{"id":"W4200188381","doi":"10.1002/aic.17545","title":"Extended moving horizon estimation for chemical processes under non‐Gaussian noises","year":2021,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaussian; Nonlinear system; Gaussian process; Control theory (sociology); Computer science; Horizon; Applied mathematics; Mathematics; Mathematical optimization; Algorithm; Control (management); Artificial intelligence; Physics","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":[0.001448749,0.0007578532,0.001051779,0.000426031,0.0003657989,0.0007244429,0.0009083435,0.0007870608,0.0009632549],"category_scores_gemma":[0.003332987,0.0004551573,0.0005662685,0.0004796361,0.0006743782,0.0008675519,0.0008079277,0.001124926,0.0001298304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007473881,"about_ca_system_score_gemma":0.0009679976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009565189,"about_ca_topic_score_gemma":0.005335274,"domain_scores_codex":[0.999432,0.0001717793,0.00002680638,0.0001287886,0.0001767542,0.00006375918],"domain_scores_gemma":[0.9988043,0.0007624346,0.0001604288,0.00006606062,0.0001736123,0.00003313387],"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":[0.0000640822,0.00001227481,0.0001806845,0.00004155938,0.00002392826,0.00003435561,0.00002272864,0.9746662,0.001115511,0.006052953,0.000139244,0.01764647],"study_design_scores_gemma":[0.000001443376,0.000007819773,0.00006754357,0.000001355729,0.00000164809,0.000002231917,8.87918e-7,0.9988152,0.000187467,0.0008323046,0.00007964136,0.000002480779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0195608,0.0002813216,0.9790576,0.00009066924,0.00003476056,0.00001401803,0.00002648126,0.0001305193,0.0008038245],"genre_scores_gemma":[0.93536,0.0003683206,0.06154997,0.000053609,0.0000491162,0.00004555685,0.00008696008,0.00002668876,0.002459721],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009565189,"threshold_uncertainty_score":0.01901901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007610956221686264,"score_gpt":0.2387831183265852,"score_spread":0.2311721621048989,"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."}}