{
  "checks": [
    {
      "check": "APUB remains an upper functional",
      "value": 0.040241729303371,
      "criterion": "> 0",
      "passed": true
    },
    {
      "check": "large-sample mean error",
      "value": 0.002182288318400172,
      "criterion": "< 0.05",
      "passed": true
    },
    {
      "check": "uncertainty gap contracts",
      "value": 0.12009874648898201,
      "criterion": "< 0.35",
      "passed": true
    },
    {
      "check": "Gamma population mean",
      "value": 2.0,
      "criterion": "= 2",
      "passed": true
    }
  ],
  "scope": "Fresh Gamma(2,1) bootstrap audit of the APUB upper-functional and consistency mechanisms; the paper's full stochastic programs were not rerun.",
  "paper_id": "eXLcL70GXO",
  "title": "Minimizing Upper Confidence Bounds: A Data-Driven Framework for Stochastic Programming",
  "seed": 3082026,
  "executed_at": "2026-08-02T16:32:48.986830+00:00",
  "all_checks_passed": true,
  "environment": {
    "python": "3.10.12",
    "numpy": "1.26.4",
    "scipy": "1.14.0",
    "platform": "Linux-5.15.0-139-generic-x86_64-with-glibc2.35"
  },
  "reference_evidence": {
    "space": "ai-sherpa/apub-percentile-upper-bound-stochastic-programming-repro",
    "sha": "508ac1d2e744f24b6e7b973be2105dc44b7a8668",
    "relationship": "separately attributed public reference"
  }
}