{"id":"W2999480807","doi":"10.5006/3421","title":"A Nonparametric Bayesian Network Model for Predicting Corrosion Depth on Buried Pipelines","year":2020,"lang":"en","type":"article","venue":"CORROSION","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Corrosion; Pipeline transport; Percentile; Environmental science; Pipeline (software); Bayesian probability; Soil science; Geotechnical engineering; Materials science; Engineering; Statistics; Metallurgy; Mathematics; Environmental 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":[0.002391399,0.001015885,0.001145116,0.001542397,0.0003234115,0.000802924,0.001904575,0.001176739,0.001690529],"category_scores_gemma":[0.006384517,0.0007502314,0.0009807027,0.001281993,0.0006490722,0.001490104,0.0007652973,0.001387961,0.0004704381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001152281,"about_ca_system_score_gemma":0.0008442755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02310234,"about_ca_topic_score_gemma":0.01679583,"domain_scores_codex":[0.9992375,0.0002859295,0.00003761045,0.0002326283,0.0001230164,0.00008327041],"domain_scores_gemma":[0.9976844,0.001578591,0.000290306,0.00008097573,0.0003107129,0.00005505434],"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":[0.00006646593,0.0000324911,0.003691545,0.000033461,0.00004422711,0.00004750739,0.00003343162,0.9753494,0.0003425936,0.004882609,0.0006459493,0.01483033],"study_design_scores_gemma":[0.00000343144,0.000006249292,0.0003782665,0.00000433912,0.000006489528,0.000007914196,0.000002836769,0.9972538,0.00005940239,0.002138088,0.0001355422,0.000003595089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0768779,0.0006369055,0.9184128,0.0004867854,0.00004399244,0.0000644191,0.001406237,0.0003933588,0.001677655],"genre_scores_gemma":[0.9052838,0.001080637,0.0848988,0.0001672752,0.0001030199,0.0003509039,0.003779802,0.00007216477,0.004263511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02310234,"threshold_uncertainty_score":0.04593575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02691131578628219,"score_gpt":0.2464840385813897,"score_spread":0.2195727227951075,"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."}}