{"id":"W1995013804","doi":"10.1002/cjce.5450800519","title":"Assessing the Performance of Model Predictive Controllers","year":2002,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Shell (Canada); University of Alberta","funders":"","keywords":"Model predictive control; Metric (unit); Computer science; Statistic; Control theory (sociology); Function (biology); Measure (data warehouse); Value (mathematics); Relevance (law); Performance metric; Mathematical optimization; Control (management); Mathematics; Engineering; Artificial intelligence; Machine learning; Data mining; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0001844642,0.0001010594,0.0001855567,0.00007087605,0.00004644215,0.00003521765,0.0002482834,0.00005085684,0.000008195177],"category_scores_gemma":[0.00009913386,0.00006651676,0.00006499067,0.0001333641,0.00004616208,0.0002525075,0.000004558194,0.0003031949,7.888696e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001864494,"about_ca_system_score_gemma":0.00004775711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002225079,"about_ca_topic_score_gemma":0.000007811453,"domain_scores_codex":[0.9992932,0.000008327916,0.0003132889,0.0000404926,0.0001417203,0.0002029407],"domain_scores_gemma":[0.999468,0.00009070926,0.00008906442,0.0001255625,0.00009461134,0.0001320501],"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":[0.00000164348,8.658923e-7,0.0000251461,0.00001635442,0.00004286741,0.000001373139,0.0002302174,0.9810506,0.01817711,0.00005243254,0.00006679959,0.0003345795],"study_design_scores_gemma":[0.0002655294,0.000009572478,0.00002223283,0.00007823836,0.00002501131,0.00003576349,0.00001728046,0.9907292,0.008690163,0.0000135777,0.00004851408,0.00006494874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.879795,0.002353343,0.1157922,0.0003536787,0.0003968443,0.0001902102,0.000006189084,0.00004262015,0.001069839],"genre_scores_gemma":[0.9990346,0.00000988894,0.0007867126,0.0000157904,0.0001162073,0.000003260602,2.435677e-7,0.00002335732,0.000009929415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1192396,"threshold_uncertainty_score":0.2712475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008935424065567809,"score_gpt":0.1842114629014157,"score_spread":0.1752760388358479,"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."}}