{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006627234,0.0002973689,0.0003466076,0.0001392622,0.0001588112,0.0001819421,0.0004052006,0.0005358729,0.00005940662],"category_scores_gemma":[0.0002766202,0.0002907614,0.0001038387,0.0003831,0.00005492545,0.0001299982,0.00002443428,0.00185247,0.000006746991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001979331,"about_ca_system_score_gemma":0.00009872964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006149384,"about_ca_topic_score_gemma":0.00001907189,"domain_scores_codex":[0.9978862,0.00002331082,0.0003275256,0.0003988765,0.0005359926,0.0008280954],"domain_scores_gemma":[0.9987333,0.0003625368,0.00002959635,0.0004496066,0.0001271984,0.0002977805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008654639,0.00004173637,0.0002205364,0.0002660365,0.000155684,0.000008787408,0.0000355666,0.304885,0.6898012,0.00002710022,0.0002529475,0.004218838],"study_design_scores_gemma":[0.002693823,0.0000514365,0.00001375747,0.00005133856,0.00002180458,0.00003574225,0.00009452985,0.9388338,0.05523791,0.00001139124,0.002606749,0.0003477594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8501543,0.0003170713,0.137841,0.0002275651,0.001361826,0.003135506,0.0001940342,0.001914781,0.004853878],"genre_scores_gemma":[0.9971761,0.000006855337,0.0003591456,0.000002547707,0.0005073405,0.001447162,0.00002931396,0.0001185681,0.0003529802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6345633,"threshold_uncertainty_score":0.9999545,"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."}}