{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002801951,0.0001117088,0.0002217984,0.0001993914,0.0001200551,0.00005280709,0.0001812379,0.00004751854,0.00001805943],"category_scores_gemma":[0.00003646504,0.00009049986,0.00009787833,0.0002601203,0.00001170178,0.00007589588,0.000008845488,0.0002904495,1.035235e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003037213,"about_ca_system_score_gemma":0.00007278487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005188978,"about_ca_topic_score_gemma":0.0004231859,"domain_scores_codex":[0.9993214,0.00001225995,0.0002475381,0.00008134665,0.0001103045,0.000227146],"domain_scores_gemma":[0.999429,0.00005038865,0.00004941061,0.0001259899,0.00004179732,0.0003033742],"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":[0.000007908048,0.000002765539,0.00003058866,0.00001660068,0.0001772391,0.000002811815,0.0001677312,0.936482,0.06256025,0.00003272381,0.00006603415,0.0004533083],"study_design_scores_gemma":[0.0002823592,0.00003100649,0.00001867746,0.00001100705,0.0001650444,0.00003710602,0.00002237107,0.9972392,0.001582807,0.00004380937,0.0004567401,0.0001098712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9308029,0.001083265,0.0671112,0.0002165729,0.0004398883,0.0001969041,0.00008052318,0.00005419936,0.00001457812],"genre_scores_gemma":[0.9992003,0.000004171434,0.0006059714,0.00002819067,0.00005566065,0.00005264881,0.000005380331,0.00002549217,0.00002222066],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0683974,"threshold_uncertainty_score":0.3690477,"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."}}