{"id":"W4403224968","doi":"10.1016/j.oceaneng.2024.119433","title":"Rapid failure risk analysis of corroded gas pipelines using machine learning","year":2024,"lang":"en","type":"article","venue":"Ocean Engineering","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Pipeline and Hazardous Materials Safety Administration; Hong Kong Polytechnic University","keywords":"Pipeline transport; Forensic engineering; Petroleum engineering; Corrosion; Engineering; Marine engineering; Environmental science; Materials science; Metallurgy; 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":[0.0009569601,0.0006737319,0.0006219474,0.00119569,0.0002527341,0.000441493,0.0006892174,0.0005244606,0.001006252],"category_scores_gemma":[0.003270676,0.0003069197,0.0005532051,0.000371751,0.0002647359,0.0006682704,0.0005076235,0.0006427208,0.000196314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005356327,"about_ca_system_score_gemma":0.0005434078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004170749,"about_ca_topic_score_gemma":0.003357697,"domain_scores_codex":[0.9997278,0.00006220712,0.00001770689,0.00005588977,0.00009862775,0.0000377038],"domain_scores_gemma":[0.9981888,0.001026388,0.0002511035,0.0001137292,0.0003688027,0.00005126106],"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":[0.00009376813,0.00005291063,0.006187528,0.00003368323,0.00003931384,0.0000963877,0.00002265854,0.954076,0.002364418,0.0006884778,0.0004554038,0.03588952],"study_design_scores_gemma":[8.914195e-7,0.00001607803,0.0006341877,0.000001252555,0.000002133458,0.00001033592,0.00000358721,0.9986464,0.0003581301,0.0002951577,0.00002987718,0.000001929565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5538899,0.0004225181,0.4425362,0.0002065434,0.00003939852,0.00006323605,0.0002840389,0.001180098,0.001378061],"genre_scores_gemma":[0.9774967,0.00006282266,0.0213466,0.0000126526,0.00001261113,0.0000200777,0.000208607,0.00002340579,0.0008165564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004170749,"threshold_uncertainty_score":0.008292913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00811398863847363,"score_gpt":0.2063747041550809,"score_spread":0.1982607155166073,"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."}}