{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001342241,0.000233681,0.0003192005,0.00008308229,0.0001985531,0.00005328303,0.0002059728,0.0002158595,0.0000245437],"category_scores_gemma":[0.0003742996,0.0001956805,0.0002162975,0.0006744276,0.00003077195,0.0001036256,0.00004279082,0.0003389426,0.0000213479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006418039,"about_ca_system_score_gemma":0.00001446683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008558507,"about_ca_topic_score_gemma":0.00001906293,"domain_scores_codex":[0.9987302,0.00002256342,0.000356181,0.0003421745,0.000212158,0.0003367697],"domain_scores_gemma":[0.9992202,0.0002320845,0.00005682112,0.0002234837,0.00008628185,0.0001811541],"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":[0.000115396,0.00001204887,0.0007435258,0.0001149485,0.000003569571,0.000001025091,0.0002574903,0.977958,0.0102027,0.00007502778,0.006983643,0.003532581],"study_design_scores_gemma":[0.0003392315,0.0001352629,0.0001111311,0.00009069742,0.00007390224,0.000001387909,0.00004931881,0.9919764,0.005868722,0.0006522639,0.0004782731,0.0002234513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06508349,0.0002703331,0.9321604,0.000491758,0.0006558954,0.0004074284,0.00002707668,0.0005252413,0.0003783861],"genre_scores_gemma":[0.9921305,0.00007421471,0.006421213,0.0004696829,0.0006771788,0.00003232528,0.00006115025,0.00004276104,0.0000909953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.927047,"threshold_uncertainty_score":0.7979621,"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."}}