{"id":"W2043300714","doi":"10.1016/j.jprocont.2003.09.002","title":"Performance assessment using a model predictive control benchmark","year":2003,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"International Council for Canadian Studies","keywords":"Model predictive control; Benchmark (surveying); Identification (biology); Computer science; Control (management); Engineering; Control engineering; Reliability engineering; Artificial intelligence","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":[0.0004645625,0.0002180736,0.0005069558,0.0001679707,0.00008359632,0.00004186199,0.000170562,0.00009613016,0.00001404063],"category_scores_gemma":[0.0001072984,0.0001937623,0.0001084377,0.0001718994,0.00002413362,0.0008756266,0.000002951485,0.0003355663,0.000001079605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002959217,"about_ca_system_score_gemma":0.0002262959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.02495e-7,"about_ca_topic_score_gemma":3.417613e-7,"domain_scores_codex":[0.9983677,0.00006394747,0.0007187304,0.0001230637,0.0004157193,0.0003108747],"domain_scores_gemma":[0.9986617,0.0000726284,0.0004201375,0.0001346193,0.0005775001,0.0001334009],"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":[0.00008925492,0.00002883423,0.00247219,0.00008345515,0.0001799116,0.000005979898,0.00009919541,0.992245,0.004308245,0.0001142237,0.00001754375,0.0003562032],"study_design_scores_gemma":[0.005321583,0.0001380026,0.0002993982,0.0001240799,0.0001573438,0.00009467561,0.00007998562,0.9930445,0.0002205626,0.0002958977,0.00004384323,0.0001801267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07218575,0.0006544014,0.9241687,0.00001935861,0.0003295215,0.0003482535,0.00001014923,0.00005047756,0.002233367],"genre_scores_gemma":[0.9912245,0.00003991443,0.008442371,0.00005419076,0.0001561256,0.00002443738,5.067279e-7,0.00004273964,0.00001526178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9190387,"threshold_uncertainty_score":0.7901399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007080006964876396,"score_gpt":0.243336640966209,"score_spread":0.2362566340013326,"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."}}