{"id":"W2782361845","doi":"10.1177/0954407017747372","title":"A hybrid electric vehicle energy optimization strategy by using fueling control in diesel engines","year":2018,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Powertrain; Automotive engineering; Diesel fuel; Fuel efficiency; Energy management; Fuel injection; NOx; Control (management); Diesel engine; Hybrid power; Brake specific fuel consumption; Engineering; Engine control unit; Brake; Computer science; Power (physics); Internal combustion engine; Combustion; Energy (signal processing); Torque","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":[0.0001252423,0.0004653095,0.0003108848,0.0002211765,0.000174739,0.0003807698,0.0003769033,0.0002077939,0.0006884371],"category_scores_gemma":[0.0001018844,0.0001130982,0.000207495,0.0002114543,0.0001390322,0.0002438442,0.0002783797,0.0001731689,0.00009575928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002009994,"about_ca_system_score_gemma":0.0002324547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001871264,"about_ca_topic_score_gemma":0.002363996,"domain_scores_codex":[0.9999534,0.000007906913,0.000002675949,0.00001350102,0.00001541603,0.000007176225],"domain_scores_gemma":[0.9999737,0.000005571773,0.000005893076,0.000002145633,0.000009539024,0.00000323694],"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":[0.0001528851,0.0002266404,0.0008907465,0.0001985868,0.00008124068,0.0002222581,0.00008600183,0.7391993,0.0632812,0.01291467,0.000921459,0.1818249],"study_design_scores_gemma":[0.00001924029,0.0001621168,0.0002935992,0.000004497303,0.00001840927,0.000036291,0.00001274616,0.9907166,0.005883175,0.001024246,0.001821811,0.000007338864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.126521,0.0004705481,0.8599669,0.0001135098,0.00005372939,0.00009834699,0.00002633076,0.0002509612,0.01249863],"genre_scores_gemma":[0.9388043,0.0001826506,0.05604511,0.00003630239,0.00001423331,0.00006138536,0.00003576569,0.00001802784,0.004802227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001871264,"threshold_uncertainty_score":0.00372082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006485007662523152,"score_gpt":0.1930899004876204,"score_spread":0.1866048928250972,"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."}}