{"id":"W1983353144","doi":"10.1002/aic.12358","title":"Bayesian method for multirate data synthesis and model calibration","year":2010,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Syncrude (Canada); University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Syncrude","keywords":"Flexibility (engineering); Particle filter; Bayesian probability; Computer science; Process (computing); Monte Carlo method; Calibration; Sampling (signal processing); Filter (signal processing); Soft sensor; Data mining; Noise (video); Algorithm; Artificial intelligence; Mathematics; Statistics","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":[0.0005224823,0.0000713691,0.0001046147,0.00004512081,0.00009130597,0.000102471,0.0001206711,0.00007164074,0.00001512066],"category_scores_gemma":[0.0001213769,0.00006116558,0.00002451005,0.00003162054,0.000006117873,0.0002748887,0.00001328334,0.0002088698,0.000001543692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007820094,"about_ca_system_score_gemma":0.00001323916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007827754,"about_ca_topic_score_gemma":0.0000810136,"domain_scores_codex":[0.9995487,0.00002407576,0.000160709,0.00008709907,0.00006672114,0.000112749],"domain_scores_gemma":[0.9995874,0.00009838889,0.00003273305,0.0001764309,0.00002077614,0.00008429129],"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":[0.00004707454,0.00001814399,0.0001039116,0.00007575693,0.000169141,0.000003559814,0.0003225165,0.09875396,0.6840603,0.0003484451,0.008914424,0.2071828],"study_design_scores_gemma":[0.0002468214,0.000006739586,0.0000216677,0.000006814621,0.00002525809,0.00008668555,0.00003357042,0.9882731,0.005267549,0.0002577362,0.005698018,0.00007605343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009591276,0.00007307337,0.9891543,0.0002422999,0.000462175,0.00009071533,0.00002544174,0.00006379471,0.0002969538],"genre_scores_gemma":[0.9209554,0.00002127127,0.07844771,0.00005550068,0.0003455959,0.0000139297,0.00000267631,0.00002294397,0.0001350288],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9113641,"threshold_uncertainty_score":0.249426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02275302660133552,"score_gpt":0.2809463429528549,"score_spread":0.2581933163515194,"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."}}