{"id":"W4225919316","doi":"10.1002/cjce.24415","title":"Multivariate temporal process monitoring with graph‐based predictable feature analysis","year":2022,"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":"University of Waterloo","funders":"Qinglan Project of Jiangsu Province of China; China Scholarship Council; University of Waterloo; National Natural Science Foundation of China","keywords":"Data mining; Fault detection and isolation; Computer science; Predictability; Process (computing); Graph; Dimensionality reduction; Curse of dimensionality; Principal component analysis; 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":[0.0004349007,0.0005229723,0.0003724482,0.001218041,0.0002139326,0.000576217,0.0005210516,0.0002925659,0.0006693521],"category_scores_gemma":[0.001896854,0.0001721118,0.0005405021,0.001166813,0.0003596641,0.0008262742,0.0005379687,0.0005405121,0.0001158936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004376109,"about_ca_system_score_gemma":0.0004600199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004571308,"about_ca_topic_score_gemma":0.003377677,"domain_scores_codex":[0.9996917,0.0000874453,0.00001512089,0.00008730065,0.00008394315,0.00003446253],"domain_scores_gemma":[0.9992292,0.0004049026,0.0001536629,0.00007262623,0.0001151119,0.0000245868],"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":[0.0001982802,0.00008945778,0.005543548,0.0000776225,0.00009046931,0.0001468713,0.00007824697,0.6804456,0.01281834,0.01373731,0.001189066,0.2855852],"study_design_scores_gemma":[0.000001818664,0.00001296144,0.0005710502,0.000001461452,0.000003551438,0.00001072416,0.000004827151,0.9961403,0.0006717972,0.00243767,0.0001405823,0.000003341656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04253584,0.000106273,0.9561206,0.00009654535,0.00001373819,0.00002119356,0.0001182026,0.0004780953,0.0005095976],"genre_scores_gemma":[0.874271,0.0001082954,0.1246725,0.00003206621,0.00002673033,0.0000412907,0.00027402,0.00003992182,0.0005340853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004571308,"threshold_uncertainty_score":0.00908941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004608433995508103,"score_gpt":0.1818455739584264,"score_spread":0.1772371399629183,"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."}}