{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001670361,0.00007868656,0.0001750225,0.0001062263,0.000009357334,0.000005103353,0.00009537541,0.00004181155,0.0000014666],"category_scores_gemma":[0.00006284572,0.00005201508,0.00002351682,0.00004797711,0.00003479467,0.0001461602,0.000003359254,0.0001275972,6.496199e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002624957,"about_ca_system_score_gemma":0.00007650928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001303718,"about_ca_topic_score_gemma":0.00003330415,"domain_scores_codex":[0.9995078,0.000006775347,0.0002107313,0.00004898163,0.00008219955,0.0001435088],"domain_scores_gemma":[0.9996343,0.00007499962,0.00004729354,0.00006967565,0.00006587132,0.0001078139],"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":[0.000004829573,0.00000141333,0.0002356109,0.00003541187,0.00002907495,0.00000153058,0.0004016271,0.9713748,0.0260542,0.0007241539,0.000001144153,0.001136242],"study_design_scores_gemma":[0.0006147826,0.00002167085,0.001221523,0.0002035944,0.000009987593,0.00002078691,0.00005095845,0.9954137,0.002295126,0.00007141211,0.0000109078,0.00006552961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7825775,0.01235823,0.2033018,0.001004609,0.0001247631,0.0001936985,0.00001408738,0.00002515634,0.0004001832],"genre_scores_gemma":[0.9986521,0.0003675284,0.00092036,0.000001857682,0.0000384835,0.000004169569,5.329331e-8,0.00001419439,0.00000127723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2160746,"threshold_uncertainty_score":0.2121113,"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."}}