{"id":"W4388104230","doi":"10.4271/2023-01-1607","title":"Online Identification of Vehicle Driving Conditions Using Machine-Learned Clusters","year":2023,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Powertrain; Driving cycle; Controller (irrigation); Cluster analysis; Identification (biology); Computer science; Electric vehicle; Automotive engineering; Engineering; Control engineering; Artificial intelligence; Torque; Power (physics)","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":[0.0003665455,0.0009110985,0.0006001367,0.001149659,0.0004786699,0.0007579654,0.00116014,0.0005664391,0.000869558],"category_scores_gemma":[0.002227421,0.0003934925,0.0004616296,0.0006878265,0.0003008959,0.0006863726,0.0006095933,0.0008515648,0.0006199034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000966026,"about_ca_system_score_gemma":0.001080251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02399294,"about_ca_topic_score_gemma":0.02635458,"domain_scores_codex":[0.9995601,0.0000402857,0.00001758261,0.0002243059,0.000098583,0.00005916218],"domain_scores_gemma":[0.9990888,0.0002369047,0.0001289152,0.0001506139,0.000338893,0.0000560206],"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":[0.0003296275,0.0002941819,0.0100019,0.00006773162,0.0001116004,0.00007971288,0.0002334023,0.7104994,0.01191076,0.001157971,0.00246456,0.2628491],"study_design_scores_gemma":[0.000003098927,0.00001363345,0.001531301,0.000001644794,0.000003210105,0.000005790712,0.00001915269,0.9963003,0.001562752,0.0003768164,0.0001753614,0.0000068739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3257287,0.0001689334,0.6649595,0.0001374772,0.00008094049,0.0001561893,0.0006038952,0.005380961,0.002783529],"genre_scores_gemma":[0.9415884,0.00003241217,0.0560773,0.00002525745,0.00001694168,0.000051378,0.0007902203,0.00008548667,0.00133263],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02399294,"threshold_uncertainty_score":0.04770654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0270058363376905,"score_gpt":0.3113069547376284,"score_spread":0.2843011183999379,"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."}}