{"id":"W2178278354","doi":"10.1115/icone17-75366","title":"Bayesian Analysis of Piping Failure Frequency Using OECD/NEA Data","year":2009,"lang":"en","type":"article","venue":"Volume 1: Plant Operations, Maintenance, Engineering, Modifications and Life Cycle; Component Reliability and Materials Issues; Next Generation Systems","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Nuclear Safety Commission; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Nuclear Safety Commission; University Network of Excellence in Nuclear Engineering","keywords":"Piping; Bayesian probability; Nuclear power plant; Probabilistic logic; Failure rate; Poisson distribution; Reliability engineering; Computer science; Stage (stratigraphy); Bayes estimator; Engineering; Statistics; Mathematics; Artificial intelligence; Environmental engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006635632,0.0005445669,0.0006400403,0.002478473,0.0003029916,0.000687623,0.0007112737,0.0007168025,0.0005726456],"category_scores_gemma":[0.01844835,0.0003460102,0.0008639239,0.001467611,0.0005164637,0.0008085276,0.0006500565,0.0006200762,0.0001618181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001302489,"about_ca_system_score_gemma":0.0008498637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02533451,"about_ca_topic_score_gemma":0.018538,"domain_scores_codex":[0.9980323,0.0009855767,0.00009875774,0.0002273156,0.0005587827,0.00009729418],"domain_scores_gemma":[0.9912934,0.006131609,0.0009672228,0.0006661784,0.0008796759,0.0000619099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007453106,0.00003236535,0.02698159,0.00004651637,0.00007925109,0.0001480461,0.00008375486,0.9397777,0.0009300391,0.006653406,0.0005686841,0.02462414],"study_design_scores_gemma":[0.00001316954,0.00003181298,0.02899296,0.0000158875,0.00002855564,0.00008260966,0.0000396629,0.9619259,0.001187679,0.006738307,0.0008951686,0.00004821107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4381543,0.0002182293,0.5551594,0.0001966706,0.00001195737,0.0001239024,0.002460068,0.0004735015,0.003202047],"genre_scores_gemma":[0.929534,0.0002576892,0.065578,0.00002752202,0.00001883958,0.0001348682,0.003802798,0.00004851094,0.0005978257],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02533451,"threshold_uncertainty_score":0.05037409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1106206067230977,"score_gpt":0.3023690580566887,"score_spread":0.191748451333591,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}