{"id":"W2523593693","doi":"10.1016/j.jprocont.2016.09.008","title":"Characteristics-based model predictive control of selective catalytic reduction in diesel-powered vehicles","year":2016,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Selective catalytic reduction; NOx; Diesel engine; Diesel fuel; Model predictive control; Diesel exhaust fluid; Control theory (sociology); Ammonia; Exhaust gas recirculation; Controller (irrigation); Automotive engineering; Environmental science; Computer science; Diesel exhaust; Engineering; Exhaust gas; Chemistry; Catalysis; Waste management; Control (management)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004216579,0.0005753608,0.0007668531,0.0004235737,0.000379416,0.001030061,0.0008121576,0.0005907164,0.0008172102],"category_scores_gemma":[0.001026502,0.0004509138,0.0004121955,0.0003606039,0.000467744,0.0005440089,0.0006154686,0.0005928046,0.0001882874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006413189,"about_ca_system_score_gemma":0.0006625854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0106062,"about_ca_topic_score_gemma":0.007618878,"domain_scores_codex":[0.9998529,0.00002896194,0.000007190136,0.00002845642,0.00005516079,0.00002729145],"domain_scores_gemma":[0.9996152,0.0001688805,0.00006078215,0.00002260711,0.0001199778,0.00001257764],"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":[0.00007845193,0.00002613981,0.0002226257,0.00003705849,0.00001922687,0.00002472796,0.00002611793,0.9845288,0.002454486,0.001270085,0.0002401139,0.01107212],"study_design_scores_gemma":[0.000004060267,0.0000140482,0.0001040787,0.000001351372,0.000002912415,0.000002204453,0.000002569519,0.9990706,0.0004659352,0.0002453176,0.00008503553,0.000001991054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3094838,0.0009576286,0.6720763,0.0005929386,0.0002266826,0.00009697465,0.0001188856,0.0006023869,0.01584444],"genre_scores_gemma":[0.9969403,0.00007399767,0.002050878,0.00001731071,0.000009050494,0.00001961276,0.00002245535,0.00001399769,0.00085245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0106062,"threshold_uncertainty_score":0.02108896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004908291310535237,"score_gpt":0.2158023290570929,"score_spread":0.2108940377465576,"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."}}