{"id":"W2115425990","doi":"10.1109/tsmcb.2007.908864","title":"An Enhanced Diagnostic System for Gear System Monitoring","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Lakehead University","keywords":"Condition monitoring; Reliability (semiconductor); Predictive maintenance; Reliability engineering; Computer science; Engineering; Classifier (UML); Automotive engineering; Control engineering; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000487203,0.0003543565,0.0004976516,0.0006524324,0.0001898481,0.0003910497,0.0007543535,0.0006609147,0.003321484],"category_scores_gemma":[0.00154348,0.0001870228,0.0001982946,0.0002433326,0.0001548843,0.0006371305,0.000593778,0.0004446151,0.0008454855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000277123,"about_ca_system_score_gemma":0.000285485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004921898,"about_ca_topic_score_gemma":0.0005657454,"domain_scores_codex":[0.9995649,0.00005234486,0.00003311218,0.0001039539,0.0002152393,0.00003047785],"domain_scores_gemma":[0.9993698,0.0001749788,0.00006281271,0.0001105155,0.0002548106,0.00002709286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005737846,0.0001485143,0.003013201,0.0003275051,0.00004137008,0.0003650261,0.0001298357,0.01019304,0.3196239,0.002376547,0.004744103,0.6584632],"study_design_scores_gemma":[0.0001662589,0.001073022,0.01304738,0.0001065969,0.000183773,0.003697729,0.00005580758,0.64656,0.2736089,0.002961723,0.05838569,0.0001531319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05579887,0.0006711079,0.9318483,0.0002130822,0.0002518592,0.0001933338,0.0002970045,0.007054331,0.003672032],"genre_scores_gemma":[0.6775072,0.000275171,0.3152883,0.0003289698,0.0001431002,0.0001558206,0.0003854031,0.00006720982,0.005848743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003321484,"threshold_uncertainty_score":0.0111115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01424198063190554,"score_gpt":0.2204190520453054,"score_spread":0.2061770714133999,"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."}}