{"id":"W3104233578","doi":"10.1002/cjce.23923","title":"Dynamic process monitoring using dynamic latent‐variable and canonical correlation analysis model","year":2020,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Jiangnan University; National Natural Science Foundation of China","keywords":"Linear subspace; Canonical correlation; Control theory (sociology); Process (computing); Latent variable; Computer science; Canonical form; Mathematics; Algorithm; Mathematical optimization; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0016051,0.00105275,0.0008462652,0.001262747,0.0005154691,0.001600128,0.001101175,0.0006148445,0.001463152],"category_scores_gemma":[0.003438367,0.0003713708,0.001165207,0.001790907,0.0007146428,0.001250666,0.0009919797,0.00111228,0.0003027522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009672117,"about_ca_system_score_gemma":0.001686575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01375992,"about_ca_topic_score_gemma":0.007883969,"domain_scores_codex":[0.9985599,0.0004885939,0.00006691953,0.0003935036,0.0003656706,0.0001254626],"domain_scores_gemma":[0.9988112,0.0005320262,0.0001765016,0.00008499003,0.0003570525,0.00003819412],"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":[0.0001934524,0.0001280842,0.008784972,0.0002148695,0.0002490912,0.0001774552,0.0001854446,0.8234653,0.004435317,0.03373222,0.002294725,0.1261391],"study_design_scores_gemma":[0.000002550266,0.00001207905,0.0004267654,0.000003889015,0.000007952412,0.000008957543,0.000007387383,0.997375,0.0003119495,0.001626746,0.0002069423,0.000009751523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02554978,0.0002340376,0.9718122,0.0001345833,0.00003765554,0.00004773298,0.0000995321,0.0003958975,0.001688668],"genre_scores_gemma":[0.8722414,0.0004987337,0.1233623,0.00006892459,0.00005436459,0.0002271865,0.000478057,0.00009344993,0.002975558],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01375992,"threshold_uncertainty_score":0.02735966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007872039137300701,"score_gpt":0.2008437757585209,"score_spread":0.1929717366212202,"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."}}