{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00111116,0.0004779966,0.0008026802,0.0001554496,0.0001682829,0.00005902435,0.0002726015,0.0003082221,0.000008011501],"category_scores_gemma":[0.0001080083,0.000406897,0.0001226534,0.001072131,0.0001199763,0.0002830986,0.00005692283,0.0005807593,0.000001574462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005285501,"about_ca_system_score_gemma":0.00007691655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001471799,"about_ca_topic_score_gemma":0.0002650649,"domain_scores_codex":[0.9971892,0.0001176769,0.001090203,0.0006459568,0.0004286778,0.0005282997],"domain_scores_gemma":[0.9982394,0.0003095991,0.0001924444,0.0007428132,0.0002771935,0.0002385228],"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":[0.00002859051,0.00002214785,0.001372197,0.0010226,0.00003443112,0.000003487768,0.0001506625,0.9520926,0.04452326,0.0001573202,0.00005169569,0.0005410045],"study_design_scores_gemma":[0.0003681012,0.00005595866,0.0008526919,0.0008622865,0.0001458874,0.00004006499,0.0002499128,0.9950171,0.001835901,0.00003804621,0.0001155,0.0004186157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2831427,0.00007995313,0.7154439,0.00009036003,0.0001153011,0.0004718274,0.0000828728,0.0005476659,0.00002540303],"genre_scores_gemma":[0.9751642,0.00006120611,0.0243232,0.000006681206,0.00008843699,0.00004009895,0.0002085022,0.00007918453,0.0000284434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6920215,"threshold_uncertainty_score":0.9998383,"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."}}