{"id":"W4308262764","doi":"10.1002/cjce.24757","title":"An improved dynamic latent variable regression model for fault diagnosis and causal analysis","year":2022,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Granger causality; Autoregressive model; Causality (physics); Computer science; Latent variable; Regression; Exploit; Variable (mathematics); Fault (geology); Focus (optics); Fault detection and isolation; Regression analysis; Econometrics; Machine learning; Data mining; Artificial intelligence; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.002552528,0.0009495432,0.001343057,0.00132216,0.0004159363,0.001298848,0.002149475,0.001250736,0.004186396],"category_scores_gemma":[0.004789765,0.0005841227,0.001236974,0.001361725,0.0007671801,0.001398592,0.001141832,0.002153531,0.0009597427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009891334,"about_ca_system_score_gemma":0.001418232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009277164,"about_ca_topic_score_gemma":0.007371151,"domain_scores_codex":[0.9986002,0.0006206829,0.00005874476,0.0003595774,0.0002398557,0.0001209678],"domain_scores_gemma":[0.9983217,0.001028805,0.0002169411,0.00009776295,0.0002884093,0.00004640661],"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":[0.00009264982,0.00007025515,0.001322704,0.00008571126,0.00008242646,0.0001072504,0.0000635902,0.9142576,0.00154429,0.03407118,0.001147046,0.04715525],"study_design_scores_gemma":[0.00000306658,0.000005703623,0.00005493704,0.000002374868,0.000003997492,0.000005682585,0.000001921438,0.9975432,0.00008200519,0.002103266,0.0001906429,0.000003256155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004584515,0.0001568678,0.9941067,0.0001551861,0.00002041454,0.00001572869,0.00007738182,0.0003222831,0.0005610451],"genre_scores_gemma":[0.6568553,0.0007234967,0.3303461,0.0001843043,0.0001297534,0.0002333932,0.0008972312,0.0002574457,0.0103731],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009277164,"threshold_uncertainty_score":0.01844633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006505395669728889,"score_gpt":0.1999625096510564,"score_spread":0.1934571139813275,"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."}}