{"id":"W4385856231","doi":"10.1016/j.ress.2023.109573","title":"Consequence assessment of gas pipeline failure caused by external pitting corrosion using an integrated Bayesian belief network and GIS model: Application with Alberta pipeline","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pipeline (software); Bayesian network; Gas pipeline; Pitting corrosion; Reliability engineering; Corrosion; Bayesian probability; Engineering; Petroleum engineering; Forensic engineering; Environmental science; Computer science; Machine learning; Artificial intelligence; Metallurgy; Materials science; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.000942203,0.0007263563,0.0004511368,0.0009879189,0.0003544879,0.000595556,0.0007686943,0.0008214841,0.0008976018],"category_scores_gemma":[0.002126924,0.000378353,0.0005208987,0.00065639,0.0004090596,0.0004841425,0.0004536118,0.00043356,0.00006654718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002019158,"about_ca_system_score_gemma":0.001536197,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1388464,"about_ca_topic_score_gemma":0.1570992,"domain_scores_codex":[0.9998212,0.00004559799,0.00000828814,0.00004441081,0.00005810825,0.0000222076],"domain_scores_gemma":[0.9991671,0.0004645009,0.00007992245,0.00002702884,0.000225745,0.00003578623],"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":[0.00008547462,0.00003988252,0.008646239,0.00002122915,0.00002171918,0.00009564619,0.0000267385,0.9832124,0.0006686286,0.0004111662,0.0001558542,0.006615135],"study_design_scores_gemma":[0.000004268564,0.00001147499,0.001880229,9.855324e-7,0.000006227093,0.000007426879,0.00001081679,0.9977024,0.0001849905,0.0001676327,0.000019894,0.000003672009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8852222,0.000117808,0.1115311,0.0001650233,0.00001485785,0.00005382734,0.0003429179,0.0004240534,0.002128208],"genre_scores_gemma":[0.9873191,0.0000382278,0.01194501,0.000007147204,0.000002353704,0.000008116945,0.0001215484,0.00001133337,0.00054721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8611536,"threshold_uncertainty_score":0.2760764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007005845772046253,"score_gpt":0.2261726983315197,"score_spread":0.2191668525594735,"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."}}