{"id":"W2328989907","doi":"10.1002/cjce.22500","title":"Perspectives and challenges in performance assessment of model predictive control","year":2016,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Setpoint; Benchmark (surveying); Model predictive control; Process (computing); Computer science; Task (project management); Reliability engineering; Control (management); Process control; Engineering; Systems engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02294524,0.001467764,0.001729114,0.002092793,0.0004846682,0.005030786,0.001914818,0.00181327,0.001425179],"category_scores_gemma":[0.03489756,0.000376316,0.000784799,0.001674426,0.001781941,0.003469142,0.001754408,0.002143926,0.0003112437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001519815,"about_ca_system_score_gemma":0.001376916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002047485,"about_ca_topic_score_gemma":0.0007714652,"domain_scores_codex":[0.987826,0.00772042,0.0004738409,0.0007290564,0.002917796,0.0003327524],"domain_scores_gemma":[0.976225,0.01742717,0.001213021,0.001084065,0.003783856,0.0002669343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001786191,0.0001796828,0.00241145,0.001034242,0.0002501689,0.0001075018,0.000202405,0.5781332,0.002729668,0.1207412,0.002539639,0.2914923],"study_design_scores_gemma":[0.00002033156,0.0003281081,0.001406592,0.0003977459,0.00004313527,0.00007643107,0.0002147077,0.8779864,0.002798304,0.1105749,0.006093587,0.00005968977],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.03342947,0.03714838,0.9009312,0.00730747,0.0004172757,0.0001160058,0.0001246546,0.0005998516,0.01992564],"genre_scores_gemma":[0.8945953,0.008220136,0.09459289,0.0002875458,0.0008641156,0.0001359082,0.00011698,0.0001238754,0.001063392],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.02294524,"threshold_uncertainty_score":0.1213475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01027016525536161,"score_gpt":0.1920822121689248,"score_spread":0.1818120469135631,"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."}}