{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001486883,0.0001270323,0.0001870764,0.00005132084,0.00009374985,0.0001048588,0.0000843298,0.00008986114,0.00001656549],"category_scores_gemma":[0.000314285,0.0001245686,0.00006250323,0.0001790807,0.00001085903,0.0004281552,0.00001182954,0.0001859539,0.000005616646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000117993,"about_ca_system_score_gemma":0.0001072294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.062601e-7,"about_ca_topic_score_gemma":0.000003198037,"domain_scores_codex":[0.9991572,0.00001720777,0.0003396509,0.0001258718,0.0001436035,0.0002164424],"domain_scores_gemma":[0.9993581,0.00008934737,0.0000935032,0.0001109409,0.0002569724,0.00009114847],"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":[0.00002360317,0.00004015704,0.00007729804,0.0003031669,0.0001291287,0.00001568655,0.0001723198,0.8706564,0.08851728,0.0002183506,0.0009555346,0.03889107],"study_design_scores_gemma":[0.001771319,0.00007521307,0.0004749558,0.0002807743,0.0001048659,0.0004146039,0.0003641968,0.9111547,0.07997013,0.004025671,0.0009768953,0.0003866279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01989028,0.0008534172,0.9777247,0.0001740421,0.0004625623,0.0001391895,0.00000293052,0.0001115019,0.0006413613],"genre_scores_gemma":[0.915359,0.00011142,0.08373155,0.00003684166,0.0005635601,0.00002627803,0.00001567288,0.00004534758,0.0001102793],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8954688,"threshold_uncertainty_score":0.5079762,"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."}}