{"id":"W2064709822","doi":"10.1021/ie100058y","title":"Dynamic Bayesian Approach for Control Loop Diagnosis with Underlying Mode Dependency","year":2010,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dependency (UML); Computer science; Bayesian probability; Mode (computer interface); Autoregressive model; Markov chain; Dynamic Bayesian network; Fractionating column; Hidden Markov model; Loop (graph theory); Artificial intelligence; Data mining; Machine learning; Distillation; Mathematics; Econometrics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.001430214,0.000937258,0.00116785,0.001192838,0.0005036504,0.0009202268,0.001564513,0.0009941454,0.001872937],"category_scores_gemma":[0.005858333,0.0008148646,0.0007511413,0.0007179654,0.0008422791,0.001750712,0.001092044,0.001737424,0.0003418257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001433192,"about_ca_system_score_gemma":0.001953902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008153357,"about_ca_topic_score_gemma":0.007696223,"domain_scores_codex":[0.9991041,0.0002443671,0.00004312298,0.0001971948,0.0003366537,0.00007447445],"domain_scores_gemma":[0.9978735,0.001591908,0.0001768799,0.00007722124,0.0002391912,0.00004124007],"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":[0.000104091,0.0000605059,0.0007638613,0.0001443736,0.00008810947,0.0001739028,0.0001459063,0.8130926,0.003410168,0.07728773,0.0007683359,0.1039605],"study_design_scores_gemma":[0.000008452538,0.00001430801,0.00009485802,0.000008906367,0.00000928777,0.00002546142,0.000005389968,0.9810016,0.0005775252,0.0177992,0.0004446332,0.00001050627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001573213,0.00009562375,0.9976125,0.00007197986,0.000007638399,0.00001254086,0.00002166163,0.00009635278,0.0005084801],"genre_scores_gemma":[0.5436268,0.0006304872,0.4514941,0.0002368814,0.0001100408,0.0002897464,0.0002873981,0.0001250617,0.003199514],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008153357,"threshold_uncertainty_score":0.01621181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04531098738074615,"score_gpt":0.308287998250367,"score_spread":0.2629770108696208,"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."}}