{
 "interpreter_of_this_package": {
  "python": "3.12.3 (tags/v3.12.3:f6650f9, Apr  9 2024, 14:05:25) [MSC v.1938 64 bit (AMD64)]",
  "executable_flags_used_for_all_runs": "python -I -X utf8",
  "implementation": "CPython"
 },
 "python_version_used_for_the_v5e_fit": "not recorded in the fit log; the project interpreter is Python 3.12 (cp312 artefacts exist under brazil_raw/__pycache__ and the vendored datasus_dbc is a cp312 build)",
 "os": {
  "system": "Windows",
  "release": "11",
  "version": "10.0.26200",
  "machine": "AMD64",
  "note": "values of the machine that built this package; the fit ran on the same Windows 11 workstation"
 },
 "cpu": {
  "logical_cores_this_machine": 32,
  "logical_cores_recorded_in_amendments": "32 (A4 pins addendum: 29 of 32 logical cores were unused at 3 workers)",
  "ram_recorded": "125 GB (9520_v5d_fit_fast.py docstring); peak process-tree RSS of the v5e fit 13.13 GB (FIT_LOG_v5c.json)"
 },
 "workers": {
  "profile_stage_workers": 14,
  "command": "python -I -X utf8 9523_v5e_fit.py --fit --workers 14 --tag v5e --reuse-from work_v5c_v5d",
  "chart_and_ramp_stage_workers": "capped at 3 inside 9500 cmd_fit (unchanged); profiles do not depend on the worker count (fixed greedy partition, pure profile_task)",
  "record": "AMENDMENTS_9160.md A4 pins addendum (worker count) and A7 pins (Workers: 14)"
 },
 "stage_timings_seconds": {
  "check": 28.2,
  "profiles": 504.5,
  "pool": 346.1,
  "weights": 0.0,
  "chart": 281.2,
  "ramp": 1302.9,
  "figure": 1.7
 },
 "fit_started": "2026-10-15 01:17:41",
 "fit_finished": "2026-10-15 01:58:46",
 "core_package_versions_in_this_interpreter": {
  "numpy": "2.4.2",
  "pandas": "2.3.3",
  "scipy": "1.16.3",
  "pyarrow": "25.0.0",
  "matplotlib": "3.10.8",
  "duckdb": "1.5.4",
  "statsmodels": "0.14.6",
  "patsy": "1.0.2",
  "openpyxl": "3.1.5",
  "xlrd": "2.0.2",
  "psutil": "5.9.8",
  "threadpoolctl": "3.6.0"
 },
 "third_party_imports_of_traced_code": {
  "PIL": {
   "distribution": "pillow",
   "version": "12.1.1",
   "imported_by_n_scripts": 4
  },
  "bs4": {
   "distribution": "beautifulsoup4",
   "version": "4.14.3",
   "imported_by_n_scripts": 7
  },
  "cycler": {
   "distribution": "cycler",
   "version": "0.12.1",
   "imported_by_n_scripts": 1
  },
  "datasus_dbc": {
   "distribution": "not installed in this interpreter",
   "version": "not installed here",
   "imported_by_n_scripts": 2
  },
  "docx": {
   "distribution": "python-docx",
   "version": "1.2.0",
   "imported_by_n_scripts": 1
  },
  "duckdb": {
   "distribution": "duckdb",
   "version": "1.5.4",
   "imported_by_n_scripts": 13
  },
  "fitz": {
   "distribution": "PyMuPDF",
   "version": "1.24.0",
   "imported_by_n_scripts": 114
  },
  "lxml": {
   "distribution": "lxml",
   "version": "5.4.0",
   "imported_by_n_scripts": 3
  },
  "matplotlib": {
   "distribution": "matplotlib",
   "version": "3.10.8",
   "imported_by_n_scripts": 23
  },
  "numpy": {
   "distribution": "numpy",
   "version": "2.4.2",
   "imported_by_n_scripts": 348
  },
  "olefile": {
   "distribution": "olefile",
   "version": "0.47",
   "imported_by_n_scripts": 1
  },
  "openpyxl": {
   "distribution": "openpyxl",
   "version": "3.1.5",
   "imported_by_n_scripts": 50
  },
  "pandas": {
   "distribution": "pandas",
   "version": "2.3.3",
   "imported_by_n_scripts": 432
  },
  "patsy": {
   "distribution": "patsy",
   "version": "1.0.2",
   "imported_by_n_scripts": 8
  },
  "pdfplumber": {
   "distribution": "pdfplumber",
   "version": "0.11.9",
   "imported_by_n_scripts": 46
  },
  "psutil": {
   "distribution": "psutil",
   "version": "5.9.8",
   "imported_by_n_scripts": 1
  },
  "pyarrow": {
   "distribution": "pyarrow",
   "version": "25.0.0",
   "imported_by_n_scripts": 10
  },
  "pypdf": {
   "distribution": "pypdf",
   "version": "6.7.3",
   "imported_by_n_scripts": 15
  },
  "pyreadstat": {
   "distribution": "pyreadstat",
   "version": "1.3.3",
   "imported_by_n_scripts": 1
  },
  "pytesseract": {
   "distribution": "pytesseract",
   "version": "0.3.13",
   "imported_by_n_scripts": 2
  },
  "pytest": {
   "distribution": "pytest",
   "version": "9.1.1",
   "imported_by_n_scripts": 1
  },
  "python_calamine": {
   "distribution": "python-calamine",
   "version": "0.8.2",
   "imported_by_n_scripts": 1
  },
  "rapidfuzz": {
   "distribution": "RapidFuzz",
   "version": "3.14.5",
   "imported_by_n_scripts": 2
  },
  "requests": {
   "distribution": "requests",
   "version": "2.34.2",
   "imported_by_n_scripts": 7
  },
  "scipy": {
   "distribution": "scipy",
   "version": "1.16.3",
   "imported_by_n_scripts": 40
  },
  "shapely": {
   "distribution": "shapely",
   "version": "2.1.2",
   "imported_by_n_scripts": 1
  },
  "statsmodels": {
   "distribution": "statsmodels",
   "version": "0.14.6",
   "imported_by_n_scripts": 13
  },
  "threadpoolctl": {
   "distribution": "threadpoolctl",
   "version": "3.6.0",
   "imported_by_n_scripts": 3
  },
  "who_mdb_injury_scan_v2": {
   "distribution": "not installed in this interpreter",
   "version": "not installed here",
   "imported_by_n_scripts": 1
  },
  "xlrd": {
   "distribution": "xlrd",
   "version": "2.0.2",
   "imported_by_n_scripts": 12
  }
 },
 "package_version_caveat": "The versions above are those installed in the interpreter that built this package. The fit log does not record package versions. A reproducer should record theirs and compare the pins, not the versions.",
 "determinism_evidence": {
  "factory_two_run_determinism_file": "qa/claude_factory_20261008/determinism_935x_20261010.txt",
  "factory_two_run_determinism_lines": [
   "9351_usa_retry_ny_mn_la_coverage.py deterministic 3 parts",
   "9352_usa_fl_doh_2yo_coverage.py deterministic 2 parts",
   "9353_usa_fl_doh_k_status_archive.py deterministic 1 parts",
   "9354_usa_fl_doh_county_k_v2.py deterministic 1 parts",
   "9355_usa_la_ldh_school_k_2019_20.py deterministic 1 parts",
   "9356_usa_or_oha_county_2yo_retry.py deterministic 1 parts",
   "DONE"
  ],
  "fit_pooling_run_twice": {
   "pool_run1": "a79e6c482e528c4a926cb5680ce597d78b7c112e8e9b7eef64e0b4eb0575db43",
   "pool_run2": "a79e6c482e528c4a926cb5680ce597d78b7c112e8e9b7eef64e0b4eb0575db43",
   "contrast_run1": "d874657dcecc6829fe451a2a83828e4660d0524ce48f98456798a2b281917622",
   "contrast_run2": "d874657dcecc6829fe451a2a83828e4660d0524ce48f98456798a2b281917622",
   "identical": true,
   "design_pins": {
    "units": "7ec6e1a16b0164101f7254a355cd0af2cca71c9efb8d31277d9e18c0daf74355",
    "stacks": "1f10146a04a6b450d7a5c27372bd3871d444e0cc754f7fa7ff80a6d3d08ce7e8",
    "placebo": "d30da77f9ad6f9a3e87dd01ad2217d61c5116ff5cbaa1712fc9d04f0c61b91bc",
    "ppath": "43929303da7e71c4da0fbae6c7546b012776ae9422ad35146e23d2374ce8d1fa"
   },
   "rule_sha256": "8eb9af105542efc1a833b369cd9b460105754d6a1a2993ec20c071889dce9a07",
   "merge": "v5c"
  },
  "v5e_parts_full_tests": "A7 admits only parts with a FULL (non-quick) two-run determinism PASS of 9799 / 9999 (and sibling tests) written before the cutoff; see AMENDMENTS_9160 A7 pre-declaration and A7 refit record (\"Pooling twice was identical\").",
  "reuse": "171,440 of 190,665 design rows reused from the sealed v5d run where design hash AND cell hash AND code hash were identical (RULE 9160 V4); the other 19,225 were refitted (A7 refit record). Set V5D_NO_REUSE=1 to refit everything."
 },
 "random_seeds": "not recorded in this package (the profile and pooling code are described as deterministic; placebo/ramp seeds live in the traced driver code)"
}