{"id":"W2147228477","doi":"10.1109/epec.2009.5420888","title":"Comparative study of hybrid electric vehicle control strategies for improved drivetrain efficiency analysis","year":2009,"lang":"en","type":"article","venue":"","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Drivetrain; Automotive engineering; Control (management); Electric vehicle; Computer science; Energy management; Control engineering; Energy (signal processing); Engineering; Torque; Artificial intelligence","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":[0.0003751579,0.0003648002,0.0003292089,0.0006079702,0.0001369252,0.0004459934,0.0003117268,0.00019759,0.002383684],"category_scores_gemma":[0.0006208982,0.00009607318,0.0002464898,0.0003277249,0.00009571655,0.0002994816,0.0001328119,0.0001140819,0.0001961273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002111013,"about_ca_system_score_gemma":0.0001150585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00173721,"about_ca_topic_score_gemma":0.001800423,"domain_scores_codex":[0.9998661,0.0000305645,0.00001049438,0.00001639303,0.00006134692,0.00001502537],"domain_scores_gemma":[0.9997411,0.0001143235,0.00001861496,0.00001388904,0.0001044852,0.000007586774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001459465,0.0003817103,0.003805083,0.0007021588,0.0003209519,0.0001600719,0.0001660367,0.3684551,0.04736365,0.008838708,0.001458146,0.5668889],"study_design_scores_gemma":[0.0001136096,0.001407871,0.01198069,0.00002957981,0.0001908401,0.000118424,0.0001306365,0.9486525,0.0289479,0.002040212,0.006353,0.00003472043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.659445,0.003710946,0.2949145,0.0001235686,0.00009484395,0.0001603715,0.0001683173,0.0005418234,0.04084055],"genre_scores_gemma":[0.9891896,0.0003114379,0.008065972,0.00001148815,0.000008505363,0.00002885841,0.00006429489,0.00001248479,0.002307346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002383684,"threshold_uncertainty_score":0.007974207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01074295034467759,"score_gpt":0.2489422437431764,"score_spread":0.2381992933984988,"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."}}