{"id":"W4399357348","doi":"10.1002/cjce.25355","title":"Classification of pitting corrosion damage in process facilities using supervised machine learning","year":2024,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pitting corrosion; Corrosion; Process (computing); Computer science; Artificial intelligence; Metallurgy; Process engineering; Materials science; Machine learning; Environmental science; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006440363,0.0003322423,0.0004425972,0.0009312402,0.0001235624,0.000275681,0.0003777344,0.000401907,0.000341062],"category_scores_gemma":[0.001595377,0.0001236867,0.0003874475,0.0004168186,0.0001633055,0.0003049335,0.0001695064,0.0002509776,0.0001418742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002975567,"about_ca_system_score_gemma":0.0002411626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002409743,"about_ca_topic_score_gemma":0.002862504,"domain_scores_codex":[0.9996575,0.00009070822,0.00003065442,0.00005264077,0.0001362633,0.0000323452],"domain_scores_gemma":[0.9985008,0.0005445001,0.0002771413,0.0001343811,0.0005042687,0.00003899218],"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":[0.0006579288,0.0006792184,0.08323324,0.0002992547,0.0002152595,0.0003194439,0.0001568465,0.5977246,0.07290204,0.0004278698,0.001357521,0.2420268],"study_design_scores_gemma":[0.000003730377,0.0001167991,0.01324317,0.000005605933,0.000008807822,0.00003468134,0.00002033684,0.9781657,0.008157946,0.000155764,0.00008043316,0.000007038903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9116628,0.0001553561,0.08691806,0.00005221875,0.00001876894,0.00003614404,0.000174255,0.0003397633,0.0006426523],"genre_scores_gemma":[0.9888772,0.00002312451,0.01074442,0.000004214609,0.000004490747,0.000009460921,0.0001500204,0.000003619926,0.0001834159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002409743,"threshold_uncertainty_score":0.004791439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01605749554550115,"score_gpt":0.219050290184904,"score_spread":0.2029927946394028,"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."}}