{"id":"W4400431369","doi":"10.1002/cjce.25393","title":"Risk assessment of gas pipeline using an integrated Bayesian belief network and <scp>GIS</scp>: Using Bayesian neural networks for external pitting corrosion modelling","year":2024,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayesian network; Artificial neural network; Pitting corrosion; Bayesian probability; Pipeline (software); Corrosion; Gas pipeline; Computer science; Environmental science; Data mining; Machine learning; Artificial intelligence; Engineering; Petroleum engineering; Materials science; Metallurgy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0008230599,0.0002915434,0.0004782291,0.0002288606,0.0001707934,0.000222936,0.0002799068,0.0002205128,0.000006746192],"category_scores_gemma":[0.0001605741,0.0002277649,0.0002442115,0.0004462096,0.00009390753,0.000294086,0.00002080305,0.001232464,3.395717e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000354952,"about_ca_system_score_gemma":0.000155175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001776048,"about_ca_topic_score_gemma":0.0002546172,"domain_scores_codex":[0.9982697,0.00004695167,0.0007600665,0.0001929292,0.0002011489,0.0005292546],"domain_scores_gemma":[0.9985947,0.0004271473,0.0001496711,0.0001747145,0.0001887538,0.0004649897],"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":[0.000003449058,0.00000317538,0.0002083031,0.0001220313,0.00008386416,0.0000133934,0.000208781,0.9787669,0.01874012,0.00008193219,0.00001350432,0.001754508],"study_design_scores_gemma":[0.0001512247,0.00003540657,0.00001612691,0.0007178478,0.0003306807,0.0001756271,0.00006815574,0.9940197,0.003772972,0.0005419527,0.00004066642,0.0001296801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.435769,0.001848149,0.5618225,0.00001831863,0.0004238579,0.00007677673,0.00001227879,0.000026085,0.000003053201],"genre_scores_gemma":[0.9516741,0.0000434476,0.04736904,0.00001345637,0.0008268488,0.00000144963,0.000006979568,0.00006266079,0.000002013443],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5159051,"threshold_uncertainty_score":0.9287984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01330916450080941,"score_gpt":0.2329338693740766,"score_spread":0.2196247048732672,"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."}}