{"id":"W4372402111","doi":"10.1016/j.ress.2023.109370","title":"Dynamic Bayesian network model to study under-deposit corrosion","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Bayesian network; Probabilistic logic; Dynamic Bayesian network; Pipeline (software); Computer science; Engineering; Artificial intelligence; Mechanical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001934968,0.0008455073,0.001647822,0.001630887,0.0005016324,0.001034026,0.002235137,0.002278512,0.003830807],"category_scores_gemma":[0.007846351,0.0009205175,0.000945097,0.001321651,0.0009206487,0.002501323,0.0009679478,0.001593003,0.0004324641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001553325,"about_ca_system_score_gemma":0.001205012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02158177,"about_ca_topic_score_gemma":0.01322309,"domain_scores_codex":[0.9992746,0.0002181584,0.00002621347,0.0002111854,0.0001715234,0.00009836563],"domain_scores_gemma":[0.9969347,0.0019277,0.0003880908,0.00009680633,0.0005522646,0.0001004351],"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":[0.00005438956,0.00003245511,0.0009748272,0.00003136388,0.00005134274,0.0000895628,0.00002741483,0.9681474,0.0004542235,0.02302721,0.0008822898,0.006227486],"study_design_scores_gemma":[0.000005016044,0.000005776472,0.0001326368,0.000002024557,0.00001283457,0.00001121723,0.00000323682,0.9949778,0.00005148336,0.004619263,0.0001745544,0.000004100587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06564438,0.000744847,0.925176,0.001003325,0.0001327788,0.00005218392,0.000433548,0.0002789229,0.00653394],"genre_scores_gemma":[0.9131814,0.001041184,0.06004117,0.0002537476,0.0001524004,0.0001496719,0.0008289752,0.0001399365,0.02421152],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02158177,"threshold_uncertainty_score":0.0429123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008352864317190078,"score_gpt":0.2218594488905387,"score_spread":0.2135065845733486,"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."}}