{"id":"W2070516785","doi":"10.1115/icone16-48871","title":"Bayesian Prediction for the Gumbel Distribution Applied to Feeder Pipe Thicknesses","year":2008,"lang":"en","type":"article","venue":"Volume 1: Plant Operations, Maintenance, Installations and Life Cycle; Component Reliability and Materials Issues; Advanced Applications of Nuclear Technology; Codes, Standards, Licensing and Regulatory Issues","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Gumbel distribution; Bayesian probability; Markov chain Monte Carlo; Margin (machine learning); Credible interval; Interval (graph theory); Statistics; Computer science; Confidence interval; Markov chain; Mathematics; Posterior probability; Prior probability; Measure (data warehouse); Algorithm; Data mining; Machine learning; Extreme value theory","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001537235,0.0003012589,0.0006322038,0.0002467374,0.001902769,0.0002169896,0.0002899443,0.0002512439,0.00002007654],"category_scores_gemma":[0.001065612,0.0002217282,0.00005019266,0.0005264872,0.001104096,0.0002605545,0.0001888671,0.0001645729,0.000002824531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007764804,"about_ca_system_score_gemma":0.00009941688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008838983,"about_ca_topic_score_gemma":0.00003019884,"domain_scores_codex":[0.9973394,0.00008161401,0.001003287,0.0007577629,0.0005074411,0.0003104687],"domain_scores_gemma":[0.9975834,0.0003646803,0.0002342854,0.0007887689,0.000880449,0.0001484522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001424761,0.0007403374,0.001468197,0.0005950814,0.0003028887,0.000002443976,0.003608869,0.163559,0.08623626,0.6799361,0.02745459,0.03467148],"study_design_scores_gemma":[0.001482543,0.0002704251,0.017998,0.0003590081,0.0001484547,0.0001045934,0.004969603,0.06470835,0.001831506,0.01546311,0.8919151,0.0007492243],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5508029,0.002681733,0.4293735,0.009569565,0.0002596911,0.002815295,0.004057317,0.0003658328,0.00007418286],"genre_scores_gemma":[0.9660187,0.002683578,0.0304662,0.00008387226,0.00009831565,0.0002313699,0.000179262,0.00002805769,0.0002106857],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8644606,"threshold_uncertainty_score":0.9993966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01615797882934711,"score_gpt":0.2696652697577917,"score_spread":0.2535072909284446,"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."}}