{
  "visual_id": "E60-snapshot-likelihood",
  "title": "A binomial probability fit to released atomic snapshots",
  "classification": "scoped_public_input_calculation",
  "run_ids": [
    "20261002T062007216298Z-E23-snapshot-likelihood"
  ],
  "claim_ids": [
    "C14",
    "C15"
  ],
  "input_files": [
    {
      "path": "runs/20261002T062007216298Z-E23-snapshot-likelihood/likelihood-arrays.npz",
      "sha256": "6b59cf05abccc53174d5f3328f86f358c036ca1223708358a0938c633d0123a5"
    },
    {
      "path": "runs/20261002T062007216298Z-E23-snapshot-likelihood/counts.csv",
      "sha256": "b425c591f8aeb227ebac72ed32fcfeb0f77e4d970e5ae582ed5729714aa4843d"
    },
    {
      "path": "runs/20261002T062007216298Z-E23-snapshot-likelihood/results.json",
      "sha256": "84a07e8a9f2401e4063cacf7984ce5db7949feec1a33f0e1b80ce15ced86f3bf"
    }
  ],
  "configurations": {
    "E23-snapshot-likelihood": {
      "seed": 2026100231,
      "central_halfwidth_over_R": 0.1,
      "magnetization_threshold": 0.2,
      "sensitivity_halfwidths": [
        0.025,
        0.05,
        0.1
      ],
      "sensitivity_thresholds": [
        -0.2,
        0.0,
        0.2
      ],
      "calibration_scenarios": [
        [
          0.0,
          0.0
        ],
        [
          0.05,
          0.0
        ],
        [
          0.0,
          0.05
        ],
        [
          0.05,
          0.05
        ],
        [
          0.1,
          0.1
        ]
      ],
      "t50_bounds_ms": [
        0.1,
        100.0
      ],
      "shape_bounds": [
        0.25,
        12.0
      ],
      "profile_t50_ms": [
        1.0,
        8.0,
        281
      ],
      "bootstrap_trials": 1024,
      "maximum_job_seconds": 600,
      "maximum_RSS_bytes": 3221225472,
      "scope": "Conditional binomial likelihood for independent destructive snapshots, not longitudinal waiting times, an SGPE reconstruction or a microscopic decay lifetime."
    }
  },
  "assumptions": {
    "E23-snapshot-likelihood": [
      "Conditional binomial likelihood for independent destructive snapshots, not longitudinal waiting times, an SGPE reconstruction or a microscopic decay lifetime.",
      "Released time groups contain independent destructive samples, not repeated observations of the same cloud. Ten times contain68 shots.",
      "The predeclared central abs(x/R)<.1 and magnetization<.2 classifier follows the published40micrometre central window at nominal R200micrometres. It must reproduce the published count fractions.",
      "Probability is a phenomenological Weibull CDF. Shape1 is a constant-hazard probability benchmark; neither fit supplies microscopic nucleation or growth dynamics.",
      "Fixed false-positive/negative probabilities are declared sensitivity scenarios, not measured calibration. Classification variants reuse the same shots and cannot be combined as independent data.",
      "Parametric bootstrap samples independent binomial counts at the actual ten sample sizes; inference remains conditional on this sampling and fitted probability family. No population or fundamental-constant constraint."
    ]
  },
  "plotted_values": {
    "path": "E60-snapshot-likelihood-data.json",
    "sha256": "34702b896997b6776f24cd219e1a1d1b7dc8dfb93eac93986ffd5ddb8feb74f1"
  },
  "caption": "The published magnetization classifier reproduces all counts. Probability curves are fitted to independent binomial groups; snapshot labels are not longitudinal waiting-time records. Pointwise Wilson intervals visualize counts only. The profile uses a plug-in bootstrap joint-region cutoff with finite Monte Carlo error;16.3% of fitted-family bootstrap replicates hit the declared upper shape bound. Assumed classification errors and window variants are saved separately. No microscopic lifetime, exact-coverage interval or fundamental-vacuum claim follows.",
  "alt_text": "Bubble probability rises over the first seven milliseconds; the fitted Weibull median is about3.37ms, with a broad conditional profile region.",
  "generator": "src/metastable/public_report.py",
  "generator_sha256": "9e263878b7237d781a41d94693004412279b5cfa808ccd6e335b7b07269910d4",
  "generator_dependencies_sha256": {
    "src/metastable/render.py": "ff4837be5102588b6614aeb2b7880b10c0a0c20a155e2eb7b409014a47a3de01",
    "src/metastable/spin_report.py": "dd5c64c5c658938948672a7dc767aa02c34dde012c3fc4aedfc3642f88796847",
    "src/metastable/continuation_report.py": "f9bbce6d0e201a9f55c826d6e9c47ab901e51a71b4a1f962b52eb08a63ba5fe9",
    "src/metastable/refinement_report.py": "4539c0ec814603105ab3a2b5cc82ce291c0151113b5bd3916cdc235aadb9bfb0",
    "src/metastable/atomic_mass_report.py": "46dd1cdb56a5a02cafe001a1f6a7fb201e149461850df41e012f77a580c3694b",
    "src/metastable/closure_report.py": "b34a03a9e0058eed65722df542dbb92691dd138787e2078b43ee9f18e221f75c",
    "src/metastable/october_report.py": "f93e7f89e43165d8d25e2184b9b32e6e21c3e1ffe66020d35944f263aaecb726",
    "src/metastable/october_followup_report.py": "925d905ff3bddf97a7ace2f68a4a28e7dfea2238c4f81d891dda2a9e70c6d4f6",
    "src/metastable/october_dynamics_report.py": "6b8d84c0ce0d78f52d064c68697f0d9c885cdc736e215b54eba46821dbe59a33",
    "src/metastable/october_calibration_report.py": "f714ef7ccfaa26e847c6135054f53528b3ff1d7096e7d113cffeea333e77e3d2",
    "src/metastable/october_interface_report.py": "2fc8826a33c952d7898154f84830748c26842c9a165d14aa7b7fb9721fac00bd",
    "src/metastable/october_checkpoint8_report.py": "f3b54f23510b9bfeffc5ff04e6df54e967228f43ba25a15e12e34f898cdfe0af",
    "src/metastable/october_checkpoint9_report.py": "4bd987aac59964ccb4768a95e0cb6f7d1fdccbeffe9483860da7b431f2224ae4",
    "src/metastable/october_checkpoint10_report.py": "9da673d773b63298e041914447ffd97065c1cf2b8966c90fc4ebcbee25a0ed63",
    "src/metastable/october_checkpoint11_report.py": "5f01064f02f4c76d0059d348a763b285c92c3677e6cb0ba0bbf7faa1a4b98248",
    "src/metastable/october_checkpoint12_report.py": "46022030652e3a1e274dfd27b36dfab25d90a97fde439b1da44d6f1fdc5140b5",
    "src/metastable/october_checkpoint13_report.py": "997c59be3af1b6b7be95fd4314e0f3b98c562629901a639572284045a65980c2",
    "src/metastable/october_checkpoint14_report.py": "d84de7d8516d8a4d8e21b5f9f279476f3035f719c1c8ab03b7ac1e44bf822240",
    "src/metastable/october_checkpoint15_report.py": "1f6e17b9549dc31fbb58ba578c4d8ed8e219b4ad30810df0a9d7d2d00e63efdc",
    "src/metastable/october_checkpoint16_report.py": "74cb8aac45d1d5ce0e6eb6678e9b6cdf98cd46fd6e264a513b612ab763fcd034",
    "src/metastable/october_checkpoint17_report.py": "9daceb74e91f6f8db0f7d89929226d46c464c284d06d0694f525fecf0c736bf6",
    "src/metastable/october_checkpoint18_report.py": "f33f88e316cae93af87cef75d8df41ce82970c8f8bc466dd7a094a92c5f00f8b",
    "src/metastable/october_checkpoint19_report.py": "ce2250507145354b6478cafaaff98c2b8ae2795c3c309f0d903a920365a77090",
    "src/metastable/october_checkpoint20_report.py": "e2ea0a8a374036254008cf6de8f10352dfb2585c4ccb18c1e850c017e6128294",
    "src/metastable/october_checkpoint21_report.py": "596b873676167227b2c92d358fcdbc0614908a5a9daad1da6227b81fce203eec",
    "src/metastable/publication_files.py": "e68bd0ee4e849a72fa828f0cfcf34cb6b1b9f2a8bbdbb3d51085054915039607"
  },
  "command": "python run.py full public-report",
  "output_sha256": {
    "svg": "6fada6b6b9b494ca2a614844cdf17625764e9ffb5158bc30f3f7e2b053c4f4d8",
    "png": "d77144320d26b5c5fa1c230a05ea5029b843c066f029d30d18dcd91a52d1fca3"
  }
}