{
  "schema": "mathbio-blog-lorenz-provenance-v2",
  "updated_at": "2026-09-29",
  "source_repository": "https://github.com/shayshay42/weighted-weak-SINDy",
  "source_commit": "f62145b1a89acb1e143474f0b4cc50ac9821408b",
  "core_sindy": {
    "source_file": "artifacts/v2/core/bootstrap_summary.csv",
    "source_sha256": "c4c0b830356f7308ed41dab832a07962414c71e8dfb5b6aac793693ca576620e",
    "local_asset": "assets/lorenz/results.csv",
    "local_sha256": "acc84cf32e5b3c3c16af23ada144444b98b8a2a0efe3e9a301e8c58693c5d662",
    "extraction": "Exact sindy_strong, sindy_weak, and sindy_weak_weighted rows for restricted mean VPT at all four training-noise levels.",
    "uncertainty": "Original paired hierarchical bootstrap intervals; 10000 resamples; seed 2026; not recomputed."
  },
  "survival_extension": {
    "source_summary_file": "artifacts/v2/tsfm_integral_survival/summary.csv",
    "source_summary_sha256": "c214e6375b51ed85fcbef8b5e593decde21dd6c8405ab66ee78b517ff6dfc929",
    "source_provenance_file": "artifacts/v2/tsfm_integral_survival/provenance.json",
    "source_provenance_sha256": "85c88bc3673814960c6cf4a3b6e195e1da40abd82ee43d0facb130c89cf74b70",
    "local_asset": "assets/lorenz/survival-extension.csv",
    "local_sha256": "c214e6375b51ed85fcbef8b5e593decde21dd6c8405ab66ee78b517ff6dfc929",
    "combined_run_rows": 720,
    "combined_trajectory_rows": 92160,
    "scope": "Adds noise-matched known-form integral matching and fixed-clean-prefix Panda and channel-wise Chronos-T5 references without changing the accepted core benchmark."
  },
  "conditioner_study": {
    "source_file": "artifacts/amortized_sindy/v1/weak_invariant_loss_ablation.json",
    "source_sha256": "6592698ad8f03cdf5920f6771a1377ffe24d1132666384e5214bbc7f247ef106",
    "local_asset": "assets/lorenz/conditioner-results.csv",
    "local_sha256": "4eff2dd5a93868c4db6bf7d09294a6b4f9e84816d0449ae13410984593564412",
    "scope": "Exact displayed source-validation and reserved-Lorenz metrics from the one-seed weak/Birkhoff loss ablation.",
    "source_split": "1024 training trajectories from four non-Lorenz families; 128 Sprott-B validation trajectories.",
    "reserved_audit": "16 Lorenz trajectories; target optimizer steps 0; target sparse-regression solves 0; gate failed.",
    "ctf4science_run": false
  },
  "figure": {
    "source_png": "artifacts/v2/tsfm_integral_survival/forecast_survival__all_tracks__noisy_levels.png",
    "source_pdf": "artifacts/v2/tsfm_integral_survival/forecast_survival__all_tracks__noisy_levels.pdf",
    "local_png": "assets/lorenz/forecast-survival.png",
    "local_pdf": "assets/lorenz/forecast-survival.pdf",
    "png_sha256": "7265920daaa5e8849ca0de68f5b8977d6ba97436eb9b22700d1758ea3151db5b",
    "pdf_sha256": "94ded9d7cb179b16481be2b3faee6d2d588cfe0fb49907e315d47014c6b80bf0",
    "dimensions": [4222, 1623],
    "noise_levels": [0.001, 0.01, 0.05],
    "information_tracks": [
      "state_only_dynamics_learning",
      "known_equation_form_hidden_parameters",
      "known_physics_forward_surrogate",
      "externally_pretrained_sequence_forecasting"
    ],
    "copied_without_modification": true,
    "layout": "No figure title or floating annotation; one boxed legend below the panels, partitioned into four information-track columns and containing all 16 methods."
  },
  "references": [
    "docs/methods.md",
    "docs/benchmark.md",
    "docs/results/lorenz63_v2.md",
    "docs/results/tsfm_integral_survival_extension.md",
    "docs/results/weak_invariant_loss_ablation.md",
    "docs/results/weak_invariant_experiment_walkthrough.md"
  ]
}