{"id":"W4291430734","doi":"10.1002/rnc.6328","title":"Layer‐wise contribution‐filtered propagation for deep learning‐based fault isolation","year":2022,"lang":"en","type":"article","venue":"International Journal of Robust and Nonlinear Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Nonlinear system; Fault detection and isolation; Artificial intelligence; Isolation (microbiology); Attribution; Deep learning; Classifier (UML); Fault (geology); Observer (physics); Algorithm; Pattern recognition (psychology); Machine learning","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":[0.0009859664,0.001528701,0.0006888263,0.0007371071,0.0004110395,0.0006055318,0.001435585,0.001009678,0.002005369],"category_scores_gemma":[0.002194717,0.0003976276,0.0007169183,0.0004260521,0.0007022848,0.0009894185,0.001264264,0.001545488,0.0003499713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001040447,"about_ca_system_score_gemma":0.001567921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006070625,"about_ca_topic_score_gemma":0.00497354,"domain_scores_codex":[0.9997259,0.00004922491,0.0000150735,0.00005178897,0.0001009663,0.00005708817],"domain_scores_gemma":[0.9992932,0.0003624355,0.00008046717,0.00004857835,0.0001713398,0.00004408202],"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":[0.0001508226,0.00007548479,0.0005237292,0.00008590244,0.00004552269,0.00007354565,0.00004232326,0.8838477,0.006276078,0.005697761,0.001044448,0.1021367],"study_design_scores_gemma":[0.000002400032,0.00001013955,0.00002238763,0.000002486601,0.000003641908,0.000003311934,0.000001043319,0.9974426,0.001271409,0.001148784,0.00009014622,0.000001587202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0119127,0.0001889202,0.9860548,0.0001119988,0.00003323007,0.00002282313,0.00002749568,0.0009038943,0.0007441101],"genre_scores_gemma":[0.7975363,0.0002030967,0.1980395,0.0001597835,0.00004766954,0.0000979313,0.0001516712,0.0001382252,0.003625815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006070625,"threshold_uncertainty_score":0.01207054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009887537130888194,"score_gpt":0.2329623632848128,"score_spread":0.2230748261539246,"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."}}