{"id":"W2037178935","doi":"10.1021/ie0710014","title":"Fault Detection and Classification for a Process with Multiple Production Grades","year":2008,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Department of Family and Community Medicine, University of Toronto; National Science Council","keywords":"Principal component analysis; Subspace topology; Outlier; Computer science; Covariance; Fault detection and isolation; Data mining; Process (computing); Cluster analysis; Covariance matrix; Set (abstract data type); Statistic; Mahalanobis distance; Data set; Algorithm; Pattern recognition (psychology); Artificial intelligence; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006598447,0.0007120299,0.0007597166,0.00168434,0.0005473301,0.0006866399,0.0006616265,0.0009465308,0.0006816724],"category_scores_gemma":[0.001492513,0.0002015286,0.0006661151,0.001025715,0.0003360786,0.000598432,0.0003753386,0.0005433016,0.0002209493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006848628,"about_ca_system_score_gemma":0.0005185464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005118904,"about_ca_topic_score_gemma":0.003139551,"domain_scores_codex":[0.9992598,0.00005193294,0.00005182632,0.0002286728,0.0003316956,0.00007608203],"domain_scores_gemma":[0.9994168,0.0001616851,0.0001150194,0.00004595802,0.000232308,0.00002819745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000948191,0.0004150601,0.02720228,0.0003033232,0.0001405838,0.0007956148,0.0003847373,0.1975994,0.08345811,0.001756804,0.001498697,0.6854972],"study_design_scores_gemma":[0.000007757817,0.0001058257,0.007391843,0.000004932349,0.00002025363,0.00009324998,0.00003736149,0.9790734,0.01229906,0.0004747861,0.0004779786,0.00001354158],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3246475,0.0005949318,0.6715976,0.0001779805,0.0001097003,0.00005831541,0.0001356394,0.001653768,0.001024655],"genre_scores_gemma":[0.9495737,0.000157093,0.04923464,0.00002653251,0.00001949089,0.00003029158,0.0001006211,0.00002059701,0.0008369531],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005118904,"threshold_uncertainty_score":0.01017821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08757735906815933,"score_gpt":0.2996906559678742,"score_spread":0.2121132968997149,"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."}}