{"id":"W2615589810","doi":"10.1002/cjce.22897","title":"Efficient recursive kernel canonical variate analysis for monitoring nonlinear time‐varying processes","year":2017,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Technology Project of Nantong City; National Natural Science Foundation of China","keywords":"Random variate; Kernel (algebra); Mathematics; Nonlinear system; Covariance; Applied mathematics; Kernel density estimation; Variable kernel density estimation; Probability density function; Algorithm; Control theory (sociology); Mathematical optimization; Computer science; Kernel method; Random variable; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003180084,0.0001546369,0.0003273129,0.0001736152,0.0002160597,0.0002120898,0.0004804946,0.0000948853,0.000009544679],"category_scores_gemma":[0.0006252166,0.000126124,0.0001996573,0.0001863213,0.00003513846,0.00006546601,0.00001080916,0.000305238,0.000004242293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002729394,"about_ca_system_score_gemma":0.0002354689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005619916,"about_ca_topic_score_gemma":0.0001397374,"domain_scores_codex":[0.9990407,0.000007075272,0.0003478793,0.00009729582,0.0001596628,0.0003474383],"domain_scores_gemma":[0.9989333,0.0001318577,0.0001314046,0.0002503823,0.000185873,0.0003672023],"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":[0.00001277286,0.000003222842,0.0001536397,0.00006949533,0.0005577288,0.00001441836,0.0002098773,0.9732587,0.02537184,0.00001893596,0.00003154362,0.0002977731],"study_design_scores_gemma":[0.000491388,0.00001884295,0.0002104106,0.0001480562,0.0003254461,0.00005065299,0.00001258268,0.9554714,0.04192885,0.00001462016,0.001116497,0.0002112794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9841999,0.0009974487,0.01239505,0.0003651879,0.00139139,0.0002295487,0.00003346134,0.00006840718,0.0003195953],"genre_scores_gemma":[0.9984468,0.000002272589,0.0007538809,0.000006352108,0.0007185596,0.00001046543,0.000001085464,0.00003208977,0.00002847261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01778738,"threshold_uncertainty_score":0.5143188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009641985389781688,"score_gpt":0.2183401259545196,"score_spread":0.2086981405647379,"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."}}