{"id":"W4317569114","doi":"10.1186/s43065-022-00062-5","title":"Classification of failure modes of pipelines containing longitudinal surface cracks using mechanics-based and machine learning models","year":2023,"lang":"en","type":"article","venue":"Journal of Infrastructure Preservation and Resilience","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Machine learning; Gradient boosting; Artificial intelligence; Random forest; Computer science; Decision tree; Naive Bayes classifier; Pipeline transport; Support vector machine; Boosting (machine learning); Algorithm; Failure mode and effects analysis; Fracture mechanics; Engineering; Structural engineering; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.0004071255,0.0001165562,0.0003009115,0.0001987657,0.00007520059,0.00002586749,0.00012509,0.0001197816,0.00001036535],"category_scores_gemma":[0.0002610066,0.00008840017,0.00006392835,0.0004190818,0.00008973717,0.0004707562,0.0000314157,0.0003321269,4.065947e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002288925,"about_ca_system_score_gemma":0.00003553376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005197155,"about_ca_topic_score_gemma":0.00002563675,"domain_scores_codex":[0.998918,0.00005880276,0.0005398392,0.0001085246,0.000267012,0.0001078163],"domain_scores_gemma":[0.9989213,0.000165133,0.0003476157,0.00009414434,0.0004186365,0.00005316403],"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":[0.00004752896,0.00000261898,0.02308482,0.0001742434,0.00003191698,7.949385e-7,0.0002658732,0.8171045,0.1583598,0.0003725635,0.00001007835,0.0005453112],"study_design_scores_gemma":[0.0002377737,0.00006113605,0.03120016,0.000148737,0.00004949856,0.00001335643,0.0007423508,0.9501079,0.01293033,0.004422814,0.000009686106,0.00007628382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9059635,0.0005160148,0.09324095,0.0001405565,0.00004964155,0.0000534142,0.000007005781,0.000018114,0.00001074756],"genre_scores_gemma":[0.9910707,0.0003614376,0.008520924,0.000005401089,0.00002340855,2.811619e-7,0.000004385742,0.000008196956,0.00000529632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1454294,"threshold_uncertainty_score":0.3604854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04142823575006829,"score_gpt":0.2662568600758257,"score_spread":0.2248286243257574,"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."}}