{"id":"W2082104257","doi":"10.1109/ecce.2010.5618098","title":"Particle swarm optimization for efficient selection of hybrid electric vehicle design parameters","year":2010,"lang":"en","type":"article","venue":"","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Drivetrain; Particle swarm optimization; Electric vehicle; Computer science; Selection (genetic algorithm); Automotive engineering; Torque; Engineering; Power (physics); Artificial intelligence; Algorithm","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":[0.0008345243,0.0008796678,0.000720175,0.0004391866,0.0002858415,0.000541059,0.0003926455,0.0005358913,0.0008529234],"category_scores_gemma":[0.00121858,0.0004535827,0.0003808447,0.0003743382,0.0003524339,0.000409758,0.0003523943,0.0005042268,0.0001961671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003413669,"about_ca_system_score_gemma":0.0005367454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002177783,"about_ca_topic_score_gemma":0.001611135,"domain_scores_codex":[0.9998025,0.00008328674,0.000009780731,0.0000286168,0.00005965334,0.00001606076],"domain_scores_gemma":[0.9997789,0.0001211074,0.00002878791,0.00001353696,0.00004953305,0.000007970349],"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":[0.00003943842,0.00002567952,0.0003661435,0.00005312149,0.00003532312,0.00003575041,0.0000329471,0.954853,0.002636975,0.004562889,0.0005488645,0.03680981],"study_design_scores_gemma":[0.00001246115,0.00002269554,0.0001362886,0.000004558739,0.000006194824,0.000006155022,0.000004340526,0.9977692,0.0004757301,0.001025414,0.0005339178,0.000002959271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01581801,0.000364268,0.9811561,0.00006681681,0.00002924014,0.00003985547,0.00001768621,0.0001329864,0.002375069],"genre_scores_gemma":[0.6451179,0.0007128427,0.3500815,0.00006111651,0.0000575367,0.0003648826,0.0001438792,0.00007666203,0.00338367],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002177783,"threshold_uncertainty_score":0.004413426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01196910721424643,"score_gpt":0.2076549413352156,"score_spread":0.1956858341209692,"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."}}