{"id":"W4312584651","doi":"10.1109/tim.2022.3228271","title":"Fault Diagnosis of Unseen Modes in Chemical Processes Based on Labeling and Class Progressive Adversarial Learning","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Key Research and Development Program of China; Shanghai Rising-Star Program; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Adversarial system; Class (philosophy); Computer science; Artificial intelligence; Fault (geology); Pattern recognition (psychology); Machine learning; Geology; Seismology","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":[0.0001726299,0.0001016094,0.0001253067,0.000158935,0.0001134778,0.00001786139,0.00003015132,0.00003138122,0.00003456781],"category_scores_gemma":[0.000008101517,0.000108843,0.00002288214,0.0001782277,0.00001940473,0.00006310867,6.559275e-7,0.0001897653,5.209658e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001708861,"about_ca_system_score_gemma":0.00003055803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003995815,"about_ca_topic_score_gemma":0.00004491564,"domain_scores_codex":[0.9991297,0.00006079463,0.0002089874,0.0001434719,0.0003551423,0.0001019428],"domain_scores_gemma":[0.9997926,0.0000367886,0.00004263426,0.00004699409,0.00004088825,0.00004003247],"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":[0.0002541859,0.0001528212,0.0004507469,0.0001879591,0.0000440306,0.000001196135,0.0008753166,0.9205839,0.02337793,0.000007293057,0.000006169751,0.05405847],"study_design_scores_gemma":[0.005430235,0.0005530762,0.000189542,0.0002320571,0.00005586636,0.000004832505,0.00456218,0.6580122,0.3301648,0.00001213133,0.0005211204,0.0002620026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9822013,0.00012811,0.01636722,0.0001558047,0.0002985733,0.0005171004,0.0000204872,0.0001002923,0.0002110926],"genre_scores_gemma":[0.9992615,0.00003831555,0.00007983665,0.00003593524,0.00001059291,0.0005536168,0.000002532021,0.00001242772,0.000005251233],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3067869,"threshold_uncertainty_score":0.4438487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01653015333096951,"score_gpt":0.2228150447983922,"score_spread":0.2062848914674227,"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."}}