{"id":"W2898923096","doi":"10.1109/tvt.2018.2861902","title":"Low-Emission Maximum-Efficiency Tracking of an Intelligent Bi-Fuel Hydrogen–Gasoline Generator for HEV Applications","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Combustion Engine Technologies","field":"Chemical Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Internal combustion engine; Gasoline; Nonlinear system; Automotive engineering; Combustion; Power (physics); Control theory (sociology); Engineering; Computer science; Physics; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001152836,0.0002969308,0.0003474472,0.0006958225,0.0001906332,0.000008938202,0.0005895222,0.0005411737,0.00004437964],"category_scores_gemma":[0.00004773954,0.0003002657,0.0001590569,0.001121812,0.0003560628,0.0001229392,0.000007437697,0.0004664106,0.00002678281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001318281,"about_ca_system_score_gemma":0.00003676846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002607875,"about_ca_topic_score_gemma":0.00000416784,"domain_scores_codex":[0.9982913,0.00001284454,0.000524256,0.0005490378,0.000190467,0.0004321383],"domain_scores_gemma":[0.9985014,0.00006496015,0.0001421078,0.0009047721,0.0002984544,0.00008836044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003092158,0.000460871,0.000001295662,0.00007186184,0.00004459296,0.000002018222,0.00003411438,0.2141325,0.6799346,0.00174254,0.000005747594,0.1035389],"study_design_scores_gemma":[0.0003520227,0.0003397601,4.391964e-7,0.00004405126,0.0000404566,0.00002179175,0.0001026823,0.2168806,0.7769254,0.002685543,0.002378264,0.0002288777],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1632726,0.0001415256,0.833993,0.0002226294,0.0001287438,0.000595479,0.00004317131,0.001581442,0.0000215221],"genre_scores_gemma":[0.9565567,0.0000641826,0.04254332,0.00002730442,0.00006597117,0.000596637,0.00001228558,0.00007014687,0.00006342903],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7932842,"threshold_uncertainty_score":0.9999449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01406455188819999,"score_gpt":0.2669722803863002,"score_spread":0.2529077284981002,"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."}}