{"id":"W2999364082","doi":"10.1002/cjce.23702","title":"Process monitoring method based on correlation variable classification and vine copula","year":2020,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Vine copula; Vine; Copula (linguistics); Linear subspace; Subspace topology; Correlation; Mathematics; Statistics; Variables; Computer science; Econometrics; Artificial intelligence; Ecology","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":[0.0001730332,0.00008761199,0.0001295728,0.000063703,0.0000355749,0.00004311991,0.0000951796,0.00006190895,0.000007867709],"category_scores_gemma":[0.0001612068,0.00007303061,0.00002758713,0.0001745722,0.000008280987,0.00006561116,0.000001475946,0.0003000186,0.000001758619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001117871,"about_ca_system_score_gemma":0.00006051982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000570155,"about_ca_topic_score_gemma":0.000003752536,"domain_scores_codex":[0.999485,0.00001158211,0.0002026011,0.00005769946,0.0001145466,0.0001286067],"domain_scores_gemma":[0.9994996,0.0000700821,0.00004197895,0.00005847407,0.0000476329,0.0002822941],"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":[0.000008807288,9.18746e-7,0.000140146,0.0000495441,0.0000158894,0.000002814596,0.0001454453,0.9080303,0.09066909,0.00009440958,0.00004384376,0.0007987481],"study_design_scores_gemma":[0.0002415644,0.00001975098,0.0001662319,0.00007238377,0.00001553831,0.00001974434,0.00002070671,0.9782298,0.02042295,0.00001258305,0.0007042224,0.00007452482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6954728,0.0008599447,0.2969067,0.003268577,0.001759737,0.0003531361,0.00001080507,0.0002231209,0.001145182],"genre_scores_gemma":[0.9987963,6.74555e-7,0.0008219521,0.00005235684,0.0003026206,0.0000036455,6.939125e-7,0.00001920598,0.000002533048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3033235,"threshold_uncertainty_score":0.2978102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01212904773594194,"score_gpt":0.2139323965450429,"score_spread":0.2018033488091009,"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."}}