{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004777312,0.0001118246,0.0002056756,0.0002062511,0.0001391245,0.0000732609,0.0001490385,0.0000443978,0.00006163753],"category_scores_gemma":[0.0001553399,0.0001083269,0.0001376442,0.00007231512,0.00001428194,0.0001680146,0.00001125369,0.0002572201,0.000002119797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001480382,"about_ca_system_score_gemma":0.00003519177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003904608,"about_ca_topic_score_gemma":0.000008282542,"domain_scores_codex":[0.998889,0.00008431171,0.0004470908,0.00009272816,0.0003576193,0.0001292294],"domain_scores_gemma":[0.9989733,0.0001390153,0.0002421172,0.00004928656,0.0005320944,0.00006419755],"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":[0.00094688,0.00005314136,0.0007176148,0.00001367954,0.0002465099,0.00001367362,0.00008723951,0.9738948,0.01434307,0.00008952379,0.0002560515,0.009337867],"study_design_scores_gemma":[0.006141378,0.0002795201,0.0003334933,0.00001543638,0.00004327741,0.0001065151,0.00008549017,0.9529974,0.000540418,0.00003214558,0.03931593,0.0001090314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.161565,0.0007947349,0.8328846,0.001417114,0.002595151,0.000468097,0.0001064829,0.00009042591,0.00007847697],"genre_scores_gemma":[0.9981827,0.00001822352,0.0007938637,0.0001322714,0.0006764908,0.00004781076,0.00004511802,0.00001959823,0.00008397688],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8366177,"threshold_uncertainty_score":0.4417441,"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."}}