{"id":"W4391956969","doi":"10.1016/j.psep.2024.02.051","title":"Predicting failure pressure of corroded gas pipelines: A data-driven approach using machine learning","year":2024,"lang":"en","type":"article","venue":"Process Safety and Environmental Protection","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Hong Kong Polytechnic University","keywords":"Pipeline (software); Pipeline transport; Interpretability; Dependability; Resource (disambiguation); GRASP; Integrity management; Engineering; Risk analysis (engineering); Computer science; Reliability engineering; Machine learning; Mechanical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005565229,0.0009007388,0.0008421475,0.001396642,0.0003286529,0.0007475416,0.001455976,0.001320024,0.0007654852],"category_scores_gemma":[0.002533588,0.0006105906,0.0008707739,0.0006363225,0.0003844379,0.0007091282,0.0004470309,0.0009158703,0.000320824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006289156,"about_ca_system_score_gemma":0.0006882108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007406806,"about_ca_topic_score_gemma":0.007643291,"domain_scores_codex":[0.9997533,0.0000343096,0.00001960142,0.00008262406,0.00007498867,0.00003530521],"domain_scores_gemma":[0.9985768,0.0008070983,0.0001704699,0.00008154825,0.0003033166,0.00006082594],"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":[0.0001789842,0.0003294998,0.01691927,0.00006828948,0.00007138632,0.0001584961,0.00002687995,0.9376892,0.00414948,0.0003199993,0.0007020274,0.03938651],"study_design_scores_gemma":[0.000001519426,0.00001100134,0.0009411527,0.000001046297,0.000002662061,0.000006874761,0.00000225626,0.9983656,0.0005085158,0.0001355296,0.00002136057,0.000002572348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7684109,0.0003885338,0.22637,0.0003710121,0.00005822693,0.00008927562,0.001585215,0.001665293,0.001061486],"genre_scores_gemma":[0.9846138,0.0000617995,0.0138144,0.00002558217,0.00002314888,0.00003927762,0.0009567465,0.00002623712,0.0004389655],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007406806,"threshold_uncertainty_score":0.01472735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01575146156128851,"score_gpt":0.2155777395735589,"score_spread":0.1998262780122704,"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."}}