{"id":"W2481410538","doi":"10.1109/acc.2016.7525579","title":"Novel Exergy-wise predictive control of Internal Combustion Engines","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Combustion Engine Technologies","field":"Chemical Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Alberta; National Science Foundation","keywords":"Exergy; Exergy efficiency; Combustion; Second law of thermodynamics; Internal combustion engine; Process engineering; Ignition system; Model predictive control; SPARK (programming language); Environmental science; Computer science; Automotive engineering; Engineering; Control (management); Thermodynamics; Chemistry; Aerospace engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.000363988,0.0005508673,0.0005689605,0.0002274177,0.0002544614,0.00073557,0.0008748652,0.0003452405,0.0007569398],"category_scores_gemma":[0.0004069523,0.0002467012,0.0003101997,0.000217759,0.000434359,0.0003482452,0.0004740983,0.0006119128,0.0001361015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000342828,"about_ca_system_score_gemma":0.0005329316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003184791,"about_ca_topic_score_gemma":0.002931236,"domain_scores_codex":[0.9998134,0.00002246997,0.000007763223,0.00003430001,0.00009689439,0.00002528615],"domain_scores_gemma":[0.9998882,0.00004042942,0.000020454,0.000008146411,0.0000356349,0.000007089114],"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":[0.0001010644,0.00007662956,0.0002905545,0.000134939,0.00003488898,0.0001424957,0.00007527321,0.8945057,0.02064901,0.01111195,0.0008076662,0.07206976],"study_design_scores_gemma":[0.000005036996,0.00003042885,0.00006495785,0.000002814261,0.000004135611,0.000008274089,0.000002019611,0.9975867,0.001236637,0.0006319207,0.0004243254,0.000002787996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03374208,0.0005368124,0.9558731,0.0001204317,0.0001201282,0.00004899042,0.00003114501,0.000437022,0.009090368],"genre_scores_gemma":[0.9690363,0.0002615504,0.02791746,0.00003605847,0.00003343289,0.00005876386,0.00003252098,0.00001756297,0.002606445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003184791,"threshold_uncertainty_score":0.006332517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007931624572435916,"score_gpt":0.2176116212260168,"score_spread":0.2096799966535809,"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."}}