{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002020682,0.0009954543,0.001591799,0.001699413,0.0005817805,0.0009139603,0.00178662,0.001396945,0.002436143],"category_scores_gemma":[0.007507191,0.0007522954,0.001484928,0.00139051,0.0009698406,0.001680673,0.001819492,0.002143448,0.001018286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007515011,"about_ca_system_score_gemma":0.001720948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005123209,"about_ca_topic_score_gemma":0.003671101,"domain_scores_codex":[0.9984282,0.0005176021,0.00007036015,0.0002706531,0.00060417,0.0001090431],"domain_scores_gemma":[0.9975835,0.001385332,0.000286626,0.000189122,0.0004689791,0.00008644693],"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":[0.0002497479,0.0001259666,0.001626785,0.0002912606,0.0001921882,0.0002009736,0.0002440053,0.5412025,0.01514174,0.06938832,0.004719321,0.3666171],"study_design_scores_gemma":[0.00001232013,0.00002467186,0.000214973,0.00001227474,0.00001651118,0.00008082597,0.00001011063,0.9855617,0.001560545,0.01107214,0.001416539,0.00001741168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007953487,0.00007482724,0.9986947,0.00004283595,0.000008142929,0.00000989115,0.00001797164,0.0001034603,0.0002529152],"genre_scores_gemma":[0.1302845,0.0005496025,0.8648759,0.0002208116,0.0001631771,0.0001701825,0.0004627435,0.0001749264,0.003098065],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005123209,"threshold_uncertainty_score":0.01068652,"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."}}