{"id":"W2011987043","doi":"10.4271/2014-01-1815","title":"Energy Efficient Routing for Electric Vehicles using Particle Swarm Optimization","year":2014,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Chrysler (Canada)","funders":"","keywords":"Particle swarm optimization; Computer science; Routing (electronic design automation); Multi-swarm optimization; Energy (signal processing); Mathematical optimization; Computer network; Algorithm; Physics; Mathematics","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":[0.0003580434,0.0005910965,0.0005200766,0.0004368915,0.0004187129,0.0007817441,0.0004476876,0.0007398803,0.002250015],"category_scores_gemma":[0.000773389,0.0003497509,0.0005094709,0.0004901918,0.0003956557,0.0005355857,0.0004740949,0.0004698697,0.0003035974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007345302,"about_ca_system_score_gemma":0.0007239432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006659313,"about_ca_topic_score_gemma":0.005038233,"domain_scores_codex":[0.999871,0.00004323345,0.000006232539,0.0000242989,0.00004167295,0.00001351933],"domain_scores_gemma":[0.9998242,0.00009347217,0.00002381844,0.00001225972,0.00003659184,0.000009604614],"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":[0.00001180492,0.00001027466,0.00009405996,0.00001249808,0.000007998489,0.00001578222,0.00001141167,0.9874234,0.0005796386,0.003514627,0.0005342059,0.00778428],"study_design_scores_gemma":[0.000003954784,0.000005559375,0.00002318205,0.000001560742,0.000001359303,0.000002317064,0.000003215986,0.998255,0.00009321143,0.001229214,0.0003801284,0.000001256584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01573382,0.0002228112,0.9751799,0.0002366531,0.00007599815,0.00005831511,0.00004420795,0.0002551741,0.008193184],"genre_scores_gemma":[0.59202,0.0006357479,0.3872218,0.0001276551,0.00007012986,0.0003547289,0.0002634643,0.0001229275,0.01918346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006659313,"threshold_uncertainty_score":0.01324105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01498423640770682,"score_gpt":0.2554483754922992,"score_spread":0.2404641390845924,"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."}}