{"id":"W4387128349","doi":"10.1016/j.automatica.2023.111301","title":"Event-triggered risk-sensitive smoothing for linear Gaussian systems","year":2023,"lang":"en","type":"article","venue":"Automatica","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Smoothing; Gaussian; Measure (data warehouse); Event (particle physics); Linear system; Mathematics; Property (philosophy); Algorithm; Computer science; State (computer science); Gaussian random field; Gaussian process; Mathematical optimization; Applied mathematics; Data mining; Statistics; Mathematical analysis","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":[0.003737724,0.001104176,0.001527516,0.0005784906,0.0003920195,0.001290705,0.001174003,0.001045132,0.001817299],"category_scores_gemma":[0.01305311,0.0006577615,0.0008104753,0.000628524,0.001091541,0.001178313,0.001664427,0.002029943,0.0002609643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009511576,"about_ca_system_score_gemma":0.001241089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005652847,"about_ca_topic_score_gemma":0.002994947,"domain_scores_codex":[0.99867,0.0004487177,0.00007385926,0.0003008156,0.0002960855,0.000210605],"domain_scores_gemma":[0.9942377,0.004130868,0.0004526667,0.0003997678,0.0005930203,0.0001861365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003955506,0.00006672097,0.0008616555,0.0001489068,0.0001035867,0.0001507504,0.0001362235,0.9104521,0.003721297,0.04412867,0.00118132,0.03865316],"study_design_scores_gemma":[0.000004429155,0.00001528485,0.0001497243,0.000003560077,0.000006312129,0.0000109924,0.000003130493,0.9921955,0.0003379197,0.007158916,0.0001084763,0.000005830832],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01714025,0.0002512053,0.9815244,0.0001476692,0.00004241378,0.0000167195,0.00004184048,0.0002674576,0.0005679424],"genre_scores_gemma":[0.9512017,0.0003671116,0.04415771,0.00009606735,0.00008026911,0.00004649281,0.0001669868,0.0001012663,0.00378232],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005652847,"threshold_uncertainty_score":0.01976717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01032985890296413,"score_gpt":0.2436140301439426,"score_spread":0.2332841712409785,"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."}}