{"id":"W2316854872","doi":"10.1021/ie5009585","title":"A Moving Window Formulation for Recursive Bayesian State Estimation of Systems with Irregularly Sampled and Variable Delays in Measurements","year":2014,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Else Kröner-Fresenius-Stiftung; University of Alberta; Western Canada Research Grid; Compute Canada","keywords":"Estimator; Benchmark (surveying); Window (computing); Computer science; Variable (mathematics); State variable; State (computer science); Algorithm; Process (computing); Control theory (sociology); Mathematical optimization; Mathematics; Statistics; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.001402154,0.000146761,0.0002648625,0.0001523489,0.00004819222,0.00008097396,0.0001015707,0.0001769152,0.000002531806],"category_scores_gemma":[0.0005046312,0.0001508761,0.00002153953,0.0003800577,0.00001390549,0.0001570679,0.00001420247,0.0003185371,3.433105e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002262645,"about_ca_system_score_gemma":0.00004916733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002022046,"about_ca_topic_score_gemma":0.000007277019,"domain_scores_codex":[0.9986674,0.00004184587,0.0003499479,0.0001943512,0.0003910431,0.0003553828],"domain_scores_gemma":[0.999257,0.0002570114,0.00004929775,0.0001762822,0.0001642853,0.00009612856],"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":[0.00008639049,0.000007627961,0.00022105,0.0004002287,0.00003903431,3.669746e-7,0.00008039481,0.6928535,0.3049857,0.00004180618,0.00001445611,0.001269423],"study_design_scores_gemma":[0.002188151,0.00007548637,0.00009348196,0.0004522955,0.000008694318,0.000006037008,0.00005639254,0.9181927,0.07851691,0.00003968406,0.0002277789,0.0001423374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8331029,0.0001321258,0.1647356,0.00001760596,0.0002158751,0.001276253,0.00002691208,0.0001446884,0.0003479784],"genre_scores_gemma":[0.9991996,0.000001597454,0.0003773562,3.661595e-7,0.0001174229,0.0002042534,0.00001731262,0.00003646239,0.00004565069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2264688,"threshold_uncertainty_score":0.6152549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04448977499918805,"score_gpt":0.2734608223271738,"score_spread":0.2289710473279857,"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."}}