{"id":"W4386892956","doi":"10.1016/j.ress.2023.109672","title":"Improving failure modeling for gas transmission pipelines: A survival analysis and machine learning integrated approach","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Hong Kong Polytechnic University","keywords":"Censoring (clinical trials); Pipeline (software); Pipeline transport; Reliability engineering; Reliability (semiconductor); Computer science; Machine learning; Covariate; Engineering; Data mining; Artificial intelligence; Statistics","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":[0.0009331778,0.0008841146,0.0009040656,0.000967161,0.0004477743,0.0006577195,0.001162828,0.001083945,0.001645001],"category_scores_gemma":[0.002868981,0.0004613841,0.001081496,0.000470854,0.0003409896,0.001050271,0.0005487726,0.001141686,0.0003660335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007588219,"about_ca_system_score_gemma":0.00116676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01585176,"about_ca_topic_score_gemma":0.0107367,"domain_scores_codex":[0.9997125,0.00008223135,0.00001781425,0.00006092532,0.00008668513,0.00003984108],"domain_scores_gemma":[0.9987447,0.0007367196,0.000122877,0.00009133566,0.0002709781,0.0000333597],"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":[0.00001425029,0.0000259253,0.0006763274,0.0000101943,0.0000132745,0.00001211852,0.00001027219,0.9851372,0.0005713623,0.0005110821,0.000142353,0.01287563],"study_design_scores_gemma":[3.223653e-7,0.000003077195,0.00005119832,4.683651e-7,0.000001397927,0.000001253674,8.399843e-7,0.9996567,0.00007758775,0.0001923936,0.0000140892,6.78516e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08228797,0.000208958,0.9151608,0.0002048972,0.00001979192,0.00003500381,0.0001546005,0.001052409,0.0008755944],"genre_scores_gemma":[0.9177567,0.0001833899,0.07905633,0.00006625613,0.00004250721,0.00007054421,0.0003541901,0.0001270357,0.002343012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01585176,"threshold_uncertainty_score":0.031519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01002705680750701,"score_gpt":0.1977532676427102,"score_spread":0.1877262108352032,"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."}}