * d[\"y\"].diff()\nd[\"dy\"] = d[\"dy\"].round(8)  # remove float noise; data have 1 bp resolution\nch = d.dropna(subset=[\"dy\"]).copy()\nch[\"wd\"] = ch[\"date\"].dt.dayofweek\nassert ch[\"wd\"].between(0, 4).all()\nch[\"weekday\"] = ch[\"wd\"].map(dict(enumerate(DAYS)))\n\npd.DataFrame([\n    dict(step=1, rule=\"drop rows with empty DGS10 field (holidays)\", n_before=n_raw,\n         n_after=n_raw - n_missing, n_removed=n_missing, rationale=\"TASK rule: no published yield\"),\n    dict(step=2, rule=\"first available observation has no change\", n_before=n_raw - n_missing,\n         n_after=len(ch), n_removed=n_raw - n_missing - len(ch), rationale=\"TASK rule: change needs y_{t-1}\"),\n]).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\ntrain = ch[ch[\"date\"] <= \"2009-12-31\"]; test = ch[ch[\"date\"] >= \"2010-01-01\"]\nassert len(train) + len(test) == len(ch)\n\n# (i) classical one-way ANOVA, all changes\ngroups = [ch.loc[ch[\"wd\"] == k, \"dy\"].values for k in range(5)]\nF, p = stats.f_oneway(*groups)\nkw_H, kw_p = stats.kruskal(*groups)                       # sensitivity (rank-based)\nlev_W, lev_p = stats.levene(*groups, center=\"median\")     # equal-variance check\n\n# (ii) Monday contrast, Welch two-sample interval (primary)\nmon = ch.loc[ch[\"wd\"] == 0, \"dy\"].values; oth = ch.loc[ch[\"wd\"] != 0, \"dy\"].values\nest = mon.mean() - oth.mean()\nv1, v2 = mon.var(ddof=1) / len(mon), oth.var(ddof=1) / len(oth)\nse = np.sqrt(v1 + v2)\ndf_w = (v1 + v2) ** 2 / (v1 ** 2 / (len(mon) - 1) + v2 ** 2 / (len(oth) - 1))\ntq = stats.t.ppf(0.975, df_w)\nlo, hi = est - tq * se, est + tq * se\nwelch_p = 2 * stats.t.sf(abs(est / se), df_w)\n# sensitivity: OLS on Monday dummy with HAC (Newey-West, 5 lags) SE\nX = sm.add_constant((ch[\"wd\"] == 0).astype(float).values)\nhac = sm.OLS(ch[\"dy\"].values, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": 5})\n\n# (iii) out-of-sample\nwmeans = train.groupby(\"wd\")[\"dy\"].mean()\nf_model = test[\"wd\"].map(wmeans).values\ne0 = test[\"dy\"].values; e1 = e0 - f_model\nrmse0 = float(np.sqrt(np.mean(e0 ** 2))); rmse1 = float(np.sqrt(np.mean(e1 ** 2)))\ne_unc = e0 - train[\"dy\"].mean()\nrmse_unc = float(np.sqrt(np.mean(e_unc ** 2)))\n# Diebold-Mariano on squared-error loss differential (model - baseline), HAC 5 lags\ndl = e1 ** 2 - e0 ** 2\ndm = sm.OLS(dl, np.ones_like(dl)).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": 5})\n\nif p >= 0.05:\n    concl = \"no_detectable_effect\"\nelif lo > 0:\n    concl = \"positive_effect\"\nelif hi < 0:\n    concl = \"negative_effect\"\nelse:\n    concl = \"no_detectable_effect\"\n\n# table + figure\ntab = ch.groupby(\"wd\")[\"dy\"].agg(n=\"count\", mean_bp=\"mean\", sd=\"std\").reindex(range(5))\ntab[\"se_bp\"] = tab[\"sd\"] / np.sqrt(tab[\"n\"])\ntab.insert(0, \"weekday\", DAYS)\ntab[[\"weekday\", \"n\", \"mean_bp\", \"se_bp\"]].to_csv(f\"{OUT}/table1.csv\", index=False, float_format=\"%.6f\")\ncrit = stats.t.ppf(0.975, tab[\"n\"] - 1)\nfig, ax = plt.subplots(figsize=(6, 4))\nax.errorbar(range(5), tab[\"mean_bp\"], yerr=crit * tab[\"se_bp\"], fmt=\"o\", capsize=5, color=\"C0\")\nax.axhline(0, color=\"grey\", lw=0.8, ls=\"--\")\nax.set_xticks(range(5)); ax.set_xticklabels([s[:3] for s in DAYS])\nax.set_ylabel(\"Mean one-day change in DGS10 (bp)\")\nax.set_title(f\"10y Treasury yield changes by weekday, 2000-2019 (n={len(ch)})\\n\"\n             f\"95% CI; one-way ANOVA F={F:.2f}, p={p:.3f}\", fontsize=9)\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=150); plt.close(fig)\n\nr = lambda x: float(round(float(x), 10))\nres = {\n    \"task_id\": \"F6-conf-01\", \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": [\n        {\"issue\": f\"{n_missing} rows with empty DGS10 field (US market holidays)\",\n         \"handling\": \"dropped before differencing, per TASK rule\"},\n        {\"issue\": \"changes following a holiday span more than one trading day (e.g. Fri->Tue after Monday holidays)\",\n         \"handling\": \"kept and attributed to weekday of t, per TASK rule; noted as limitation\"},\n        {\"issue\": \"extreme changes (min -51 bp on 2009-03-18, QE1 announcement; max +25 bp); heavy tails\",\n         \"handling\": \"kept (genuine market events); rank-based Kruskal-Wallis reported as sensitivity\"},\n        {\"issue\": \"yields recorded to 0.01 pct, so changes are discretised at 1 bp\",\n         \"handling\": \"none needed; noted\"},\n    ],\n    \"n_input_rows\": {\"DGS10.csv\": int(n_raw)},\n    \"n_analysed\": int(len(ch)), \"random_seed\": None,\n    \"n_missing_rows\": n_missing, \"n_train\": int(len(train)), \"n_test\": int(len(test)),\n    \"anova_f_stat\": r(F), \"anova_p_value\": r(p),\n    \"effect_estimate\": r(est), \"effect_ci_low\": r(lo), \"effect_ci_high\": r(hi),\n    \"baseline_metric\": r(rmse0), \"model_metric\": r(rmse1),\n    \"conclusion\": concl,\n    # supplementary\n    \"effect_se_welch\": r(se), \"effect_df_welch\": r(df_w), \"effect_p_welch\": r(welch_p),\n    \"effect_ci_method\": \"Welch two-sample t interval (Monday vs Tue-Fri), all changes\",\n    \"sens_hac_effect_se\": r(hac.bse[1]), \"sens_hac_effect_p\": r(hac.pvalues[1]),\n    \"sens_kruskal_H\": r(kw_H), \"sens_kruskal_p\": r(kw_p),\n    \"sens_levene_W\": r(lev_W), \"sens_levene_p\": r(lev_p),\n    \"oos_rmse_ratio_model_over_baseline\": r(rmse1 / rmse0),\n    \"oos_rmse_unconditional_train_mean\": r(rmse_unc),\n    \"oos_dm_mean_loss_diff\": r(dm.params[0]), \"oos_dm_t\": r(dm.tvalues[0]), \"oos_dm_p\": r(dm.pvalues[0]),\n    \"train_weekday_means_bp\": {DAYS[k]: r(v) for k, v in wmeans.items()},\n    \"n_changes_total\": int(len(ch)),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_analysed\", \"anova_f_stat\", \"anova_p_value\", \"effect_estimate\",\n      \"effect_ci_low\", \"effect_ci_high\", \"baseline_metric\", \"model_metric\", \"conclusion\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/table1.csv out/exclusions.csv","description":"Write and run analysis 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n_train+n_test==n_analysed: PASS\n- table n sums to n_analysed: PASS\n- exclusions: raw - removed == analysed: PASS\n- n_missing independent check: PASS\n- CI contains estimate: PASS\n- SEs positive: PASS\n- contrast matches table1: PASS\n- p in [0,1]: PASS\n- conclusion consistent with p>=0.05: PASS\n- RMSE magnitudes ~ sd of test changes (bp): PASS\n- baseline RMSE recomputed: PASS\n- first test change date 2010-01-04 uses 2009-12-31 level only: PASS","is_error":false}]},"uuid":"52f495ae-c453-4997-810f-2ffca797a1a8","timestamp":"2026-10-30T05:22:02.628Z","toolUseResult":{"stdout":"IDENTICAL\n- n_train+n_test==n_analysed: PASS\n- table n sums to n_analysed: PASS\n- exclusions: raw - removed == analysed: PASS\n- n_missing independent check: PASS\n- CI contains estimate: PASS\n- SEs positive: PASS\n- contrast matches table1: PASS\n- p in [0,1]: PASS\n- conclusion consistent with p>=0.05: PASS\n- RMSE magnitudes ~ sd of test changes (bp): PASS\n- baseline RMSE recomputed: PASS\n- first test change date 2010-01-04 uses 2009-12-31 level only: PASS","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"354c4e7f-1660-4687-a346-3122b5507979","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0276","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","version":"2.1.314","gitBranch":"HEAD"}54{"type":"api-request-blob","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","hash":"fd251be69fe6723cdcfed36f9ff18962dd48df2917699933286ddd74364b404e","message":{"role":"assistant","content":[{"type":"text","text":"Now P4 verification: rerun from clean state, consistency checks, gate."},{"type":"tool_use","id":"toolu_01X36MRte63jsgHLA1uvrrW1","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json && rm -f out/results.json out/table1.csv out/figure1.png out/exclusions.csv && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r1.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json,pandas as pd,numpy as np\nr=json.load(open('out/results.json')); t=pd.read_csv('out/table1.csv'); e=pd.read_csv('out/exclusions.csv')\nd=pd.read_csv('data/DGS10.csv')\nc=[]\nc.append((\"n_train+n_test==n_analysed\", r['n_train']+r['n_test']==r['n_analysed']))\nc.append((\"table n sums to n_analysed\", t.n.sum()==r['n_analysed']))\nc.append((\"exclusions: raw - removed == analysed\", 5217-e.n_removed.sum()==r['n_analysed']))\nc.append((\"n_missing independent check\", d.DGS10.isna().sum()==r['n_missing_rows']))\nc.append((\"CI contains estimate\", r['effect_ci_low']<r['effect_estimate']<r['effect_ci_high']))\nc.append((\"SEs positive\", (t.se_bp>0).all() and r['effect_se_welch']>0))\n# Monday contrast reproduced from table\nmon=t.mean_bp[0]; oth=(t.mean_bp[1:]*t.n[1:]).sum()/t.n[1:].sum()\nc.append((\"contrast matches table1\", abs(mon-oth-r['effect_estimate'])<1e-5))\nc.append((\"p in [0,1]\", 0<=r['anova_p_value']<=1))\nc.append((\"conclusion consistent with p>=0.05\", (r['anova_p_value']>=0.05)==(r['conclusion']=='no_detectable_effect')))\nc.append((\"RMSE magnitudes ~ sd of test changes (bp)\", 3<r['baseline_metric']<8))\n# independent recompute of baseline RMSE\nx=d.dropna(); x=x.assign(dy=100*x.DGS10.diff()); te=x[x.observation_date>='2010-01-01']\nc.append((\"baseline RMSE recomputed\", abs(np.sqrt((te.dy**2).mean())-r['baseline_metric'])<1e-6))\nc.append((\"first test change date 2010-01-04 uses 2009-12-31 level only\", te.observation_date.iloc[0]=='2010-01-04'))\nfor n,v in c: print(f\"- {n}: {'PASS' if v else 'FAIL'}\")\nEOF","description":"Reproducibility and consistency 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FQ9HrWq6GPEu4vVT6UVeWlFikrNdE3u917oVKtyAw+F4FwKqUpjaVDxG20ctN8GVeJWel1jgkdzPH3oxE+6za36ltqp6YzQXowo+1OqkC3ulxinYf/nll12hFbV2hmslDUfrFxpoeOsXfMGpC01dhKulQDd/1qxZ4+br0aOHRcsrhKTATy1j+pwKALVstUpqulpUdOEWXDTJ25YK1HSxnRRvflWXTKolPJhu/OjiVmm0WoaX9ZDSYzol5xu15Os7rhs5Cga9G0EprS4ajm7aqSiUWjR1wym0tTP4PB+uUI4+p1rtg/8WJDZ/uL8dwfOHZkqEzq/W72iqGqs4klLw9Z1WZoNSnZPzuZQdoOAzJS3twXQzQX8LgwtBide6re2eFC9o9FoKT5XO/cmp+B2uZT0c77wZfIMludcR+l3fB32vQoPlSNcdKaEbRfqby3i7mRt9CAEf6OJM9Mc01JQpU1wwEdoacNZZZ7mLOaUiatBiXSyk5sVLUrzBi/UHJSnq+6CWG11Yht4lDC21H0oXx2olUauSAkT1p/BK6CtQixS0pGSIBS+9RS0KShNV+pGWoyqE0aZNivaXLuKVwqtlaFnq4xJNa1tytms4CoLUKqLgQRePwRT0RLtd1AKlC1btn8TulqfE6tWr3bEbeoGQ1LEQDe9ueWi6baRp2t5qLVJwlxwKqnRBrdZCBRgKQsJVA40kuNqfx6swqhb/YF7rtAIKPXR8BQ9JkBS12ij4VOCnh5bvpXGppVDH2vfffx/3/+DvnlpXlMIazTGQ0mNX5wS1MCmIVOXW999//5SO6ZSeb7y+2QoGtZ21jZJzIygxupmlPpdKOda29s5doXRDT4F6pL8FwX8vgn9Pq/mTopRInS8VUGuZoX1HPTqOdDMl3PlE30uvonFqU0Crmw06lr1+honR/lHw8vfff6fK+59qH8JIdPMptKpqcq8jEptf07RPtT1OlfqP62Z1cNVZZELpXdUGyAqiHZheVeeCq+up2mJiA9N/8MEHbrl6naoGqiphSiRVZTS4Uqdn06ZNriJgtWrVElQU9KoQepX8VI0tb968rmpjcGW0FStWBEqUKJGg0qaqaGoA5dCKf+edd56rCOlVUVNVQ203VZQMrmKpwXM1GHGkKqNJDaA+ZcqUuO2qn6pCmFyqahm8jHBVTcNVGU3Odk3PKqNaz2i2a7hqr6raqMGrg6ts6tg988wzwx6HquynRzih86vKqL4zqloYXIFTA4HrWAmtMqpqizqmLrroorCVQpctWxb3ndQ2DrdPVLlPy1X1zmjPBarEGK7KqI7xcFQRVFUBdVyrynBydezY0X3OYsWKBR544IG46TNnznTrU7JkSVfBUt+zYPpMel4VeEOf03f5xx9/jPu/Po+Odw24Hlqp1zvPaXtGqjKqyrPeIOvBg7In95hO7vkmuEJmxYoV476z+szheOud1HnEM2HCBFcZ8owzzkgwqHw4yR2Y/oILLoh6YHqdJxMbmD7S8ReOztM6ZkqVKuVen5RIA9NfccUV7tjXIPIprTIqoZWLte91vOh1zzzzTNSfS5VzK1euHPa55JyLTpXOZaF/B4MrgOqccCrXEckdmD4lVUZVjVrzqbovMjfCeSCFlLLkDVTr/VTBF68ymfqIeS0kStdQ1TClE6nVQWOhKeVRrWK6SxduzDdRvwiND6Y0MLWihUvVSStq7dJddLVUqLVS76++LUq10edVC4jutGsMKH1m3R3X4OBqRVAxBbXqqQ+N+kKEVnHUtlN/J6WvqXVDd2x1h1MtFar456XDKNVPVQO1fbRcLUtVDpX2peqMajlNCRXY0Hor1UcFEryWj+RQqo03TpvWVxUdU3u7RqIKm+PGjXOpUmpRVaqU0jO9QeODB4xOKpVP8+pYVUuntosK23ipi2rpSwntMxVD0Hrp8+mzqRVVd6wTK5gTDd3RVl9NjX+oljBV3FValYrGqPVLKZ7BqaH6vikd0xtsXn0aVTBCrdCqnqk78Wq5U+uGWhGVGqrWTe0bfTe1vmrdUj8/jakYLX1XldKoFiilKKoFSf2g9B0JR62EXkthSjIB9Nl1PtG2Dm4FVMVdtX4rbUwtKKF38dVip1bFt956K67wi1rONBaaWgL1PfZafNQap+2sfap9q++gWprVWqd+iFqO+nhp24WjVhy1sioTQJ9R32+dC5N7TCf3fOPRcaHzi9Yxse3spVFG0+Kh76zO02pB1vlAqcahdAyoUIhHfwu0rfT+2h5KHde5TC0t6kOt/pDBVChJy9b2VjaDtqOKqegY1mcNzm7QNtX86u+q49g7ZnVsKHUz3PqFo+NIqfFq2VO6cbiiTEo51cOjlGB9DhWeUWqjWo10XtfvSoEO3gai87hXIdRrwde53nsv7WPte4+KkOm8qONL+1KtXHqdipSFVqdNjM7VSjvVOS64v6bflJquc732rVpe1T9R5yKdk3TNoOeDJfc64uyzz3bnYZ3/9H3SMaG/nyrypL9fGrs1tOVd84vXUq9zgFeFV/tT39NgXrEnr1ATMrH0jkiBzMprCYj0CG7R8Xz22Wfubq9aiHSXXePOqWUjqTv/iY0XlFYthMEtLLpLqLvRar3QT41tpvH4duzYEe/OtMYyU8uQxt5Sa5BaVlavXp3gbvt3330XuPnmm11Lku5aa1tou7z77rsJWin0/8ceeyxuTC+NeaQxrxIbhzCaO/tei0Sk8cCi8d577yU69la4FsLkbtdwLYTeeIa33HKLGytOd/GbN28e+O2339w+Ts6pXS0Teg/tL419qRamhg0bBt5+++0UtxCqJWbIkCGBGjVquH2mu/E6NnQ3/FRbCL2WgRdffDFQtWpVt856H43v5o3FNX78+ATLmTZtWuCaa65xLUja3mpFueyyywJvvvlm3HiGGtOvZ8+egfPPP9+11qkVSseoxizzxiqM9rygZWpsQI1z57VmzZo1K+LrNL/WS+8brtUgKWoN0vuqpSD4+JFmzZq55wYOHBj2tdpf77//vtvvGk9M66ttq5YsbdNwreMab7NChQpundX6qO+vjo/gFtbQFkKP1q927dquVU2tayk5ppNzvgmm8Vb1fPBYf6E09p+2o1pwkhI8RmekR7hxVLWe+v7r+6btrTHmgsdwDKUx/9RyXLhwYddKpNajH374IeL8Ot51LGpe7dOrrroq7NiEkXjnrsQe4bax9u3dd9/tjnvtF2V3aKzL0DH1gs9t0W63Pn36uONGn0kPtTbrHBxu2YnxjgGNi5qeLYRqDVWrns6P+juo7aVz2YMPPphoNlByriO0bd555x2XUaHlq7W3c+fOCcYBjWaf63gK9Z///Me1UgeP2YjMKZv+Se+gFEBkamXQ3W/d2VMLA1KH7paqNUnbNdrKnKHUeqs7+upLGE1BA6QtlfhX649a/tTym5mon5zu+qvFLlxRFKQOtaipVVktzJGqAqtlTX0AVRgGWZNauZUBoaFVEitChsj0t1MtrGoRVl9rZG4UlQEyMJXAVxqlqlcSDKYepbep5LxSh1IaDKqymsZ+00U8waC/VGApdIBr7VMF50pBTo1CCX7TuJxemhzSjoJtpVVGKtqj9Fjd4ElOlVdkPkqXVzq4hpdByigVVd0lohluCRkffQiBDEh9AjT+0NChQ93AzBpQGadOQaD6Z2j7KjkitD9ENNSXSZX31FdGY/lpLDT4S31rhg0b5vovKaBXXyD181MFSQXpmeWOv44f9fdSxcNRo0a5fjihFUhx6tavX+/6aum7r757GiomUol89ccLN04mshb1Z1S123BDGiE66vuq8224sUWR+ZAyCmRAutBVMQUVwVAn8muuuSa9VylLUOd4XXirA7/SC1NSvEPjSKl8uAo/KL1PAyPDX/pu6A6/0izVUqubJio4oQBfBUYyCxU1UsqV1l8FfXSBGlpQBKdON3B0XKgYi7ICVFiFi1gA+D8EhAAAAAAQo+hDCAAAAAAxioAQAAAAAGIUASEAAAAAxCgCQgCZyoEDB1zxkOrVq7vy8SrC0blzZ9uyZUvYoh2qOBnp8eeff8abf/fu3a4cfcmSJV0FNY0z+O+//yZY7h9//GG5cuWyiRMnpugzaFzJnj172rnnnusKXehzVK5c2W655RabNm1agkI44dYVGcvWrVvdMaF99e677yZalMg7/mbOnBl2Hh3TOh4iVSZVUSMNdaJjR8VRNMzGI4884tYhWLt27dz7TJ06NcmhGDTfwIED401XVU5vXSdPnmzJ5R27kR4q9pKaFi1a5ApwFS1a1AoWLGhNmjSxKVOmRP36v/76y22Diy++2EqXLu2+l9WqVXPngR07dkR83a5du9z2P+OMM1yVUp0/mjZtap999pllNjpndujQwVVhVbGjCy64wMaMGZNm52iPhn9QYSi9Z/Hixa19+/ZunMBQKiT1xBNPuPXSfNreZ511lj311FN28ODBFH1mAAw7ASATOXTokLvQ0jhh559/vhtcfuPGja5yqC785s2bF7ZKY506dey6665LMD209LyCQS1HF7L79++3119/3U6cOGFvvPFG3DwaruK///2v/ec//3EVC5Priy++cIGfLl70GfReuqhft26dfffdd648vi7QVcEUmYf2m0rYK9BRKfauXbsm+RoNWaKKqdHSjQgddxoQWsf0nXfeaTlz5nTH/eDBg+2DDz5wA697lVZvu+02d6Gt78dll10WcbneUB0a7zR0unif6corr7SU6NatW9hhHiIFvSmxYMECFwDq+9mxY0cXLI8dO9at8/jx4925IikaYFvBj8YWVUCiYEb7R+Nbarv+8ssvdvrpp8d7jYYM0fZWwKhg9IYbbrC9e/e68WN1LtH/MwudgxSUKcDV5y9fvrwbXuemm25yY39quI60OEer6qvOqboJd88997ibHqNHj3bj8Gp+VeL1aL5ff/3VGjVqFHe8fv/99y6Q19AzGhZINwMAJFMAADKJQYMGBXTauvnmmwMnTpyIm/7111+76Z06dYo3/5o1a9z02267Lcllb9682c377LPPxk276667Annz5g2cPHkybtrw4cMD+fLlc8tOrl9++SWQK1euQKFChdw6h/r333/dZ3zmmWfipmndtV7Lly9P9vvBP+ecc06gZMmS7tjU/lqxYkXY+fr27euer1q1qvv5zTffJJindOnSgUqVKsWbtnPnzkC5cuUC2bJlC7zyyisJXjN+/PhAnjx53LG1cuVKN+3YsWOBUqVKBQoWLBg4cOBA2PX5448/3Ho0b9483vQjR44EihcvHqhbt26gadOm7pjft29fsraJn8dugwYNAtmzZw/MmDEjbtq2bdsCZcqUCZx++unuu5WUESNGuO0R6oEHHnCfo2vXrvGmHz161O137a8lS5YkeJ22f2Zyww03uM85atSouGk6bvQZ8+fPH9iyZUuqn6O3b98eKFCggNtP2l+e6dOnu2O9TZs28eZ/4403Epx7tZ3btm3rlq/3B5B8BIQAMo1GjRpFvMDUBaEuiP/5558UBYRz585183777bdx09566y03zbtQ2b17t7voHzhwYIrW/6KLLnLLGz16dKLz6WI8qYvqdevWuWm6KI3W5MmTA82aNXNBgy7wLrjggngXf6FBiy7K3nvvvUDNmjUDuXPnDpxxxhmBoUOHhl32rl27Aj169HCBjubVRfIdd9wR2LRpU1Tr9sknn7j3/PTTT+NNv+aaa9z0xx9/PN703r17u+neBbwuPt98883AlVde6QInBd4KBG6//fbAhg0b4r1WgX7wa0PVr18/0SAq1IIFC9zy7r333sD333/vfn/yySfDzuttW21XHa/16tWLd8MhUkD46KOPhg1Kgj3//PNunvbt28dN6969e4KL/GCPPPKIe37kyJHxpn/++edu+gsvvODWVb+///77gYwYECoY0/to34fSzRU9p4A5pRQIaRm1a9eON13bVNMVSKYFb/v9/fffgaeeeipQoUIFd8ycd955gS+++CJV32vPnj2BnDlzBs4888wEz3388cduPYYMGZLq52gFeJECuRYtWgRy5MgR2LFjR5LvO2fOHLecli1bJjkvgIToQwgg01DaklSqVCls+tmRI0dcWlcopdgp/XPQoEEutS9cfyClK3n9kDwLFy50fVS8dDel+Kl/0qOPPprsdV+xYoVbN63njTfemOi8uXPnTnJ5SpdS3xl9tmgo5e+qq65yqWxKWVVK47Zt29xy+vbtG/Y1L7/8suvreNFFF7n5lUarNMXQvpPqu6ZUM81fs2ZNl+6qflh6z4YNGyba/8qjNDMJ7ld38uRJlwIWOl1+/PFH14eodu3a7v9Hjx61++67z6WstWzZ0rp37+7W6aOPPnLroDQ4jz6DjBgxImxaptIPlTIX7eDl3nLU90oDzCsl7sMPP3Tpi5FUqFDBpccptU5pnUlRyp1of0Ry7733uj5YSpFUPy4vbTT49cGUDv3JJ5+4FLvrr78+wWdSqqjSBdUXUcekl0Ka0Xhpt+HSYi+//PJ486SE+oaK0nODjRs3zm2jNm3a2PLly12q94svvuhSv7VtU4u+T0OHDnX7SMeu+vnpPaM5bqKlc5NSnk91Gyb3HJ3UvtN2nD17dor3EYDo8M0BkGkoAFi5cqXr66LAI5gukrw+PS1atIj3nPquBBeXyJcvnz377LP20EMPxQsIdQGi4GjNmjXugloXywqEsmfP7oIEFQvRxV40AVuoOXPmuJ/q56SLSD+pT5OCJQWzCkC8izV91gYNGrj+NwqAvODKowsxFXHw+nopyDrzzDNdn6rg/pPqW6QCEN98840LOj0KHNWHSAUf1E8oMeqbpQIeCvQ8Cl617goWtS4K9hTwHD582PUtUn86b1tqn+gYqFixYrzl6oJTfbzUD9QLfBXgqhCLbg4899xz8S4iveDujjvuiGrbHjt2zPV30jZt3LixWx9tS20j9YFSgBiJimOo35/6rrVt29YdZ+Hocyl4VxBZtWrViMtTYKf+bwqidbxqu6mvYd26dV2xIt088G58iL4TKnCkoDE4+NVF/bfffuuCer2naL9qf65atcrtp+TQtg/tQ6h1Cu7Xq761ugETLX13ixQp4n7XOUHCrZc3zZsnJbxgOnRfahurgIwCQR1bwTcAVDBKfdpCj8eU0PdAN6q8bajvofarvtOtWrWKOx+pSE9yCvWo/7L33U5sG5YqVcodW9Fsw+Seo1Nr30XaRwCiFKbVEAAyJKVN6bR16623xkuzU5qnpof2AVSqZ79+/QKLFi0K7N+/36V+Kf1JqYSaV2mKof1ZbrrpJtd3qmzZsoFu3boFDh486NIRlV7ppeIpda58+fIuxeriiy8O/Pnnn0mu+3PPPRc29dGPtDv1e9QylGYZSqmCeq5Xr14J0hr79++fYP5LL700UKxYsXjbWH23brzxxrDvff7557vtGY3OnTu7fkNeipj6ymnZP/zwg1ufKVOmuOnqJ6b/v/zyy1Et99xzz3X94IINHjzYLWPSpEnx+iIpXVOpsdGaMGGCW85jjz2WIP1Yx2kob9t6qclPPPFEgrTD0JRR9T3VPEo5Tor2g+YdM2ZM3DRtJ03TMRisQ4cObvq0adPiTffmf/vtt+OmaXmapu9gco/dcI/QNO7E5g33CO5Hdvfdd7tpOk5CKaVazzVp0iSQEvreKcVax3BoHzqlPyqlUenJr776qjtulcqt1GG9p84ZoenAyeFtE6UCh/rvf//rnvvuu+8SHFvRPpQSHppaGyktWOfMihUrpvo5Wt81TQvX39JLv+7Tp0+i76nzgc7F1atXDxw6dCjJdQSQECmjADIN3RlXKXOl4ykdUKXeldKmu+TnnHOOmye4lUV3tnXnXnfrdYdbqXyqQPjVV1+5VqEBAwbEW77u9qu1Z+fOna7lRC0bapF677333LAPQ4YMca0vd999t0v7/Prrr92wFGrpUHpjRqUWBrnkkksipmoGp8p61AoRriVPrXbBQxPosyslU0MqhD7UqqfntE0l9PlXXnklbllelUgvPVQ/1ZKkFj7tP6/10Hte8wfTPlKlWLXKqNXEG95Anz+05L3mU5rZ8OHD46aphVMtcWo5iZbXoqjjyqNWVx2nwambkSj9WC1dGkpCrY1p4eabb3afVd8bzz///ONa5dSy2axZswSfSfMHV8jUd6xQoUJJpsKGo3TK/1+zIO4Rmq6r/4fOk9gjNSuURqJUaFUOVYu0Uo9Dq2PquFdKo1rqlNapFjwde2+++aZLU1Y1TC/l+VSoomYoLT/4uy36PiVnG4bu9/Q4R58qfeeV0qysD1WV1U8AyUfKKIBMQymPSh1Uip3SsZSWp5Lk6h+oYRx08aGgLikaw00PXbDpwlgl6iNR/zel9uliS+l2PXr0cGmTL730knteF8m6YPvhhx8SLcuvcc1Egabf9BmD1yHcennzBAu3XRRIBwe/e/bsCZuWG0r7RxfMCnyCKSDxUne94FSBn1Iole6p/o45cuRw6ZheQKifhQsXdsFi8IWhAjEFVdoPuihVGqQCQgUb6rsUTMeJ0l51HClgVaqb5tPFqtfvLikKchVEKtVWNx2CKUDs37+/62eWWICpYFBBofqnvv/++64fYKR9FE1/Ue/4Ct7X+qxKr1XKp9Ic69ev78bI080M9SENTmHWjQE91A9T28SjC231W1NqnrZ/WgQTKeUdp+GOYW+ajpfk0DGhtEalOSq1ODgV2qNl6hhQsBNKgaTSxLW9w92ISY5w5zTd7Ir0mVN7G3rTg4d/SK1zdPD7FitWLMF7JrbvlKauvoe66aQUZ53TAaQMASGATEUXE2+//bZ7BPMKhUR7UeD1x9FFSmIB4WOPPebG4/LGBdTg1cEtZ97vCkgSCwi9O/pq3dLdeT/7EXqfT61fobxpiW2DxCggFvVDVFCTlMRal9Tqoz5rCjiWLVvmgnEvSFRroAIsFbbRhbYCkuCWBl146rmff/45QYuKxpZT8BNKx4wKc6ivqArCqOX4iiuuiNfPLjEKFBSALl26NOL+VCGWpFocNfC5+qFpG4bru6jtogBvw4YN7iI4Uj9CtUYqAFHQrqAvmIJcBYQK6PSc1+cqdOxBr+VOrd+JfabUDghPpQ+hgn9R/8ZQ3jRvnmjoJoeCQe3XYcOGRSwCVaNGDRcQhgtYvGnhjrvk0vdAN6HCFW8J/t6eSh/CxLah3kvHVrTbMDnnaC1Tx6zeNzQgTGzfqY+iMgcUuCvwDM0WAJA8BIQAMj3dIdZFgS4cgluNItFFvFpBlA6aWIui7nTr4lcBSnDhkeDWptCWp0h0QacUqrlz57rWGRUeiUQVM1NSuCYSL2hV+poCnmBelb9w6aHR0ODTChz0uVKDAkAFaAoQtFyvdUXTta2VjucNfh1MgZJatEKDQQW8urAMHVBcFMAr2FfaqFo9dVxEW0xGvKqburBWK2aoqVOnumNHF6/hqi561JKpVmgFOUpTDkcprmqV1uOtt94KO48uwLVtFMCEDs6tFittH6VEK8VR+12trsEX26oyqW2v74UC5HC0X9TqqfWMtgprNLTc5FQx1Tb3AkLvGNH2Dq0A7LVaR9tKp5sKag1UMSUda4kF8wqKdY5QSqyK+QTTNEmNojJ6DxX4CVekykvDFAWDoS3widH6ewGhCi3pHKdtGCq52zA552gtUzds9L4XXHBBgvfV9yr0O61WcLUM6qdu6IQWEQOQAmH6FQJAhhU6OLaKEWi8O53OVDAmmIrJBA+OLMePHw88+OCDbv5bbrkl4vtovjp16iQofqGiHSow4Y1RpzEFQ4s7JDUwfeHChcPOf/jwYTfuWzQD06tISmhxjcTGGNO4eioGs379+njbUuOOqXBL8MDaweMQhvLWJ1jr1q3dtI8++ijB/BoQXEVWouWNeafxHlUMJnhsRg2Orul6XuOOhY4tqII0y5Yti/eadu3auflDx/ULHc9QRYSKFi3q9kE0Fi9e7F6nokKRvP766wmK84QWlfHofTXOnI4tHR+h66uCJSrsoc+osdtCffnll4G8efPGG5g+1H333efeu2HDhnFjIYYuI6nvxcMPP5zouIbpNTC9CrgkZ2B6rVPodlIBqUsuucSts4oOJUXjA+r7rIHbg89Lf/31l/u+acD1nTt3JhgXNbTAUVLbT+NqBo/Fp/fVOKIqPhQ8ZqnfA9Nr+4Xbt8k5R3sD0+v7FzwwvfZjuIHpNf9ZZ53livkEF04CcGoICAFkKrrgve6669yA2g888EBclToFeaEUqKgaqKqDav577rnHXUxoflWk27p1a8T3UYVLBQi6AAn2008/uddrQHG9/2mnneYGbg8NPBOrSqkLIC1DAzVrvVXh8+abb44LdhRIJHVRraqJmh466HokqmKpCywFHKqeqkHkVTUwXBW/5AaE2o7eftDA1Pfff3/goYcecvupSJEiYQcMj2TFihVx1Qi1nNAKp5qu7RdalVBBpwIC7TPtZwU/2i9VqlRxgX2kgHDVqlVuu2i52i7R0vYLF1QFUzCggEHHWlIBoajCo/fZw63v77//7i6c9XzdunXdoPMasN67OaBjcerUqRHXZ/78+XHLV/C4d+/eeM+3bdvWPafqjpHoJovmueyyywIZKSDUZ1PQosedd97pto0CKe3bcIO4h9vGnTp1ctOrVavm9lPoI1xVW1UA1WsqV67sjjlVylVAr/cNrdi5evXqqLdd8Pb7z3/+44JafadUwVTHuJb/2WefBVLT2rVrA6VKlXLBVseOHd2xVaNGDbcOr732WoL5tf3CtSsk5xwtb731Vlzgq/2m/aebPyVKlHDbLNx5r379+mH3kSoqA0g+AkIAmUrXrl3dBYYuGHQBrIvhcePGhZ1XLVa6mFLLiy6A9Zqzzz7btQqFXgwH051wLVsXKuEMHTrUXQzlzp3bXaBEM+xEsI0bN7qLJd15V0uClqPl6SIsdAiASBfVulhSQJkcapXU9tJ7altoSIhwLT3JDQhF21Ml52vVquW2tbafftcF7Lx585K1nmrV0XuE7lcNIaLpl19+edjXKchSS5GCAgXXaunavHmz+8yRAkJREKvlLliwIKr184anCBdUhdKFsZY9a9asJANCLde7eI60vmrtVQCvIFeBsfajWnkVoOqzJkXHv5av4VWC7dq1yx2HuihP6uaGglEF38GtzekdEMrChQsDV199tQvItG3UehtuKAoJt429wDrSI9I++fTTT913Uced3lfLCbd/NcSJlhPpvBJp+6lFUN8t3dzSUBfa/uGC3NSgAExZEMom0PGtwEtZEOFECgiTc472KLjVd1evUcCrlv1wLd3ee0Z6RNv6CiC+bPonJammAID0sWLFCjfo86effhqx4AWio/6aKiKjPobhht4AUkuvXr1cJdn169e7fppJUf9F9atcs2aNL8NsAIhdjEMIAJmMKmmqWIXG38KpUaVQVYq866672JRI8+9t165dowoGAcBPtBACAGKOKnJqjLl33nnHXaCvXLkyVatmAqeKFkIAfmHYCQBAzHn++edd2XqV7dfwAgSDAIBYRQshAAAAAMQo+hACAAAAQIwiIAQAAACAGEVACAAAAAAxioAQAAAAAGIUASEAAAAAxCgCQgAAAACIUYxDmMWUKVPGDh48aBUrVkzvVQEAAACQCnbt2mUNGjSwiRMnWmqjhTCLUTB47Nix9F4NAAAAAKl4jb9q1SpLC7QQZjFey+DSpUvTe1UAAAAApILatWtbWqGFMAr//vuv9enTx2rUqGF58+a1008/3Tp37mybNm06pY3/999/W758+Sxbtmx2+eWXn9KyAAAAACC5CAiTcPjwYWvevLkNGDDADhw4YK1bt7YKFSrY8OHD7bzzzrPVq1dbSnXp0sWOHDmS4tcDAAAAwKkgIEzCwIED7ZdffrGGDRvaX3/9ZWPGjLG5c+fa4MGDbceOHa6lMCWGDh1qM2bMsLvvvjtFrwcAAACAU0VAmIijR4/aG2+84X5/8803rWDBgnHP9ejRw84991z78ccfbcGCBcna6Nu2bbNHH33UWrRoYR06dEjpvgMAAACAU0JAmIiff/7Z9u3bZ9WqVXPpoaHatWvnfk6aNClZG/3BBx90/RLfeuut5O4vAAAAAEg1BISJWLRokftZr169sM970xcvXhz1Bv/mm29c2ukTTzxh1atXT97eAgAAAIBURECYiPXr17uf5cuXD/u8N33dunVRjx9y77332plnnmmPPfZY8vcWAAAAAKQixiFMhKqKSv78+cM+X6BAAfdz//79UW3s3r17u+Bx+vTpljt3bkuLsUg0YKVSXAEAAAAgKbQQ+mT+/Pn22muvWadOnaxZs2Z+vS0AAAAAREQLYSK8qqKHDh2KmAIqhQoVSmwxdvz4cTe8RJEiReyll16y1LB06dJktRwCAAAAQCgCwkRUrFjR/dy4cWPY573plSpVSmwxbr6FCxdamTJl7IYbboj33N69e91PDV3htRxqfEIAAAAASGsEhImoU6eO+/nbb7+Ffd6brvEIo7F161b3CEeBocY0BAAAAAC/0IcwEY0bN7bChQu7Qi1q4Qs1btw497NVq1aJbuTKlStbIBAI+1CBGbnsssvipgEAAACAHwgIE6FKoPfdd5/7vVu3bnF9BmXIkCFu/MGmTZta/fr146a/8cYbVrNmTevVq1da7jcAAAAAOGWkjEYxVMSUKVNs9uzZdsYZZ9gll1ziho6YO3eulSxZ0oYNGxZv/p07d9qKFStsy5Ytp753AAAAACAN0UKYhLx587q0zqeeesqNR/jFF1+4gPD22293fQirVq2alvsHAAAAANJMtgCd1rIUb9iJSMNSAAAAAMhcaqfhNT4thAAAAAAQo+hDCABABnPo6PFUXV7+3Py5BwCEx18IAAAymFp9Jqfq8tY+1zJVlwcAyDpIGQUAAACAGEULIQAAGcyy/lcm+vy+f49Zw0HT4v4/p1dzK5wvlw9rBgDIaggIAQDIYJLq83fsRCDB/PQTBACkBCmjAAAAABCjCAgBAAAAIEYREAIAAABAjCIgBAAAAIAYRUAIAAAAADGKgBAAAAAAYhQBIQAAAADEKAJCAAAAAIhRBIQAAAAAEKMICAEAAAAgRhEQAgAAAECMIiAEAAAAgBhFQAgAAAAAMYqAEAAAAABiFAEhAAAAAMQoAkIAAAAAiFEEhAAAAAAQowgIAQAAACBGERACAAAAQIwiIAQAAACAGEVACAAAAAAxKqdfb3T06FGbPXu2/fjjj7Zw4ULbsWOH7d2714oUKWIlS5a0unXrWtOmTa1Ro0aWO3duv1YLAAAAAGJWmgeEf/75p73zzjv20Ucf2Z49eywQCISd78svv7Rs2bK5ALFTp07WpUsXO+uss9J69QAAAAAgZqVZQLhx40br3bu3CwRPnjxpFStWtJYtW1qDBg2sZs2aVqxYMTvttNNs3759LlBcvny5zZs3z7Ugvvrqq/b666/brbfeagMGDLDy5cun1WoCAAAAQMxKs4CwRo0a7ufdd99tt9xyizVu3DjR+S+77LK433/66Sf78MMP3eOzzz6zAwcOpNVqAgAAAEDMSrOAsGvXrvbYY49ZmTJlkv3aiy++2D369etnL7zwQpqsHwAAAFLHoaPHU3VT5s/tW5kLIOal2bft5ZdfPuVllC1bNlWWAwAAgLRTq8/kVF3e2udapuryAETGsBMAAAAAEKPSpT1eQ1D8/vvvtnnzZvf/008/3Q07kSdPnvRYHQAAAJyCZf2vTPT5ff8es4aDpsX9f06v5lY4Xy62ORBrAaGKw/Tp08eGDh2aoFBMwYIFrXPnzta/f38rVKiQn6sFAACANOzzd+xEIMH89BMEYiwg1PASzZo1s8WLF7v/16lTxypXrux+X7dunRus/rXXXrNp06bZzJkzrXDhwn6tGgAAAADEJN/6EKplcNGiRXbppZfakiVL7LfffrPx48e7x4IFC2zp0qXWvHlz95zmBQAAAABkkYDw888/d0NQTJw40c4666wEz2uwej1XunRpNy8AAAAAIIsEhLt27bKmTZta/vz5I86TL18+a9Kkie3evduv1QIAAACAmOVbQFi1alXbs2dPVH0Nq1Sp4ss6AQAAAEAs8y0g7NKli82YMcMVj4lEz6mozF133eXXagEAAABAzPKtyuiDDz5oK1eudEVl7r//frvxxhutUqVKcVVGx44da6+//rp17drVunfv7tdqAQAAAEDM8i0gzJEjh/sZCATsmWeecY9Qeu7NN990j2DZsmWz48eP+7WqAAAAABATfAsIK1So4AI7AAAAAECMBYRr1671660AAAAAABmpqAwAAAAAIEZbCMPxhqEoUqQI6aQAAAAAkNVbCCdOnGhXXHGFFSxY0EqUKOEehQoVctO+/PJLv1cHKXDo6PFUfQAAAADI4i2EqiB655132siRI93vXsug7N2716ZMmWJTp061W2+91YYPH06LYQZWq8/kVF3e2udapuryAAAAAGSwFsJXX33VRowYYWXLlrW3337bBYG7d+92j3379tk777zjnvvwww/dvAAAAACALNJC+N5771n+/Plt1qxZVqVKlXjPKWW0S5cu1qJFCzvnnHPcvA899JBfq4ZkWtb/ykSf3/fvMWs4aFrc/+f0am6F8+ViOwMAAACxGhCuWbPG9RMMDQaD6bnLLrvMvv/+e79WCymQP3fih82xE4EE8yf1GgAAAABZOGW0ZMmSljt37iTny5Urlys0AwAAAADIIgFhmzZtbNq0aXFDTYSj/oSa57rrrvNrtQAAAAAgZvkWEA4cONCqVq1qzZs3d0FfqOnTp7s+hNWqVbNnn33Wr9UCAAAAgJiVZh27FPiFUsroggULXOBXrFgxq1Spkpu+fv1627Vrl/v9oosuci2EGoICAAAAAJAJA8IZM2ZEfE7jECoA9ILAYHPmzGEMQgAAAADIzAGhqooCAAAAAGIwIPTSQQEAAAAAMV5UBgAAAAAQIwHhzp07M9RyAAAAAAA+BYRVqlSxXr16pTig2759u/Xs2dMtBwAAAACQiQLCG264wV588UUrX768G0ZizJgxtnXr1kRfs2XLFhs9erS1atXKKlSoYEOGDLH27dun1SoCAAAAQExLs6Iyw4YNs+7du1vv3r3tq6++skmTJrnpp59+up155plWtGhRK1SokO3fv992795tK1ascAGhZM+e3QWFAwYMsNq1a6fVKgIAAABATEuzgFDOOecc+/LLL23jxo02dOhQFxguXLjQNm3alHBFcua0888/31q2bGmdO3d2LYsAAAAAgEwaEHoU3PXt29c9Dh48aMuWLXN9BPft22eFCxe2UqVKuZbA/Pnz+7E6AAAAAAC/AsJgBQoUsAsuuICNDwAAAACxFhACAAAAQHo6dPR4qi4vf+7MG1Zl3jUHAAAAgBSo1Wdyqm63tc+1zLT7Ic2GnQAAAAAAZGy0EAIAAACIKcv6X5no8/v+PWYNB02L+/+cXs2tcL5clhUREAIAAACIKUn1+Tt2IpBg/szcTzAxpIwCAAAAQIwiIIzCv//+a3369LEaNWpY3rx57fTTT7fOnTvbpk2bot7Qe/futU8++cQ6dOhgVapUsdy5c1uhQoXswgsvtFdffdWOHTt2KvsRAAAAAJIta7Z7pqLDhw9b8+bN7ZdffrGyZcta69atbe3atTZ8+HD76quv3PSqVasmuZyXXnrJnnnmGcuWLZvVrVvXBYI7duywn3/+2ebNm2fjxo2zyZMnW/78+X35XAAAAADgS0D4+++/26RJk2zx4sW2bt06279/v5uuFrJKlSrZueeea61atbLzzjsvw+2RgQMHuqCvYcOG9v3331vBggXd9CFDhtjDDz/sWgpnzJiR5HIKFChgPXv2tG7dulnFihXjpv/99992+eWX208//eTe69lnn03TzwMAAAAAvgSEaklTwPTjjz+6/wcC8TtnyoIFC2z8+PH29NNPW7NmzWzo0KFWuXJlywiOHj1qb7zxhvv9zTffjAsGpUePHjZy5Ej32fQZ6tevn+iyevXqFXb6GWecYc8995x17NjRRo8eTUAIAAAAIPMHhJs3b7aLLrrItm/f7loA27VrZ/Xq1bPy5cu71jI5ePCgbdy40X777Tf77LPPbPr06a4lTgGW+umlN6Vz7tu3z6pVqxa29VKfSa2eav1MKiBMTJ06deK2GQAAAABk+oDwqaeecsGgUisfeuihiPMpWLz66qutd+/ebt5HHnnEFXD54IMPLL0tWrTI/VQgG443XUHhqVi9erX7WaZMmVNaDgAAAABkiCqj3333nSucklgwGEppmHrNt99+axnB+vXr3U+1aobjTVe/yFOhKqOigjUAAAAAkOlbCHfv3m1NmjRJ9utUZGbhwoWWERw4cMD9jFT500t99YrkpMQ777xjU6ZMsSJFitjjjz8e9etq164ddvqqVatciisAAAAApFsLoSppzpo1yw4dOhT1azSvXlOhQgWLBfqsDz74oBuKYtiwYRmi3yQAAACA2JFmAeGNN97oiqRceeWVUfWx0zyad+vWra7iZkbgVRWNFNSqKI43fEZyLVmyxKWIqpKpUkbbtGmTrNcvXbo07IPWQQAAAADpnjL6xBNP2A8//OAqdapCpwIVr8qol4KpQMurMqpURw1LocqkkYZo8Js3XqDWMRxvutJck2PNmjV2xRVX2J49e6xfv352//33p8LaAgAAAEAGCQjz5s3rBmwfMGCAG8Nv5cqV7iFKkQwdl7Bw4cJ23333uWqjefLksYzAGw5CAWs43nRVSo3Wli1brEWLFu6n0kX79u2bSmsLAAAAABloYHoFdgMHDnRBj1oKNYyDKnd6xVqUkqlWOAVejRs3tly5cllGonVSoKrWSxW6qVu3brznx40b5362atUqquWpRVBpsVreHXfcYS+//HKarDcAAAAApHtA6FGg16xZM/fITHLnzu1aLZ955hnr1q2bff/993GVRTVmovo9Nm3aNN6g9G+88YZ7qE/goEGD4qYrPbZly5b2xx9/WPv27e3999+PaykFAAAAgCwbEGZmSmHVsBCzZ8+2M844wy655BI37uDcuXOtZMmSrjposJ07d9qKFStcSmiwJ5980ubMmWM5cuSwnDlz2p133hn2/UaMGJGmnwcAAAAAMmxAuG3bNjty5EhcQZf0pr6Q06dPd619n3zyiX3xxRdWrFgxu/32213/yEiD1odLF5UTJ0645URCQAgAAAAg0w87kVLXXXedVa1a1TKSfPnyWf/+/V1RHAWrav0bPnx42GBQVUNVLCc0sNP/NT2pBwAAAADEbEAoBEYAAAAAEKMBIQAAAAAgE/chbN68eYpet2zZslRfFwAAAACAjwGhBqXXsAopSf9kOAYAAAAAyMQBoQZ0/+eff9zYfYUKFYr6dXfddRethAAAAACQmQPC888/36ZNm+YGd7/wwgujfl3BggXTapUAAAAAAH4UlfGCwPnz56fVWwAAAAAAMmILYaNGjey0006z5cuXJ+t1LVu2tJo1a6bVagEAAAAA0jogvPrqq23Pnj3Jfl3v3r3TZH0AAAAAAPExDiEAAAAAxCgCQgAAAACIUWmWMhqNOXPm2Ndff207d+60cuXKWZs2bezss89Oz1UCAAAAgJiRZi2Es2bNcv0IX3vttbDPd+/e3S6++GIbNGiQvffee9avXz8777zz7MUXX0yrVQIAAAAA+BEQTpw40SZPnmznnntugufGjh1rr776qgUCAatfv749/PDDdu2119rJkyetV69e9ssvv6TVagEAAAAA0jpl9Oeff7bixYtbs2bNEjw3ZMgQ91NB4IQJEyxbtmzu/x988IF16dLF3n33XbvooovSatUAAAAAAGnZQrhhwwY7//zzE0xXf8Fff/3VBYFKE/WCQbnzzjutcuXKLpgEAAAAAGTSgHDHjh1WrFixBNMVDCpVtHTp0la3bt14zyk4VFGZTZs2pdVqAQAAAADSOiDMnTu3bdu2LWxAKBdccEHY1xUqVCitVgkAAAAA4EdAeMYZZ9jcuXNt37598aZ/9dVXriVQFUbD2bx5s5UpUyatVgsAAAAAkNYBoQrGHDhwwDp27Ghr1qyx/fv32/PPP2/z5893AeH111+f4DXHjh2zBQsWuH6EAAAAAIBMWmW0R48eNmLECPv222+tevXq8Z67/fbbrWrVqmGHqlAQGa4yKQAAAAAgk7QQqi/gzJkz7ZprrrEcOXK4QjJ58uSxrl272htvvBH2NS+88IL7eeWVV6bVagEAAAAA0rqFUCpUqOBa/Q4fPmx79uyxEiVKWK5cuSLO//3337ufhQsXTsvVAhBjDh09nqrLy587TU+dAAAAvvHlqiZv3rxWtmzZJOcjEASQFmr1mZyqy1v7XMtUXR4AAECWSxkFAAAAAMRwC6H6EG7cuNEuvPBCq1atWtx0VRudPDn8HXtVH+3WrVtarhaAGLOsf+L9kvf9e8waDpoW9/85vZpb4XyR09sBAACyijQLCHfs2GEtW7a0UqVK2aJFi+I99+eff9qMGTPCvk4D1994442uvyEA+NHn79iJQIL56ScIAABiQZoFhKNHj7aDBw9a3759rWDBggme11iEs2fPjjftl19+se7du9tHH31kDz30UFqtGgAAAAAgLQPC7777zgoUKGAdOnSIOI9SSYM1aNDAnn32WZdOSkAIAAAAAJm0qMzixYvtggsuSHSYiXCthrVq1bI//vgjrVYLAAAAAJDWLYQ7d+60pk2bhn1O03PmDP/WGp5CqaMAAAAAgEwaEKplUAPSh3P77be7RzhHjhyJGCwCAAAAADJBymiZMmVcNdHkWr58uXstAAAAACCTBoSNGjVyAeGSJUuifo2Gp9BrGjdunFarBQAAAABI64Dw1ltvtUAgYF27dnVpoEnRPPfcc48rLKPXAgAAAAAyaUB4+eWXu4Hp58yZYxdddJFNmzYt4rx6TkNQzJs3z73msssuS6vVAgAAAAD8f2laveWTTz6xq666ygWFLVq0sBIlSljdunXdT68S6cKFC91PtSY2bNjQvQYAAAAAkMkDwkKFCtnMmTPtxRdftFdffdW2bdtmP/zwQ4L5Spcu7Qaif/jhh6kwCgAAAAA+SfPxHXLkyGGPP/64Pfroo/b777+7wjG7du1yzxUvXtzq1Klj5513npsPAAAAAOAf3wb8U8B3/vnnuwcAAAAAIP35PgL88ePHbc+ePa6aaNGiRWkZBAAAAICsVmU02B9//GFdunSxM844w/LkyeMGnle/wdy5c1vNmjXt3nvvtWXLlvmxKgAAAAAAvwLC3r17u8qiQ4cOtVWrVrlqosGPv/76y9555x0799xzrX///mm9OgAAAAAAPwLCF154wZ599lnLnj273XzzzfbVV1/Zxo0b7fDhw/bvv//ahg0bbOLEidaxY0eXQvr000/bkCFD0nKVAAAAAABp3Ydw+/bt1qdPHytQoIALBJs2bZpgnnLlyrnHNddcY3fddZf7qRbFTp06xY1VCAAAAADIZC2EI0eOtKNHj9pzzz0XNhgM1axZMzevWg9HjRqVVqsFAAAAAEjrgHDWrFmWP39+1/IXrbvvvtu9ZsaMGWm1WgAAAACAtA4IVVm0Xr16rqpotDRv/fr1bcmSJWm1WgAAAACAtA4INdZg2bJlk/06DUmxe/fuNFknAAAAAIAPAeH+/ftdQZnkUsrogQMH0mSdAAAAAAA+BIQaYzA9XgsAAAAASOdhJ2TlypXJrhiq1wAAAAAAMnlA+PPPP7tHclsHNUg9AAAAACCTBoS33XZbWi0aAAAAAJCRA8Lhw4en1aIBAAAAABm5qAwAAAAAIGMjIAQAAACAGEVACAAAAAAxioAQAAAAAGIUASEAAAAAxCgCQgAAAACIUQSEAAAAABCjCAgBAAAAIEYREAIAAABAjPI1IFy2bJndfvvtVrVqVcuXL5/lyJEj7CNnzpx+rhYAAAAAxCTfIq85c+bY5Zdfbv/++6/7f7FixaxMmTJ+vT0AAAAAIL0Cwl69erlg8KGHHrLevXu7gBAAAAAAEAMpo/Pnz7e6devakCFDCAYBAEihHfuP2PszV8eb9sGs1W46AAAZtoUwd+7cVrNmTb/eDgCALOXwsRP29KSlNm7BRjt2IhDvudenrbR3flxl7epXsL6talneXDnSbT0BAJmLbwHhxRdf7IrKAACA5AeDtw2bZ3PX7I44j4LE0fPW2+odB2xk5wYEhQCAjJUy+uyzz9rKlSvtzTff9OstAQDIEtQymFgwGEzzPT2JG7AAgAzWQvjbb7/ZHXfcYQ888ICNHTvWWrRoYeXLl7fs2cPHpJ06dfJr1QAAyLC27z/s0kSTY9yCDdajRQ0rWShPmq0XACBr8C0g1PiD2bJls0AgYLNmzXIP/T+Untd0AkIAAMzG/rohQZ/BpGj+sfM3WLdLq7MJAQAZIyDs06dP2AAwM9BwGYMGDbJPP/3U1q9f76qkXnXVVTZgwAArV65cspa1Z88e69evn33xxRe2detWNxZjmzZt3LQiRYqk2WcAAGRO0aaKhvpl9S4CQgBAxgkIFfBkRocPH7bmzZvbL7/8YmXLlrXWrVvb2rVrbfjw4fbVV1+56VWrVo1qWTt37rSGDRu6vpR6zXXXXWdLly61V1991b799lubM2cOQ3IAAOI5cOS4r68DAMQW34rKZFYDBw50QZ8Cub/++svGjBljc+fOtcGDB9uOHTusc+fOUS/roYcecsFg27ZtbcWKFW5ZS5Yssfvvv98tu0ePHmn6WQAAmU/BPDl9fR0AILYQECbi6NGj9sYbb7jfVR21YMGCcc8peDv33HPtxx9/tAULFiS5obds2WKjR4924zG+9dZbljPn//2hfvHFF61kyZL20Ucf2fbt209tjwIAspQLqxRL0esuqlo81dcFAJD1pNntQ7Wcqc+ghpsoXbp0slrS9LqhQ4daevv5559t3759Vq1aNTvvvPMSPN+uXTtbvHixTZo0yerXr5/osr777js7efKkXXLJJW57BMuTJ4+1atXKhg0bZt98840rwAMAgLS/oIK9OvXvZBWWyZUjm7U/vwIbEACQfgHhiBEjXGD32GOPuQBI/89sAeGiRYvcz3r16oV93puuoDA1lqWAMJplAQBiR6lCea1d/fI2et6GqF/Trn4FhpwAAKRvQDh9+nT3s2LFivH+n5mooqhovMRwvOnr1q3zdVkAgNjSt1VtW73jYFQVR5Vi2rdVLV/WCwCQ+aVZQNi0adNE/58ZHDhwwP3Mnz9/2OcLFCjgfu7fv9/XZUnt2rXDTl+1apUVLVrU9VMMddNNN7kqprt373ZDaIRz7733up8qfvP9998neF7L7tChg/t93rx5Nn/+/ATzlK9YOe73BrnW26ih71mO7PGHHDn//POtQYMG7nf1rdRwHKGuuOIKq179f2Nohfs8fn4mVYXVUCNe+u/q1asTzMNnyrz76cTJgF2X53/fUVm7prrVqXVmpv5Mofg+Zf791OhkNjtcsKgtPXSaHT+ZMH00uwXsnIIHrPHxdZY3V8NM8Zk8fJ+y/n7yzrP7A3ls6tEzssRnyor7ic9UNW4//Tj1B7suz5K4bTJq6Fp3PZte+0nzaf60QAkyAAAygVzZA3Zlsd320m3NbOLyffbWtBV2WrYjltNO2pmFjlmdQgesQI6T6b2aAIBMJlsgEIi+l3qMUSXRl19+2bp3725DhgwJ2y+wbt26rv9fUpVGNdTEhAkT3JiDDzzwQILnv/zySzcuoeb7/PPPU7zOXsuhxjdML/v+PWZ1nv6/u02L+l5hhfPlSrf1AZLCMYvMhmMWmQ3HLDKbfRnsejYtr/EZdiIRXv/HjRs3hn3em16pUiVflwUAAAAAqYGAMBF16tRxP3/77bewz3vTNR6hn8sCAAAAgNRAQJiIxo0bW+HChV2hloULFyZ4fty4ce6nxhBMijqoZs+e3WbNmpVg8PkjR464sQxz5MhhV199dfL3IgAAAACkAAFhInLnzm333Xef+71bt2528ODBuOfUp1BjBqp6avCg9G+88YbVrFnTevXqFW9ZZcuWdRWojh496ioGHT9+PO65nj172o4dO+yWW26xUqVKpWQ/AgAAAECyUWU0Cb1797YpU6bY7Nmz7YwzzrBLLrnEjRU4d+5cK1mypBtMPtjOnTttxYoVtmXLlgTLeuWVV+yXX35xRWMUNKpsrTqGLlmyxC07XOEaAAAAAEgrtBAmIW/evDZ9+nR76qmn3BiCX3zxhQsIb7/9dtfvT+PKRKtEiRJufJr777/ftRSq6ui+fftc1VFNT6uxRQAAAAAgQwSEGvyyTZs2Vq5cOcuTJ4/deeedcc9NnjzZDfWwefNmy0jy5ctn/fv3d4NCqr+fWv+GDx9u5cuXTzBvv379TCN5jBgxIuyyFPS99tprtn79ercs/dRQFEWKFPHhkwAAAABAOgWEDz74oP3nP/9xY+7t37/fjh075oKn4H52SqscM2aMn6sFAAAAADHJt4Bw1KhR9vrrr7sCLEq1/OeffxLMoyEXKlSo4CpuAgAAAACySFGZt99+26VFfv31164YSyQKCv/44w+/VgsAAAAAYpZvLYSqpNmoUaNEg0HRuH/btm3za7UAAAAAIGb52ocwW7ZsSc6jgjIq4gIAAAAAyCIBocbZU99BFZKJRIVmFi5caLVr1/ZrtQAAAAAgZvkWEN5www1uuIbHH3884jy9evVy4/LddNNNfq0WAAAAAMQs34rKPPTQQ/bpp5+6YSVmz55trVu3dtNXrVplL7/8shuk/aeffrJ69erZ3Xff7ddqAQAAAEDM8i0gVL/AKVOm2O23327ffvutzZs3z02fNWuWe0iLFi3so48+sty5c/u1WkhlO/YfsZGz18ab9sGs1dapYWUrWSgP2xsAAACIxYBQVGFUw04sWrTIvv/+e1u7dq2dPHnSypcv74LBBg0a+Lk6SEWHj52wpycttXELNtqxE4F4z70+baW98+Mqa1e/gvVtVcvy5srBtgcAAABiLSD01KlTxz2QdYLB24bNs7lrdkecR0Hi6HnrbfWOAzaycwOCQgAAACDWhp1A1qSWwcSCwWCa7+lJy9J8nQAAQMbpTvL+zNUJupNoOoAYaiEcNWpUVPOp/2Dx4sVdC2KpUqXSfL1warbvP+zSRJNj3IIN1qNFDfoUAgCQhdGdBMgcfAsIVUwmmoHpPZr38ssvt9dff92NYYiMaeyvGxL0GUyK5h87f4N1u7R6mq0XAABIP3QnATIP3wLCPn36uCIyaiksWLCgXXHFFVaxYkX33IYNG1yRGQ1Mf+utt1qePHnc0BSadskll9iCBQusXLlyfq0qkiHaVNFQv6zeRUAIAEAWlZLuJIPanpPm6wUgHQNCBXqqItq5c2cbPHiwFS5cON7z//zzj/Xo0cONRzh37lyrWrWqPfroo26Mwueee861FCLjOXDkuK+vAwAAGRvdSYDMxbeiMr169bKiRYvae++9lyAYlNNOO809p3meeOIJy549uw0aNMjKli1r3333nV+riWQqmCenr68DAABZtzsJgCwcEE6fPt0uvPBCF+hFXJns2V0r4rRp0+IKzKi4zKZNm/xaTSTThVWKpWibXVS1ONsaAIAs6FS6kwDIwgHhoUOHbOvWrUnOt23bNjt8+HC8lsOcOWlNyqjaX1DBcuWIvliQaP7251dIs3UCAADph+4kQObiW0B4zjnn2MyZM90jklmzZtmPP/7o5vWo4EzJkiV9WkskV6lCea1d/fLJek27+hUYcgIAgCyK7iRA5uJbQNizZ087ceKEXXnllda1a1f74Ycf7M8//3QP/X7PPfe45wKBgJtX9u3b5yqMXnTRRX6tJlKgb6vaUaeOar6+rWqxnQEAyKLoTgJkLr7lYrZt29ZVDH3sscfs/ffftw8++CDe8woE1WdQ87Rp08ZN27Vrlz399NN22WWX+bWaSIG8uXLYyM4NXMloDTofriO50kTVMqhgUPMDAICs253k1al/J6uwDN1JgPTja+e8Bx980K699lobOnSoG2dwy5YtbroqiTZu3NjuuOMON9yER78rgETGpyBP4wf1aFHDRs1Za69PWxn33P3Nq1unhpVJEwUAIIa6k4yeF33VULqTAOnH92otVapUsYEDB/r9tvBJyUJ57K5LqsYLCPX/wvlysQ8AAIih7iSrdxyMquIo3UmAGOlDCAAAgNjqTtKhQcWI1cg1Xc9rPrqTAOknXcZzWL9+vUsXPXLkSMR5mjRp4us6AQAAIPXQnQTIHHwNCIcNG2YDBgxwAWFSVJEUAAAAmRvdSYCMzbeAcPjw4XbXXXe5388++2yrUaOGFSpUyK+3BwAAAACkV0A4ZMgQy5kzp40bN85VGgUAAAAAxEhRmb///tv1CyQYBAAAAIAYCwiLFStmJUqU8OvtAAAAAAAZJSBs3bq1/fzzz3bs2DG/3hIAAAAAkBECwmeffdYKFChgd9xxh+3Zs8evtwUAAAAApHdRmYcffthq1aplo0ePtq+//trq169v5cuXt+zZE8ak2bJls6FDh/q1agAAAAAQk3wLCEeMGBH3+759+2zatGkR5yUgBAAAAIAsFBBOnz7dr7cCAAAAAGSkgLBp06Z+vRUAAAAAICMVlQEAAAAAZCwEhAAAAAAQo3wNCA8dOmQDBw60Cy64wIoUKWI5cuQI+8iZ07dMVgAAAACIWb5FXqoseskll9jSpUtd0Jc7d24LBAJWtmxZ27p1q/tdKlWq5NcqAQAAAEBM862F8LnnnrMlS5ZYly5d7J9//rF27dq54SU2bdpkBw8edMNSlClTxi688EJbvXq1X6sFAAAAADHLt4Dwiy++sNNPP91ee+01y5s3rwsGPfp/p06dbMqUKTZhwgQbPHiwX6sFAAAAADHLt4Bw3bp1Vq9ePcuVK9f/3jj7/9762LFjcfPUqlXLDU8RPIg9AAAAACCTB4RqBdTDc9ppp7mf6j8YrFixYrZmzRq/VgsAAAAAYpZvAWGFChVsw4YNcf+vWbOm+/njjz/GTTt+/Lj9+uuvVrx4cb9WCwAAAABilm8BoSqMLl682Pbv3+/+36pVKze8xAMPPGDvvPOOTZo0yRWaWbt2rUsbBQAAAABkkYDwpptusnPPPddmz57t/q8CM4MGDbK9e/dat27d7LrrrrOJEyda6dKl7fnnn/drtQAAAAAgZvk2DuHFF19sc+bMiTetR48e1rhxY1dZdM+ePVajRg274447XD9CAAAAAEAWCQgj0biDegAAAAAAsmjKKAAAAAAgxlsIDx06ZPPnz7ctW7bYkSNHIs6ngeoBAAAAwE879h+xkbPXxpv2wazV1qlhZStZKE+W2xm+BoR9+vSxl19+2QWFkQQCAcuWLRsBIQAAAADfHD52wp6etNTGLdhox04E4j33+rSV9s6Pq6xd/QrWt1Uty5srR5bZM74FhC+88IINHDjQcuTIYS1btnQFZAoVKuTX2wMAAABAxGDwtmHzbO6a3RG3kILE0fPW2+odB2xk5wZZJij0LSB8//33LV++fDZr1iyrV6+eX28LAAAAAIl6etLSRIPBYJrv6UnLbFDbc7LEVvWtqMyGDRvcgPMEgwAAAAAyiu37D7s00eQYt2CD62uYFfgWEJYpU8YKFCjg19sBAAAAQJLG/rohQZ/BpGj+sfM3ZImt61tAeNNNN9mMGTPs4MGDfr0lAAAAACRqbpSpoqF+Wb0rS2xZ3wLCfv362VlnnWXXXnutrVy50q+3BQAAAICIDhw57uvrYqaoTPPmzRNMO3nypGslVGBYqVIlK1++vGXPnjAm1bATU6dOTatVAwAAAACnYJ6cvr4uo0mzT6HAL5ITJ07Y6tWr3SMcBYQAAAAAkNYurFLMZv29M9mvu6hqccsK0iwgXLNmTVotGgAAAABSRfsLKtirU/9OVmGZXDmyWfvzK2SJPZBmAaFSQgEAAAAgIytVKK+1q1/eRs+Lvmpou/oVrGShPJYV+FZUBgAAAAAyor6tarvU0Whovr6tallW4VtAOG3aNGvbtq3NmjUr4jwzZ8508+gnAAAAAPghb64cNrJzA+vQoKJLBw1H0/W85tP8WYVvpXHeffdd++GHH2zkyJER56lbt659//33ljdvXmvSpIlfqwYAAAAgxuXNlcMGtT3HerSoYaPmrLXXp/3fUHn3N69unRpWzjJpounSQjhv3jw777zzrFChQhHnOe2006xevXo2d+5cv1YLAAAAAOIo6Lvrkqr/N8HM/T8rBoO+BoRbt261ChWSrsSjebZs2eLLOgEAAABALPMtICxQoIBt27Ytyfm2b9/uUkYBAAAAAFkkIFS66M8//2zr16+POI+eU9GZOnXq+LVaAAAAABCzfAsIO3fubEeOHLFrrrnG5s+fn+B5TWvVqpUdO3bMzQsAAAAAyCJVRjt06GATJkywcePG2YUXXuhaAatVq+aeW7VqlS1atMgCgYC1adPGbr31Vr9WCwAAAABilm8BoXz66af27LPP2pAhQ2zhwoXu4SlSpIh1797dnnjiCT9XCQAAAABilq8BYfbs2a1379722GOPuRTRDRs2xFUWrV+/vuXOndvP1QEAAACAmOZbH8JguXLlsoYNG1r79u3dQ79n1GBQhXCuvvpqK1asmBUsWNAaNGhgo0aNSvZyFixYYP369bNGjRq51lB9XgXCt9xyiy1evDhN1h0AAAAAMkwLYWbz+eef24033mgnT560Jk2aWIkSJWzq1Kl22223uSDupZdeimo5x48ft/PPP9/9rsBSQaGG4fj999/t448/ts8++8z9bNeuXRp/IgAAAABI5xbCzGD37t2u2umJEydcIZwZM2a4n3/++adVr17dBg8e7KZF64ILLrAvvvjCjbP4zTffuCDwr7/+sieffNKOHj3q3mvnzp1p+pkAAAAAIBgBYQQffPCB/fPPP9a6dWtr27Zt3PTSpUvbCy+84H5XUBiNnDlz2rx589yycuTI8X8bP3t2GzBggJ155pm2f/9++/rrr6NaHgAAAACkBgLCCLzgLFwaZ8uWLS1v3rw2ZcoUO3z48CntgGzZstm5557rft+8efMpLQsAAAAAkoOAMAKNiyj16tVL8JwKwpx99tkuGFTa56lavXq1+1mmTJlTXhYAAAAARIuAMAyliu7bt8/9Xr58+bAbzpu+bt06OxU//fSTq0CqIPOqq646pWUBAAAAQIasMnrw4EFXWTMzOHDgQNzv+fPnDzuP91nU9+9UAk8Vk5Hu3btb2bJlo35t7dq1w05ftWqVVatWLcXrBAAAACB2+BYQnn766dahQwe766674oZgSEtt2rSx5cuXJ+s1Gl9Q4wz6QdVLb775Zvv777/de/bv39+X9wUAAAAA3wNCFU9577337P3337c6depYly5drGPHjnbaaaelyfutWbPGVqxYkazXHDp0yP3UAPTB08Kto1o8pVChQilav//+97/21VdfuQqjKmCjlNHkWLp0abJaDgEAAAAg3foQbtmyxYYPH24NGza0hQsXWrdu3VyroVIm58yZk+rvp/cIBALJejRr1sy9VgFg4cKF3e8bN24Mu3xveqVKlZK9bo8//rgLjCtUqGA//PCDG/AeQPrYsf+IvT/zf4WdPB/MWu2mAwAAZHW+BYT58uWz2267zRVRUSrnQw895PrnjRgxwi6++GJXtfO1116zPXv2WEagVkz57bffEjx37NgxW7JkiRt6okaNGslarsYwfP75561UqVIuGFRQCMB/h4+dsF7jF1uj56baG9NXxnvu9Wkr3fRe4/9w8wEAAGRV6VJlVGmSGtR906ZN9umnn1rz5s1dkKjCKuXKlbNbb73VZs2aZelJYw3KuHHjEjynVE8NOXH55Ze7oDBaahV87LHHrEiRIjZ58mS3HQD4T0HebcPm2eh5G+zYiUDYeTR99Lz1bj6CQgAAkFWl67ATuXLlsvbt29tnn31mDz74oEvbVKD18ccfu/RNtdIp+EoPKn6j1NEvv/zSxo8fHzd9+/bt1rNnT/f7ww8/nOB1NWvWdA8Fu8EUWN5zzz2uf+I333xjdevW9eFTAAjn6UlLbe6a3VFtHM339KRlbEgAAJAl+VZUJhy1AqrV7PPPP3eBYJ48eez66693LYZjxoyxKVOmWOvWre2tt96yrl27+rpuxYoVs2HDhrmAtV27di5ALV68uFunvXv3Wo8ePeL6HAbzCtkorTQ4iFRF0ZMnT1qVKlXs3XffdY9Q1113nXsASDvb9x+2cQvC9w2OZNyCDdajRQ0rWShPmq0XAABATASEO3futJEjR9oHH3xgf/31l2sVrF69uqs6escdd7igS1RsZt68eXbFFVfYiy++6HtAKApOZ86caQMHDrRffvnFjh49arVq1bL77rvP9YeMliqV6rXyxx9/uEc4lStXJiAE0tjYXyOniUai+cfO32DdLq2eZusFAACQpQNCtaypNVApmGo9y5EjhxsrUGmU6osXjsbnU1++sWPHWnpp3Lixffvtt1HPrwA3XKAXbjoA/0WbKhrql9W7CAgBAECW41tAqJY+UVXNu+++2/XRK1OmTJKv0/zly5f3YQ0BxIIDR477+joAAICMzLeiMmrpmzhxohswvnfv3lEFg/Lcc8+51wBAaiiYJ6evrwMAAMjIfLvCmTRpkl9vBQARXVilmM36e2eyt9BFVf/XvxkAACArSddhJwDAb+0vqGC5cmRL1ms0f/vzK6TZOgEAAKQX33Og1q9f71oL//77b9u/f3/YYivZsmWzoUOH+r1qAGJAqUJ5rV398m5Q+mi1q1+BIScAAECW5GtA2L9/fxswYIAbj8/jBYQKAr3/ExACSEt9W9W21TsORlVxVCmmfVvVYocAAIAsybeUUQ00369fP1c19L333rMWLVq46ZMnT7a3337bmjZt6oJBDfg+bdo0v1YLQAzKmyuHjezcwDo0qBgxfVTT9bzm0/wAAABZkW8thG+99Zblzp3bpk+fbpUqVbKffvrJTfcCQw08//LLL1vPnj0ZnB1AmlOQN6jtOdajRQ0bNWetvT5tZdxz9zevbp0aViZNFAAAZHm+tRAuXrzYGjVq5ILB0BRRT/fu3e3MM8+0gQMH+rVaAGJcyUJ57K5Lqsabpv9rOgAAQFbnW0B45MiReGMP5s2b1/3cu3dvvPnq1Kljv/76q1+rBQAAAAAxy7eAsGzZsrZ9+/a4/5crV879XLp0abz5Nm7caCdOnPBrtQAAAAAgZvkWEJ5zzjm2YsWKuP83a9bMpYv27dvXDh486KaNHTvWZs2aZbVr1/ZrtQAAAAAgZvkWELZq1co2bdoUV0G0cePGdumll7oiM0WLFrUSJUpYhw4dXN/Cp556yq/VAgAAAICY5VtAeMstt9jy5cutbt26cdMmTJhgXbp0sWLFirlB6mvVqmUffvihXXXVVX6tFgAAAADELN+GnciTJ4+rIBrstNNOs3feecc9AAAAAABZtIUQAAAAAJCxEBACAAAAQIxKs5TRqlXjD/ScHCoss2rVqlRdHwAAAACATwHh2rVrUxQIaigKAAAAAEAmThk9efJkgsd9991nBQsWtMcee8wWLlxoe/fudY9FixbZ448/7p7TPJoXAAAAAJBFqoy+9tpr9u6779rPP/9s559/foJB6/Vo27atNWrUyKWbPvTQQ36tGgAAAADEJN+KyigYbNasWYJgMJiea968ub3//vt+rRYAAAAAxCzfAsLVq1e7AeiTUrRoUVuzZo0v6wQAAAAAscy3gFDB4MyZM+3w4cMR59FzmkdBIQAAAAAgiwSEbdq0sS1btli7du3CViDVtBtuuMG2bt3q5gUAAAAAZJGiMgMGDLBp06bZN998Y2eccYbrL1ipUiX33Lp162zBggV2/Phxq1mzppsXAAAAAJBFAkKlgc6ePdt69eplo0aNsrlz57qHJ1++fNa5c2cbNGgQKaMAAAAAkJUCQilSpIi9/fbbNnjwYNciuHnzZje9bNmyVr9+fStQoICfqwMAAAAAMc3XgNCTP39+u+SSS9LjrQEAAAAAfheVCWfixInWv3//9FwFAAAAAIhZ6RoQTpgwwZ5++un0XAUAAAAAiFnpGhACAAAAANIPASEAAAAAxCgCQgAAAACIUekaEAYCAfcAAAAAAMRYQDhixAg7efJkeq4CAAAAAMQsUkYBAAAAIEb5HhAuW7bMunfvbo0bN7YzzzzTevbsGffc7Nmz7bXXXrPdu3f7vVoAAAAAEHNy+vlmQ4YMsccff9yOHz/u/p8tWzbbuXNnvHkULObJk8e6du3q56oBAAAAQMzxrYXw66+/tkceecQqVKhg48ePt+3btycoKNOoUSMrWbKkffnll36tFgAAAADErJx+tg4WKFDAfvjhB6tatWrE+erWrWsrVqzwa7UAAAAAIGb51kK4YMECu+iiixINBqVEiRK2detWv1YLAAAAAGKWbwHh0aNHrVChQknOp1TSnDl97doIAAAAADHJt4CwSpUqtmjRoiSDxsWLF1uNGjX8Wi0AAAAAiFm+BYTXXnutrV271vUljOSFF16wHTt2WNu2bf1aLQAAAACIWb7lZmq8wY8//tgeffRRmzt3rrVp08ZN37Ztm02YMME99LxaEu+77z6/VgsAAAAAYpZvAWHRokVtypQp1q5dO/vss89s3Lhxbvp3333nHhqColatWvbFF19E1dcQAAAAAHBqfK3eor6BCxcutEmTJtn333/vUkhPnjxp5cuXtxYtWtj1119vOXLk8HOVAAAAACBm+V7OM3v27Na6dWv3AAAAAADEQFEZAAAAAECMtxAeOnTI5s+fb1u2bLEjR45EnK9Tp06+rhcAAAAAxBpfA8I+ffrYyy+/7ILCSFRcJlu2bASEAAAAAJBVAkKNMThw4EBXNKZly5auwAzVRAEAAAAgBgLC999/3/Lly2ezZs2yevXq+fW2AABkOoeOHk/W8/p/rhzZIs6fP7fvPUQAAJmEb38hNmzYYM2bNycYBAAgCbX6TE7WNmo4aFqiz699riXbHACQvlVGy5QpYwUKFPDr7QAAAAAAGaWF8KabbrKhQ4fawYMHCQwBAEjEsv5Xsn0AAFkrIOzXr5/Nnj3brr32Wnv33XetevXqfr01AACZCn3+AABZLiC8+uqr7eTJkzZjxgw766yzrFKlSla+fHnLnj1h1qqGnZg6dapfqwYAAAAAMcm3gFCBoOfEiRO2evVq9whHASEAAAAAIIsEhGvWrPHrrQAAAAAAGSkgVIooAAAAACAGh50AAAAAAMRoC6Fn27ZtNmzYMJs1a5Zt2rTJTStXrpw1adLE7rjjDitdurTfqwQAAAAAMcnXgPDzzz+3zp0724EDBywQCMRN/+OPP2zy5Mn23HPPubEKr7/+ej9XCwAAAABikm8po/Pnz7cOHTq4genbtGljEyZMsN9//90WLlxoX3zxhbVt29YFih07dnTzAgAAAACySAvhoEGD3HAT48aNcwFhsHPPPdcNWK8gUa2DainUfAAAAACALNBC+NNPP1mjRo0SBIPB9Fzjxo1d/0IAAAAAQBZpIdy3b59VrFgxyfk0z6+//urLOiFlDh09nqzn9f9cObJFnD9/bt9rGwEAAADwMyAsU6aM6zOYFPUp1LzIuGr1mZys+RsOmpbo82ufa3mKawQAAAAgQ6eMXnnllbZixQp74oknXF/CUKo62rt3b/vzzz/tqquu8mu1AAAAACBm+dZC+NRTT9n48ePt+eeft9GjR1v79u2tcuXK7rl169bZZ599ZmvXrrXixYu7wBAZ17L+V6b3KgAAAADITAFh+fLlbdq0aXbzzTfbkiVL7MUXX7Rs2f7Xr8wbk/Ccc86xjz/+2M2LjIs+fwAAAEDW4Gs1DwV8ixcvthkzZrhKops3b3bTTz/9dLvkkkusWbNmfq4OAAAAAMS0dCnvqMAvswR/P//8sz3zzDP2yy+/2NGjR61WrVp23333WadOnU552XfeeacNGzbM/a4A+eKLL06FNQYAAACA6FDvPxGff/653XjjjXby5Elr0qSJlShRwqZOnWq33Xaba+l86aWXLKWmT5/ugkGlzXopswAAAACQJauMZja7d++2zp07u4qo48aNc2mu+qkqqNWrV7fBgwe7aSlx+PBh69q1q9WuXdsaNmyY6usOAAAAANEgIIzggw8+sH/++cdat25tbdu2jZteunRpe+GFF9zvCgpTYsCAAbZy5Up75513LFeuXClaBgAAAACcKgLCCL7++mv3s127dgmea9mypeXNm9emTJniWvuS448//nAVVtX6SJ9BAAAAAOmJgDCCRYsWuZ/16tVL8Fzu3Lnt7LPPdsHgX3/9FfXGVl/ELl26WJEiReJaGQEAAAAgvRAQhqFU0X379rnfI42J6E1ft25d1Bv7zTffdNVKVYymWLFiKdtjAAAAAJBKqDIaxoEDB+J+z58/f9gNV6BAAfdz//79UW3ojRs32pNPPumG20iNIStUkCacVatWWbVq1U55+QAAAACyviwbELZp08aWL1+erNeMGjXKGjRokCbr061bNzty5Ii9/fbbabJ8AAAAAMjQAeH27dvtrbfespkzZ9qWLVtcgBSOxuZTS9epWLNmja1YsSJZrzl06JD7WbBgwXjTTjvttATzHjx40P0sVKhQVOMZTpw40Z566imrWbOmpYalS5cmq+UQAAAAANItIFRrXdOmTW3Xrl2+DMS+cOHCFL9WAWDhwoVdP0KletaqVSvBPJoulSpVSnJ5kyZNcj9/+OEHFwyHW8/777/fveftt9/uHgAAAACQZQLCRx991Hbu3GnXX3+99erVy2rUqBGvJS6jqVOnjgvefvvttwQB4bFjx2zJkiVu6Al9jmipoEwkXmCoPoYAAAAAkKWqjM6aNcvOPPNMGzt2rBvKISMHg95YgzJu3LgEz3311VduyInLL7/cBYVJGTFihGsVDfdQq6m3ffT/fv36pcGnAQAAAIB0DAgV7NStW9f1D8wM7rrrLpc6+uWXX9r48ePj9YPs2bOn+/3hhx9O8Dr1EdRj06ZNvq4vAAAAAGTYlNHzzz8/WWP2pTeNEzhs2DBr3769tWvXzqVyFi9e3KZMmWJ79+61Hj16hE3v9ArZKK0UAAAAADIy31oIlQr566+/xhVYyQzU31H9CK+88kr7/fff7ZtvvrHq1au7FNDBgwen9+oBAAAAQOYZduLBBx+0tm3bWseOHa1FixZWvnx5y549fEzapEkTywgaN25s3377bdTzJ7eC6owZM1KwVgAAAACQiQJCpVeq/6ACpg8//NA++uijROc/ceKEX6sGAAAAADHJt4CwU6dOmaagDAAAAKJ36OjxZD2v/+fKEfm6MH9uX5PYgJjm27dN/e4AAACQ9dTqMzlZ8zccNC3R59c+97/hvwBkoaIyAAAAAICMhfZ4AAAAnJJl/a9kCwKZlO8B4fr1693QE3///bft378/bFVO9TUcOnSo36sGAACAFKDPH5B5+RoQ9u/f3wYMGGAnT56Mm+YFhF7BGf2fgBAAAAAAslAfwjFjxrjB6StUqGDvvfeeG4dQJk+ebG+//bY1bdrUBYM9evSwadMS72gMAAAAAMhELYRvvfWW5c6d26ZPn26VKlWyn376yU33AsOuXbvayy+/bD179rTrrrvOr9UCAAAAgJjlWwvh4sWLrVGjRi4YDE0R9XTv3t3OPPNMGzhwoF+rBQAAAAAxy7eA8MiRI1amTJm4/+fNm9f93Lt3b7z56tSpY7/++qtfqwUAAAAAMcu3gLBs2bK2ffv2uP+XK1fO/Vy6dGm8+TZu3GgnTpzwa7UAAAAAIGb5FhCec845tmLFirj/N2vWzKWL9u3b1w4ePOimjR071mbNmmW1a9f2a7UAAAAAIGb5FhC2atXKNm3aFFdBtHHjxnbppZe6IjNFixa1EiVKWIcOHVzfwqeeesqv1QIAAACAmOVbQHjLLbfY8uXLrW7dunHTJkyYYF26dLFixYq5Qepr1aplH374oV111VV+rRYAAAAAxCzfhp3IkyePqyAa7LTTTrN33nnHPQAAAAAAWbSFEAAAAAAQoy2EnuPHj9vXX39t8+bNs507d9qFF15onTt3ds9t3rzZTVPqaM6cvq8aAAAAAMQUX6Oun376yfUl3LBhg6swqgIyx44diwsI58yZY+3bt7fPPvvM2rZt6+eqAQAAAEDM8S1ldNmyZa5YzJYtW+z+++93Q0woKAytRJo/f377/PPP/VotAAAAAIhZvrUQDhgwwA4fPmzffPONXXHFFWHnyZ07t9WrV89+//13v1YLAAAAAGKWby2EGm+wQYMGEYNBT7ly5VxfQgAAAABAFgkI9+7daxUqVEhyvoMHD7p+hQAAAACALBIQlipVylauXJnkfBq8PprAEQAAAACQSQLC5s2b28KFC13qaCQTJkxwQWOLFi38Wi0AAAAAiFm+BYSPP/64Kxpz3XXX2dtvv21bt26Ne27Pnj02bNgwu/POO61AgQLWo0cPv1YLAAAAAGKWbwFhzZo1bfTo0Xby5Em77777XPEYjUM4cuRIK1GihN1999125MgR+/jjj61KlSp+rRYAAAAAxCzfAkJR6+CSJUvcOIQKEPPmzetaDatWrWpdu3a1xYsX27XXXuvnKgEAAABAzPJtHEJPpUqV7JVXXvH7bQEAAAAA6dlCCAAAAADIOAgIAQAAACBGpVnKqPoFppSKzaxatSpV1wcAAAAA4FNAuHbtWhfYBQKBZL9WrwMAAAAAZPKiMvXr17dbbrnFWrdubfny5UvrtwMAAAAApHdA+Omnn7oxBb/77js30HyfPn2sbdu2Ljhs3rw5rYAAAAAAkFWLyrRv396+/PJL27Jli73++utWu3ZtNwj9FVdcYRUqVLBHH33UFi5cmFZvDwAAAABI7yqjxYoVs3vvvddmz57tCsX069fPChUqZIMHD3bppGeffbY9//zztmHDhrReFQAAAABAeg07UaVKFXvqqads+fLlNnfuXLv//vtt165d9sQTT9gFF1zg56oAAAAAQMxLt3EIK1Wq5IamOP30010l0pMnT8b8zgAAAACALFVlNNihQ4ds/PjxrtjM1KlT7cSJE1a4cGG7++677dZbb/VzVQAAAAAg5qV5QKiWv8mTJ9tHH31kEydOdEFh7ty57dprr3UVR6+++mr3fwAAAABAFgkI1UdQLYFjxoyxHTt2uGEmmjRp4oLAdu3auZZBAAAAAEAWDAgbNmzogsBzzjnHHnnkEevYsaOVK1curd4OAAAAAJDRUkaXLVtmTz75pHtES4HkkSNH0nS9AAAAAMSmQ0ePJ+v5Q0ePW64c2SLOnz+3r6VZUlWarrmqhx4/nvjGBgAAAAA/1eozOVnzNxw0LdHn1z7X0jKrNAsIGUYCAAAAADK2zNu2CQAAAAApsKz/lWy3/4+AEAAAAEBMycx9/lJb9lRfIgAAAAAgUyAgBAAAAIAYRUAIAAAAADGKgBAAAAAAYhQBIQAAAADEKAJCAAAAAIhRBIQAAAAAEKMICAEAAAAgRhEQAgAAAECMIiAEAAAAgBhFQAgAAAAAMYqAEAAAAABiFAEhAAAAAMQoAkIAAAAAiFEEhAAAAAAQowgIAQAAACBGERACAAAAQIzKmd4rAABp7dDR48l6Xv/PlSNbxPnz5+bUCQAAsgauagBkebX6TE7W/A0HTUv0+bXPtTzFNQIAAMgYSBkFAAAAgBhFCyGALG9Z/yvTexUAAAAyJAJCAFkeff4AAADCI2UUAAAAAGIUASEAAAAAxCgCQgAAAACIUQSESfj555/t6quvtmLFilnBggWtQYMGNmrUqBRv8EAgYCNGjLAmTZq4ZebLl8+qVq1qHTt2tKVLl6Z4uQAAAACQXBSVScTnn39uN954o508edIFcCVKlLCpU6fabbfdZosXL7aXXnopWRv78OHD1qZNG/vuu+9cMNi4cWMXEK5evdrGjh3rAs/atWsneycCAAAAQEoQEEawe/du69y5s504ccIFhm3btnXTt23bZhdffLENHjzYrrnmGmvWrFnUG/uee+5xweDdd99tr776qgsGPVu2bLFjx46laCcCAAAAQEqQMhrBBx98YP/884+1bt06LhiU0qVL2wsvvOB+V1AYrXnz5tnIkSNdyum7774bLxiUsmXLWsWKFVO0EwEAAAAgJQgII/j666/dz3bt2iV4rmXLlpY3b16bMmWKSwONxvvvv+9+3nfffZYtW7YU7SwAAAAASE2kjEawaNEi97NevXoJnsudO7edffbZNn/+fPvrr7/s3HPPTXJDT5s2zf1s1KiRrVq1ykaPHm0bNmywkiVL2lVXXeXSUAEAAADATwSEYShVdN++fe738uXLh91wmq6AcN26dUkGhGpFVOEYLzC8//777ciRI3HPP/PMM654jaqXKtgEAAAAAD+QMhrGgQMH4n7Pnz9/2A1XoEAB93P//v1JbuS9e/fG/X7vvfe6lNPly5e76ePHj3fVS8eMGWNPPvlk1DtO1UjDPdT6CAAAAAAx3UKo4R0UdCWHWuhU9CW1adgKT82aNe2zzz6z7Nmzx61nnjx5XJD4xhtv2FNPPWWnnXZaqq8DAAAAAMRMQLhmzRpbsWJFsl5z6NAh91MD0AdPCxegHTx40P0sVKhQkssNXl6nTp3igkGPxh8sVaqUbd++3VUjvfzyy5NcZqRB7BnHEAAAAIDFesrowoULLRAIJOvhjSmoALBw4cLu940bN4Zdvje9UqVKSa6Llle0aFH3e+XKlcPO401XUAgAAAAAfsiyAeGpqlOnjvv522+/JXhOA8gvWbLEDT1Ro0aNqJZXt25d93PPnj1hn9+9e3eC1kQAAAAASEsEhBGoT5+MGzcuwXNfffWVqxyq1E4FhdG49tpr3c8ZM2YkeG79+vW2du1a9/t5550X/d4DAAAAgFNAQBjBXXfd5VI9v/zyS1cJ1KOUzp49e7rfH3744QSvU9EYPTZt2hRveufOneOqiU6cODFeH0VVHj1+/LjrS1ihQoVT2Z8AAAAAELVsAXWeQ1iff/65tW/fPq5/YfHixW3KlCluuIgePXrY4MGDE27QbNniitqE9hecPHmytWrVygV/F154oZUtW9bmzp1rmzdvdvP+9NNPVq5cuVPaGypyo5TWatWqsVcBAACALGD9+vVWsWLFiIUlTwUthIm4/vrrbebMmXbllVfa77//bt98841Vr17dRowYETYYTIqW8+uvv7qhJlauXOlST5Vy2r17dzf9VINBb3zEXLlyWXrTeIiMiYjMhGMWmQ3HLDIbjllkNqsy0PWsrvHTqsGHFkKkCW/4i7S4iwGkBY5ZZDYcs8hsOGaR2dSOketZWggBAAAAIEYREAIAAABAjCIgBAAAAIAYRUAIAAAAADGKgBAAAAAAYhRVRgEAAAAgRtFCCAAAAAAxioAQAAAAAGIUASEAAAAAxCgCQgAAAACIUQSEAAAAABCjCAgBAAAAIEYRECKebNmyxT3mzJkTceuMHTs2br7KlSuzFZFhjttoHhyz8Nvvv//ujr1y5cqFff7kyZNWpEgRN0+XLl3CzjNz5kz3fO3atVN9/WbMmOGWffvtt6f6shGb51eOKWRkSR3HzZo1i3pZOub1mswsZ3qvADKujz/+2Bo2bBj2uY8++sj39QEiue222xJM++mnn2zVqlVWp04dq1u3brznSpQowcaEr3QcnnbaabZ582ZbvXq1Va1aNd7zf/zxh+3bty/u2A1n1qxZ7ucll1ziwxoD/8P5FbF2fEvNmjUtlhAQIoEcOXJYrVq1bMyYMfbKK69YzpzxD5Ndu3bZd999Z/Xq1bPffvuNLYh0N2LEiATT1NKhgPC6666zfv36pct6AZ7s2bNbo0aN3LlTAV9oQOgFewocFy9e7M6zxYsXDzsPASH8xPkVsXZ8J9fUqVPt2LFjlpmRMoqwbr75Ztu5c6dNnjw5wXMKFHXg33LLLWw9AIiSF8iFawHUtFy5ctlDDz1kgUDAfv755wQppV4aPwEhAGQc1apVy/QtigSECKtjx44uHzpcaqimFSxY0Fq3bp3o1vvmm2+sRYsWVrRoUcubN6+deeaZ9vjjj9vevXsTzKsWHL2f7tQoderaa691rytQoIA1bdrUZs+ezZ5Cqgg+1pLbF2D58uWu5bFChQqWJ08eK126tN100022dOlS9g6S5AVyXktfME1T1oXOmeHmWbRokf3zzz9WsWJF95Djx4/b22+/7VL7lY6aL18+lx6tzA49F46OVbWa6/xaqFAht05qtQTS0u7du+2///2vlS1b1p07zz77bBs2bFiC+ZLqd6jpel7zAX64PeiYUyPJpZdeGtff27uezQp9CAkIEZYueJs0aWITJ060AwcOxE1X3xfdpW7Tpo3lz58/4tYbNGiQtWzZ0n2B6tev7y5ADh06ZM8//7xdeOGFtm3btrCvmz9/vl100UW2du1au/LKK+2MM85whRQuu+wyW7JkCXsL6eaLL76w8847z0aOHOn6IOqmRZUqVVyBpQYNGrjjFEiMjhNdDK9YscJlYASfV9W38OKLL3ZFZypVqpSgFTE0XfTff/+1K664wu69917766+/3HlTweSWLVuse/fudv3117tWxXDn1y+//NLKly9v11xzjVvO1VdfbZ999hk7D2lCF826aaHrCR2/jRs3tj///NPuvPNO++CDD9jqyBQ++eQT+89//mMHDx50Py+44IJMHwQGIyBEREoJVRA3fvz4eIVmvOci+fXXX613796uFVEXNVOmTLFPP/3UVq5caTfccIO7eOnWrVvY17755psumFQfGr1m4cKFLoXq8OHD9sILL7C3kC50g0LHvFL6fvjhB1cxUhfQv/zyi2sJ91Kojx49yh5CRAoGdRERmhLqBX8KCEUXzOqfrWAtdB4vIHzkkUds+vTpduONN7qA8vvvv3cX3DrPKsDT7++9917c6/WeKp6gG3x9+vRxmRijR492QeIbb7xhb731FnsOaUI3INT6reNUN9CmTZtm48aNc88NGDCArY5M4f3333fnzHnz5sX9LFy4sGUVBISIqF27du4CxgsCRb8r5UMtdpHo4kJ3pu+//37XGujRsvSc0pomTJhgGzZsSPBaXQg98MAD8aYpuBRaYJBelIKnu4K6WXH55ZfHe+6qq65yqVA6nr/++ut0W0dk3rRR73ed/7yfurkwd+7csAHh9u3b3cWJMjmGDx8e76JEaaBDhw613Llzu3RSj7I1li1b5orZKCAMplbG4HM1kJqUzqy//boG8ChrSGmj69evdzfcgPQSadiJtSHHpbLedAMuqyIgRETKkdYXQNWTtm7d6lr+lOqkPlOqRBqJd3GjwjShSpUq5dKcFDCGFk0QPRdKlfaKFSvmUqGA9KDWF2nbtm2iF/m6Ywgkt7CMfq9Ro4aVLFkyXmDozaNquTr/6Vx41llnueBOrdK6GaEbbKHKlCnj0u3VCui1MnrnZd3oC3f+7tChAzsOaULdRkIr5oqOeeFvO9KTMiduC/NQllswdRPJyhh2AolSGpxSRpW+uWbNmrhpiVFfGIk0+Lc3fdOmTQmeU7+WcHTXW53SgfTg3SmMNKi4J7hfGBCOhp7QEBReSqhSONWfqnPnznHznHPOOa5VxQsIvWBOKaXBd67VSqhHYnTe1HHrnZfVPzGcSOdr4FQl9nddjhw5wkZGhh92ouL/L+aVVREQIlHqi6KWwlGjRrkLCt2dVl+AU5FYJ1xdKAHpKbQQR/C0SAPYeki7Q1KU3nnuuee6/tHqg+pVqfP6D3rnQRV/UQGvEydOJCgo4x2PqiiqcQsTE5ymB6SH1Py7Hu78DPghb968WXpDExAiyYsJFYLx7kKH9u8L5/TTT3etievWrXMD3Ke0tQVIC+pbJcHVcz26+FZ6dLg73ErbGzx4cNjUJyA5FNgpIFQLYLiA0EsbVaqyCmyFFpTxWlz0mtdffz2q91Tfb9F5OZxI04GMcn6WcLUHAJw6mmOQpFtvvdVdBKvUfrh+gaG8ixZVYQq1Y8cON46LWgm9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> out/state/checks.md <<'EOF'\n# Verification checks (P4)\nClean re-run: deleted results.json/table1.csv/figure1.png/exclusions.csv, re-ran analysis.py → results.json byte-identical (cmp). PASS\n- n_train+n_test==n_analysed (2500+2501=5001): PASS\n- table1 n sums to n_analysed: PASS\n- exclusions: 5217 − (215+1) = 5001 = n_analysed: PASS\n- n_missing_rows independently recomputed (pandas NaN count = 215): PASS\n- CI contains estimate: PASS\n- All SEs positive: PASS\n- Monday contrast recomputed from table1 (n-weighted Tue–Fri mean): PASS\n- ANOVA p in [0,1]: PASS\n- conclusion consistent with ANOVA p ≥ 0.05 rule: PASS\n- RMSE magnitude plausible vs. sd of daily changes (~5 bp): PASS\n- Baseline RMSE independently recomputed: PASS\n- Information rule: first evaluation change dated 2010-01-04; weekday means use train only (code: groupby on train): PASS\n- Figure visually inspected: five weekday means with 95% bars, all covering zero: PASS\n- Assumption check: Levene test rejects equal variances (p≈1e-7) → classical ANOVA assumption violated; Welch ANOVA (p≈0.90) and Kruskal–Wallis (p≈0.52) agree with classical result. Reported, not a failure of the computation.\nEOF\necho \"- 2026-10-30 P2/P3 done (baseline zero-change RMSE in same script); P4 checks all pass.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | tail -30","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":1251,"cache_read_input_tokens":17742,"output_tokens":784,"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":1251},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXru3Jn7zZmVM75ceH9r","requestRef":"1cc7fb67-f7bd-4ce7-9e7f-837eb92b80cb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2f42e9db-95f2-4e29-af7c-4c7466c90a7f","timestamp":"2026-10-30T05:22:10.271Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0276","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","version":"2.1.314","gitBranch":"HEAD"}60{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"2f42e9db-95f2-4e29-af7c-4c7466c90a7f","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b"}61{"type":"atis-latch","atis":"","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b"}62{"parentUuid":"2f42e9db-95f2-4e29-af7c-4c7466c90a7f","isSidechain":false,"promptId":"ed6567c1-cc92-485e-be39-4424a6b85424","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GWWtbBFFx1fP8oGUxc8SQP","type":"tool_result","content":"  [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  [ok] key:n_missing_rows: ok\n  [ok] key:n_train: ok\n  [ok] key:n_test: ok\n  [ok] key:anova_f_stat: ok\n  [ok] key:anova_p_value: ok\n  [ok] key:effect_estimate: ok\n  [ok] key:effect_ci_low: ok\n  [ok] key:effect_ci_high: ok\n  [ok] key:baseline_metric: ok\n  [ok] key:model_metric: ok\n  [ok] key:conclusion: 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: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] ci:effect_estimate: -0.3193357757 <= 0.0501700984 <= 0.4196759725\n  [ok] positive:anova_f_stat: 0.2634657168\n  [ok] positive:baseline_metric: 4.8757041546\n  [ok] positive:model_metric: 4.8838415223\n  [ok] reproducibility: regenerated in 0.8s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0276/out/state/gate_receipts/20261030T052211.131123Z.json","is_error":false}]},"uuid":"fb76126d-4bfd-4d29-873e-5dff522d2a5c","timestamp":"2026-10-30T05:22:11.137Z","toolUseResult":{"stdout":"  [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  [ok] key:n_missing_rows: ok\n  [ok] key:n_train: ok\n  [ok] key:n_test: ok\n  [ok] key:anova_f_stat: ok\n  [ok] key:anova_p_value: ok\n  [ok] key:effect_estimate: ok\n  [ok] key:effect_ci_low: ok\n  [ok] key:effect_ci_high: ok\n  [ok] key:baseline_metric: ok\n  [ok] key:model_metric: ok\n  [ok] key:conclusion: 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: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] ci:effect_estimate: -0.3193357757 <= 0.0501700984 <= 0.4196759725\n  [ok] positive:anova_f_stat: 0.2634657168\n  [ok] positive:baseline_metric: 4.8757041546\n  [ok] positive:model_metric: 4.8838415223\n  [ok] reproducibility: regenerated in 0.8s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0276/out/state/gate_receipts/20261030T052211.131123Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"2f42e9db-95f2-4e29-af7c-4c7466c90a7f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0276","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","version":"2.1.314","gitBranch":"HEAD"}63{"type":"api-request-blob","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","hash":"f9b0f3cc2d874744e5f6685561f27d06aac014b9dde282c482206e166ef52e85","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01XuiiHfHat1fughs7p251y7","name":"Read","input":{"file_path":"/Users/simon-pierreboucher/wp29/runs/RC0276/out/figure1.png"}},{"type":"tool_use","id":"toolu_01GWWtbBFFx1fP8oGUxc8SQP","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\nClean re-run: deleted results.json/table1.csv/figure1.png/exclusions.csv, re-ran analysis.py → results.json byte-identical (cmp). PASS\n- n_train+n_test==n_analysed (2500+2501=5001): PASS\n- table1 n sums to n_analysed: PASS\n- exclusions: 5217 − (215+1) = 5001 = n_analysed: PASS\n- n_missing_rows independently recomputed (pandas NaN count = 215): PASS\n- CI contains estimate: PASS\n- All SEs positive: PASS\n- Monday contrast recomputed from table1 (n-weighted Tue–Fri mean): PASS\n- ANOVA p in [0,1]: PASS\n- conclusion consistent with ANOVA p ≥ 0.05 rule: PASS\n- RMSE magnitude plausible vs. sd of daily changes (~5 bp): PASS\n- Baseline RMSE independently recomputed: PASS\n- Information rule: first evaluation change dated 2010-01-04; weekday means use train only (code: groupby on train): PASS\n- Figure visually inspected: five weekday means with 95% bars, all covering zero: PASS\n- Assumption check: Levene test rejects equal variances (p≈1e-7) → classical ANOVA assumption violated; Welch ANOVA (p≈0.90) and Kruskal–Wallis (p≈0.52) agree with classical result. Reported, not a failure of the computation.\nEOF\necho \"- 2026-10-30 P2/P3 done (baseline zero-change RMSE in same script); P4 checks all pass.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | tail -30","description":"Record checks and run 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","media_type":"image/png"}}]},{"tool_use_id":"toolu_01GWWtbBFFx1fP8oGUxc8SQP","type":"tool_result","content":"  [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  [ok] key:n_missing_rows: ok\n  [ok] key:n_train: ok\n  [ok] key:n_test: ok\n  [ok] key:anova_f_stat: ok\n  [ok] key:anova_p_value: ok\n  [ok] key:effect_estimate: ok\n  [ok] key:effect_ci_low: ok\n  [ok] key:effect_ci_high: ok\n  [ok] key:baseline_metric: ok\n  [ok] key:model_metric: ok\n  [ok] key:conclusion: 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: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] ci:effect_estimate: -0.3193357757 <= 0.0501700984 <= 0.4196759725\n  [ok] positive:anova_f_stat: 0.2634657168\n  [ok] positive:baseline_metric: 4.8757041546\n  [ok] positive:model_metric: 4.8838415223\n  [ok] reproducibility: regenerated in 0.8s; identical within tolerance\nreceipt: 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failures (report/claims not yet written; phases not terminal). Contract/data/reproducibility checks pass. Now the report and claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2218,"cache_read_input_tokens":18993,"output_tokens":3882,"output_tokens_details":{"thinking_tokens":36},"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":2218},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrufj2cYGxKwsN7gtHB","requestRef":"95d583db-e275-4cc5-bbe7-41c45174c9b3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f2495e84-a89d-470c-935d-d24e034abe8b","timestamp":"2026-10-30T05:22:13.183Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0276","sessionId":"58fcbed8-4efc-4e3c-b2ba-dc6973a56a2b","version":"2.1.314","gitBranch":"HEAD"}70{"parentUuid":"f2495e84-a89d-470c-935d-d24e034abe8b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrufzeeKiqwUGTmxvqJ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FSDk1W55W6qRbKseFXk1Tx","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F6-conf-01 — Weekday effects in daily changes of the 10-year Treasury yield\n\n## Question\nDoes the day of the week carry predictive content for the one-day change Δy_t (bp) of the 10-year US Treasury constant-maturity yield (FRED DGS10), 2000–2019? Three estimands: (i) joint equality of the five weekday means (classical one-way ANOVA, all changes); (ii) the Monday contrast (mean Monday change − mean Tue–Fri change) with a 95% interval; (iii) out-of-sample RMSE (2010–2019) of weekday-specific means estimated on 2000–2009, compared with a zero-change (random-walk) forecast.\n\n## Data and exclusions\nSingle input `data/DGS10.csv` (SHA-256 verified against the README), 5217 rows (`n_input_rows`), every weekday from 2000-01-03 to 2019-12-31, no duplicate dates. 215 rows have an empty yield field (US market holidays; `n_missing_rows`) and were dropped before differencing, as the task requires. Changes were then taken between consecutive available observations; the first observation has no change. That leaves 5001 changes (`n_analysed`): 2500 in the estimation period ≤2009-12-31 (`n_train`) and 2501 in the evaluation period ≥2010-01-01 (`n_test`). The exclusion ledger (`out/exclusions.csv`) adds up: 5217 − 215 − 1 = 5001. Data issues found and how each was handled are listed in `data_issues`: holiday gaps, multi-day post-holiday changes, heavy tails (−51 bp on 2009-03-18), and 1 bp discreteness. No extreme value was removed. Full audit: `out/state/data_audit.md`.\n\n## Method\n- Joint test: `scipy.stats.f_oneway` (equal-variance one-way ANOVA, F(4, 4996)) on all 5001 changes, grouped by the weekday of t.\n- Monday contrast: difference of sample means, all changes. The 95% interval is a **Welch** two-sample t interval (Monday n=919 vs Tue–Fri n=4082), chosen because it does not assume equal variances. As a sensitivity check, an OLS regression on a Monday dummy with Newey–West (HAC, 5 lags) SE.\n- Out-of-sample: each evaluation-period change is forecast by the estimation-period mean change for its weekday (`train_weekday_means_bp`). The baseline forecast is zero. Both are scored by RMSE over the 2501 evaluation changes. A Diebold–Mariano-type test on the squared-error differential (HAC, 5 lags) is supplementary.\n- Decision rule: `conclusion` comes from the ANOVA at the 5% level. If the ANOVA rejected, the sign would come from the Monday interval.\n- Sensitivity checks on the ANOVA: Welch ANOVA, Kruskal–Wallis, and a Levene (median) test of equal variances.\n- No randomness is involved (`random_seed` = null). `out/analysis.py` regenerates every output.\n\n## Results\n- Weekday means (table1.csv, figure1.png) range from −0.22 bp (Tuesday) to +0.02 bp (Wednesday). Each 95% interval covers zero.\n- ANOVA: F = 0.263 (`anova_f_stat`), p = 0.902 (`anova_p_value`). Equality of the weekday means is not rejected.\n- Monday contrast: +0.050 bp (`effect_estimate`), 95% Welch CI [−0.319, +0.420] bp (`effect_ci_low`, `effect_ci_high`), p = 0.79 (`effect_p_welch`).\n- Out-of-sample: zero-change RMSE 4.876 bp (`baseline_metric`), weekday-mean RMSE 4.884 bp (`model_metric`), a ratio of 1.0017 (`oos_rmse_ratio_model_over_baseline`). The weekday model is slightly *worse* than the random walk. The DM statistic is t = 1.89, p = 0.058 (`oos_dm_t`, `oos_dm_p`), so the deterioration is borderline and not significant at 5%. Using the overall training mean instead gives an RMSE of 4.875 bp (`oos_rmse_unconditional_train_mean`).\n- **Conclusion: `no_detectable_effect`** (`conclusion`). These data show no evidence that the weekday predicts daily 10-year yield changes, and weekday-specific means do not beat a zero forecast out of sample.\n\n## Uncertainty\n- The Welch SE of the Monday contrast is 0.188 bp (`effect_se_welch`). The HAC SE is 0.194 bp (`sens_hac_effect_se`, p = 0.80, `sens_hac_effect_p`), so allowing for autocorrelation and heteroskedasticity barely changes the inference.\n- The CI half-width is about 0.37 bp. A Monday effect larger than about ±0.4 bp/day is therefore inconsistent with these data at the 95% level. Smaller effects cannot be ruled out, but they would be tiny next to the ~5 bp daily standard deviation.\n- The equal-variance assumption of the classical ANOVA is violated: Levene p ≈ 1e-7 (`sens_levene_p`). The conclusion holds anyway under Welch ANOVA (p = 0.902, `sens_welch_anova_p`) and Kruskal–Wallis (p = 0.52, `sens_kruskal_p`).\n- The OOS comparison rests on a single split, with no resampling-based interval for the RMSE difference beyond the DM test.\n\n## Limitations and open issues\n- **What would invalidate the result.** Strong dependence or volatility clustering beyond what 5-lag HAC captures could make the SEs too small. Since the effect is null, though, too-small SEs would only make it *easier* to find an effect, so the null conclusion is conservative in that direction. Changes in the weekday pattern over time (e.g., FOMC calendars, the 2008–09 crisis, QE) could hide period-specific effects that cancel in the pooled 20-year sample. No subperiod tests were run beyond the train/test split.\n- **Measurement convention.** Post-holiday changes span more than one trading day; for example, Tuesday after a Monday holiday captures Fri→Tue. This inflates the variance of some weekday groups (one reason for the Levene rejection) and slightly mixes weekday attributions. I followed the task rule and did not adjust for it. DGS10 is an end-of-day constant-maturity interpolated yield, not a traded price, and is discretised at 1 bp.\n- **Not checked.** Event-day effects such as FOMC days or payroll Fridays, intraday timing, other maturities, and any multiple-testing adjustment across folklore hypotheses (only the Monday contrast was tested specifically). The classical ANOVA is the mandated test even though its equal-variance assumption fails; robust alternatives agree with it.\n- **Main threats.** The power to detect sub-0.4 bp effects is limited. A single OOS split can be sensitive to regime (2010–2019 is a low-rate era). Heavy tails make RMSE sensitive to a few large days.\n- No review issues have been received yet.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The classical one-way ANOVA does not reject equal weekday means of daily 10y yield changes (F=0.263, p=0.902).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Equal-variance assumption violated (Levene); Welch ANOVA and Kruskal-Wallis agree.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.050 bp with 95% Welch CI [-0.319, 0.420] bp, i.e. not distinguishable from zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"effect_p_welch\"],\"status\":\"supported\",\"caveat\":\"Effects smaller than ~0.4 bp/day cannot be excluded.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific estimation-period means do not improve out-of-sample forecasts over a zero-change forecast (RMSE 4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"oos_rmse_ratio_model_over_baseline\",\"oos_dm_p\"],\"status\":\"supported\",\"caveat\":\"Single train/test split; the small deterioration is not significant at 5% (DM p=0.058).\"},\n {\"id\":\"C4\",\"text\":\"Overall conclusion code: no_detectable_effect.\",\"results_keys\":[\"conclusion\",\"anova_p_value\"],\"status\":\"supported\",\"caveat\":\"Decided by the ANOVA at 5% as the task prescribes.\"},\n {\"id\":\"C5\",\"text\":\"The null result is robust to unequal variances, non-normality and autocorrelation in the Monday contrast SE.\",\"results_keys\":[\"sens_welch_anova_p\",\"sens_kruskal_p\",\"sens_levene_p\",\"sens_hac_effect_p\"],\"status\":\"supported\",\"caveat\":\"HAC with 5 lags only; no subperiod analysis.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np={\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches README\",\"schema/date/duplicate/missing audit in data_audit.md\",\"exclusions.csv sums 5217-216=5001\"],\"notes\":\"only rule-mandated exclusions\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"zero-change RMSE baseline_metric=4.8757 computed and independently recomputed\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, no seed needed; 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phases not terminal). Contract/data/reproducibility checks pass. Now the report and claims."},{"type":"tool_use","id":"toolu_01FSDk1W55W6qRbKseFXk1Tx","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F6-conf-01 — Weekday effects in daily changes of the 10-year Treasury yield\n\n## Question\nDoes the day of the week carry predictive content for the one-day change Δy_t (bp) of the 10-year US Treasury constant-maturity yield (FRED DGS10), 2000–2019? Three estimands: (i) joint equality of the five weekday means (classical one-way ANOVA, all changes); (ii) the Monday contrast (mean Monday change − mean Tue–Fri change) with a 95% interval; (iii) out-of-sample RMSE (2010–2019) of weekday-specific means estimated on 2000–2009, compared with a zero-change (random-walk) forecast.\n\n## Data and exclusions\nSingle input `data/DGS10.csv` (SHA-256 verified against the README), 5217 rows (`n_input_rows`), every weekday from 2000-01-03 to 2019-12-31, no duplicate dates. 215 rows have an empty yield field (US market holidays; `n_missing_rows`) and were dropped before differencing, as the task requires. Changes were then taken between consecutive available observations; the first observation has no change. That leaves 5001 changes (`n_analysed`): 2500 in the estimation period ≤2009-12-31 (`n_train`) and 2501 in the evaluation period ≥2010-01-01 (`n_test`). The exclusion ledger (`out/exclusions.csv`) adds up: 5217 − 215 − 1 = 5001. Data issues found and how each was handled are listed in `data_issues`: holiday gaps, multi-day post-holiday changes, heavy tails (−51 bp on 2009-03-18), and 1 bp discreteness. No extreme value was removed. Full audit: `out/state/data_audit.md`.\n\n## Method\n- Joint test: `scipy.stats.f_oneway` (equal-variance one-way ANOVA, F(4, 4996)) on all 5001 changes, grouped by the weekday of t.\n- Monday contrast: difference of sample means, all changes. The 95% interval is a **Welch** two-sample t interval (Monday n=919 vs Tue–Fri n=4082), chosen because it does not assume equal variances. As a sensitivity check, an OLS regression on a Monday dummy with Newey–West (HAC, 5 lags) SE.\n- Out-of-sample: each evaluation-period change is forecast by the estimation-period mean change for its weekday (`train_weekday_means_bp`). The baseline forecast is zero. Both are scored by RMSE over the 2501 evaluation changes. A Diebold–Mariano-type test on the squared-error differential (HAC, 5 lags) is supplementary.\n- Decision rule: `conclusion` comes from the ANOVA at the 5% level. If the ANOVA rejected, the sign would come from the Monday interval.\n- Sensitivity checks on the ANOVA: Welch ANOVA, Kruskal–Wallis, and a Levene (median) test of equal variances.\n- No randomness is involved (`random_seed` = null). `out/analysis.py` regenerates every output.\n\n## Results\n- Weekday means (table1.csv, figure1.png) range from −0.22 bp (Tuesday) to +0.02 bp (Wednesday). Each 95% interval covers zero.\n- ANOVA: F = 0.263 (`anova_f_stat`), p = 0.902 (`anova_p_value`). Equality of the weekday means is not rejected.\n- Monday contrast: +0.050 bp (`effect_estimate`), 95% Welch CI [−0.319, +0.420] bp (`effect_ci_low`, `effect_ci_high`), p = 0.79 (`effect_p_welch`).\n- Out-of-sample: zero-change RMSE 4.876 bp (`baseline_metric`), weekday-mean RMSE 4.884 bp (`model_metric`), a ratio of 1.0017 (`oos_rmse_ratio_model_over_baseline`). The weekday model is slightly *worse* than the random walk. The DM statistic is t = 1.89, p = 0.058 (`oos_dm_t`, `oos_dm_p`), so the deterioration is borderline and not significant at 5%. Using the overall training mean instead gives an RMSE of 4.875 bp (`oos_rmse_unconditional_train_mean`).\n- **Conclusion: `no_detectable_effect`** (`conclusion`). These data show no evidence that the weekday predicts daily 10-year yield changes, and weekday-specific means do not beat a zero forecast out of sample.\n\n## Uncertainty\n- The Welch SE of the Monday contrast is 0.188 bp (`effect_se_welch`). The HAC SE is 0.194 bp (`sens_hac_effect_se`, p = 0.80, `sens_hac_effect_p`), so allowing for autocorrelation and heteroskedasticity barely changes the inference.\n- The CI half-width is about 0.37 bp. A Monday effect larger than about ±0.4 bp/day is therefore inconsistent with these data at the 95% level. Smaller effects cannot be ruled out, but they would be tiny next to the ~5 bp daily standard deviation.\n- The equal-variance assumption of the classical ANOVA is violated: Levene p ≈ 1e-7 (`sens_levene_p`). The conclusion holds anyway under Welch ANOVA (p = 0.902, `sens_welch_anova_p`) and Kruskal–Wallis (p = 0.52, `sens_kruskal_p`).\n- The OOS comparison rests on a single split, with no resampling-based interval for the RMSE difference beyond the DM test.\n\n## Limitations and open issues\n- **What would invalidate the result.** Strong dependence or volatility clustering beyond what 5-lag HAC captures could make the SEs too small. Since the effect is null, though, too-small SEs would only make it *easier* to find an effect, so the null conclusion is conservative in that direction. Changes in the weekday pattern over time (e.g., FOMC calendars, the 2008–09 crisis, QE) could hide period-specific effects that cancel in the pooled 20-year sample. No subperiod tests were run beyond the train/test split.\n- **Measurement convention.** Post-holiday changes span more than one trading day; for example, Tuesday after a Monday holiday captures Fri→Tue. This inflates the variance of some weekday groups (one reason for the Levene rejection) and slightly mixes weekday attributions. I followed the task rule and did not adjust for it. DGS10 is an end-of-day constant-maturity interpolated yield, not a traded price, and is discretised at 1 bp.\n- **Not checked.** Event-day effects such as FOMC days or payroll Fridays, intraday timing, other maturities, and any multiple-testing adjustment across folklore hypotheses (only the Monday contrast was tested specifically). The classical ANOVA is the mandated test even though its equal-variance assumption fails; robust alternatives agree with it.\n- **Main threats.** The power to detect sub-0.4 bp effects is limited. A single OOS split can be sensitive to regime (2010–2019 is a low-rate era). Heavy tails make RMSE sensitive to a few large days.\n- No review issues have been received yet.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The classical one-way ANOVA does not reject equal weekday means of daily 10y yield changes (F=0.263, p=0.902).\",\"results_keys\":[\"anova_f_stat\",\"anova_p_value\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Equal-variance assumption violated (Levene); Welch ANOVA and Kruskal-Wallis agree.\"},\n {\"id\":\"C2\",\"text\":\"The Monday contrast is +0.050 bp with 95% Welch CI [-0.319, 0.420] bp, i.e. not distinguishable from zero.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"effect_p_welch\"],\"status\":\"supported\",\"caveat\":\"Effects smaller than ~0.4 bp/day cannot be excluded.\"},\n {\"id\":\"C3\",\"text\":\"Weekday-specific estimation-period means do not improve out-of-sample forecasts over a zero-change forecast (RMSE 4.884 vs 4.876 bp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"oos_rmse_ratio_model_over_baseline\",\"oos_dm_p\"],\"status\":\"supported\",\"caveat\":\"Single train/test split; the small deterioration is not significant at 5% (DM p=0.058).\"},\n {\"id\":\"C4\",\"text\":\"Overall conclusion code: no_detectable_effect.\",\"results_keys\":[\"conclusion\",\"anova_p_value\"],\"status\":\"supported\",\"caveat\":\"Decided by the ANOVA at 5% as the task prescribes.\"},\n {\"id\":\"C5\",\"text\":\"The null result is robust to unequal variances, non-normality and autocorrelation in the Monday contrast SE.\",\"results_keys\":[\"sens_welch_anova_p\",\"sens_kruskal_p\",\"sens_levene_p\",\"sens_hac_effect_p\"],\"status\":\"supported\",\"caveat\":\"HAC with 5 lags only; no subperiod analysis.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np={\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches README\",\"schema/date/duplicate/missing audit in data_audit.md\",\"exclusions.csv sums 5217-216=5001\"],\"notes\":\"only rule-mandated exclusions\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"zero-change RMSE baseline_metric=4.8757 computed and independently recomputed\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, no seed needed; 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The conclusion code is `no_detectable_effect`, and the final gate run passed.\n\nThe analysis uses 5,001 daily changes: 2,500 in the 2000–2009 estimation period and 2,501 in the 2010–2019 evaluation period. The only rows removed were the ones the task requires: 215 holiday rows with no yield and the first observation, which has no change.\n\n| Estimand | Result |\n|---|---|\n| Joint test of equal weekday means (classical ANOVA) | F = 0.263, p = 0.902: no difference detected |\n| Monday contrast (Monday mean minus Tuesday–Friday mean) | +0.050 bp, 95% interval [−0.319, +0.420] bp, includes zero |\n| Out-of-sample error, zero-change forecast | 4.876 bp |\n| Out-of-sample error, weekday-mean forecast | 4.884 bp |\n\n- **Monday interval:** I used a Welch two-sample interval because it doesn't assume equal variances. A regression version with autocorrelation-robust errors gives almost the same standard error (0.194 vs 0.188 bp).\n- **Forecasting:** weekday means estimated on 2000–2009 did slightly worse than simply predicting no change. The gap is borderline and not significant at 5% (p = 0.058).\n- **Robustness:** the five weekdays don't have equal variances (Levene test p ≈ 1e-7), so an assumption of the required ANOVA fails. Two tests that don't rely on that assumption give the same answer: Welch ANOVA p = 0.902, Kruskal–Wallis p = 0.52.\n\n**Limitations:**\n- A Monday effect smaller than about ±0.4 bp per day can't be ruled out. Daily changes have a standard deviation of about 5 bp, so such an effect would be tiny.\n- The out-of-sample comparison rests on a single train/test split.\n- As the task specifies, a change after a holiday covers more than one trading day but counts toward the weekday it lands on. 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