{"id":"W3171896163","doi":"10.1002/cjce.24207","title":"Dynamic industrial process monitoring based on concurrent fast and slow‐time‐varying feature analytics","year":2021,"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":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Process (computing); Analytics; Feature (linguistics); Principal component analysis; Dynamic Bayesian network; Data mining; Inference; Bayesian probability; Artificial intelligence; Pattern recognition (psychology); Real-time computing","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":[0.0008677072,0.0007897975,0.0006617444,0.001536279,0.0004507751,0.000833668,0.0006238911,0.0003263499,0.0005367568],"category_scores_gemma":[0.002097023,0.0003146383,0.0005418992,0.001306938,0.0003865506,0.001597463,0.001048188,0.0006444486,0.0001488984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005004766,"about_ca_system_score_gemma":0.0008980262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006129426,"about_ca_topic_score_gemma":0.005775705,"domain_scores_codex":[0.9990608,0.0001200584,0.00004338883,0.0002494649,0.0004541713,0.00007223852],"domain_scores_gemma":[0.9992762,0.000182431,0.0001482442,0.00006894027,0.0002876989,0.00003652936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004307094,0.0002980855,0.0194809,0.0001473556,0.0002075694,0.0002121263,0.0002955636,0.2807294,0.08894137,0.008908499,0.001412414,0.5989361],"study_design_scores_gemma":[0.000007549878,0.00004870992,0.002984323,0.000003215514,0.00002413796,0.00003359525,0.00001698159,0.9877548,0.007158793,0.001567842,0.0003828458,0.00001732652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09736956,0.0001173691,0.9002083,0.00007770573,0.00002161348,0.00005241156,0.00007243454,0.0005330386,0.001547615],"genre_scores_gemma":[0.9133174,0.00008014452,0.08571959,0.00001973063,0.00002317673,0.00003570777,0.00009569548,0.00003309188,0.000675456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006129426,"threshold_uncertainty_score":0.01218748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008419619100596168,"score_gpt":0.2018554899329124,"score_spread":0.1934358708323162,"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."}}