{"id":"W2139346949","doi":"10.1002/cem.2712","title":"A Bayesian sparse reconstruction method for fault detection and isolation","year":2015,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Alberta Innovates - Technology Futures","keywords":"Fault detection and isolation; Bayesian probability; Gibbs sampling; Covariance matrix; Computer science; Pattern recognition (psychology); Matrix (chemical analysis); Noise (video); Bayesian inference; Algorithm; Posterior probability; Artificial intelligence; Mathematics; Statistics","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":[0.0007234523,0.00007472525,0.0001794575,0.0006311648,0.00002618754,0.00004396632,0.00003681939,0.00009427936,0.000001996084],"category_scores_gemma":[0.0004504746,0.00006958396,0.00006326662,0.000614605,0.000006281894,0.0002322113,0.000003207435,0.0001263177,9.960823e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001265904,"about_ca_system_score_gemma":0.00001582717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003185257,"about_ca_topic_score_gemma":0.00000464743,"domain_scores_codex":[0.9993435,0.00002374913,0.0003290471,0.00005668747,0.000155967,0.0000911036],"domain_scores_gemma":[0.9993461,0.00007849235,0.0001594561,0.00005143922,0.0002493595,0.0001151595],"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":[0.000138656,0.00001491918,0.0003321812,0.00008437061,0.00009491937,0.000001871987,0.0002566837,0.01641927,0.05032507,0.00001522525,0.0005929269,0.9317239],"study_design_scores_gemma":[0.001576307,0.0002341154,0.0001444725,0.00001965909,0.00004531596,0.0008123014,0.0003911054,0.9625118,0.01835344,0.0005138454,0.0152893,0.0001082733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06848186,0.0005246045,0.9289554,0.00005734865,0.001450517,0.0001010985,0.000001337419,0.00003634105,0.0003915377],"genre_scores_gemma":[0.9730635,0.00003950782,0.02643805,0.00001303694,0.0003775719,0.000003957909,2.41618e-7,0.0000147084,0.00004938237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9460926,"threshold_uncertainty_score":0.2837552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02157813684300133,"score_gpt":0.2600267921542379,"score_spread":0.2384486553112366,"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."}}