{"id":"W2034892199","doi":"10.1016/j.ress.2009.06.001","title":"A Bayesian approach to modeling and predicting pitting flaws in steam generator tubes","year":2009,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Hydrogen embrittlement and corrosion behaviors in metals","field":"Materials Science","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Toronto Metropolitan University","funders":"","keywords":"Pitting corrosion; Probabilistic logic; Boiler (water heating); Randomness; Engineering; Bayesian probability; Reliability engineering; Nuclear engineering; Corrosion; Computer science; Materials science; Metallurgy; Statistics; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002757122,0.0002797088,0.0004949493,0.0001436111,0.0001450951,0.000101037,0.0002606682,0.0001231713,0.000007308937],"category_scores_gemma":[0.0001996273,0.0002702064,0.00007316087,0.0003303649,0.00001319465,0.0002124516,0.00007754787,0.0001923241,0.000009656032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002672294,"about_ca_system_score_gemma":0.0000335076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005946855,"about_ca_topic_score_gemma":0.000002044314,"domain_scores_codex":[0.9973903,0.0001296337,0.0008913017,0.0006826313,0.0003874176,0.0005187715],"domain_scores_gemma":[0.9991297,0.00006551768,0.00005046223,0.0004774027,0.00006207643,0.0002148678],"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":[0.00001925863,0.00007302441,0.003975227,0.0002819429,0.000001984516,0.000003426233,0.0006366294,0.5725508,0.4214465,0.0003673191,0.000003771418,0.0006401335],"study_design_scores_gemma":[0.0003263268,0.00005416106,0.0009864689,0.0003972828,0.00002166067,0.00001926986,0.000454134,0.9744192,0.02291309,0.000009028941,0.00004715798,0.0003522236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.864769,0.0001809903,0.1335654,0.00003048133,0.0002864314,0.0006075316,0.00001142183,0.0003120414,0.0002367393],"genre_scores_gemma":[0.9785014,0.000006173581,0.02121873,0.00002354396,0.000143494,0.0000639448,0.000004762211,0.00002739318,0.00001052553],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4018684,"threshold_uncertainty_score":0.999975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007673088386862666,"score_gpt":0.2127853200080686,"score_spread":0.205112231621206,"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."}}