{"id":"W2560589809","doi":"10.1115/1.4035438","title":"Predictive Abnormal Events Analysis Using Continuous Bayesian Network","year":2016,"lang":"en","type":"article","venue":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"China Scholarship Council","keywords":"Markov chain Monte Carlo; Computer science; Bayesian network; Bayesian probability; Reliability (semiconductor); Variable-order Bayesian network; Data mining; Machine learning; Dynamic Bayesian network; Markov chain; Artificial intelligence; Bayesian inference","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":[0.006710124,0.0004105969,0.001601909,0.001232666,0.0001114831,0.0001343311,0.0006338704,0.0002609222,0.00003006193],"category_scores_gemma":[0.003092246,0.000268921,0.0006392887,0.002381456,0.00002861237,0.0005510157,0.0001264317,0.0005541255,0.000004428077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002429917,"about_ca_system_score_gemma":0.00006800001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001513391,"about_ca_topic_score_gemma":0.00004205835,"domain_scores_codex":[0.9947732,0.000319664,0.002330547,0.000491285,0.001360398,0.0007248828],"domain_scores_gemma":[0.9952359,0.002514907,0.0008977216,0.0004856591,0.0004046373,0.0004611746],"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":[0.0000818006,0.0000286317,0.01348241,0.00001381301,0.0007378808,0.00005857642,0.0001382099,0.9812149,0.0003418596,0.000694116,0.00003783927,0.003169959],"study_design_scores_gemma":[0.0009777423,0.0001487918,0.006789565,0.0005102006,0.0006773715,0.000119025,0.000222963,0.9883457,0.00003859587,0.0004830768,0.001314856,0.0003721466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2911232,0.001193864,0.7062731,0.00006322512,0.001149853,0.0001242018,0.00002983875,0.00003483307,0.000007797613],"genre_scores_gemma":[0.9958699,0.0009275977,0.002443797,0.000005546801,0.0006765872,0.000006497248,0.000001247542,0.00003332581,0.00003548406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7047467,"threshold_uncertainty_score":0.9999763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01684921903819164,"score_gpt":0.2681627093706881,"score_spread":0.2513134903324965,"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."}}