| { |
| "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": { |
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| "numpy": "1.26.4", |
| "scipy": "1.14.0", |
| "platform": "Linux-5.15.0-139-generic-x86_64-with-glibc2.35" |
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| "space": "ai-sherpa/apub-percentile-upper-bound-stochastic-programming-repro", |
| "sha": "508ac1d2e744f24b6e7b973be2105dc44b7a8668", |
| "relationship": "separately attributed public reference" |
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| } |
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|
