{"id":"W3214132239","doi":"10.14288/1.0401124","title":"Improved regenerative braking in electric vehicles through switch selection optimization","year":2021,"lang":"en","type":"article","venue":"Open Collections","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Regenerative brake; Engine braking; Selection (genetic algorithm); Automotive engineering; Computer science; Engineering; Brake; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009140762,0.0001425076,0.0001930425,0.0001227891,0.0007482538,0.0005700127,0.000176573,0.0001197475,0.0001232827],"category_scores_gemma":[0.00008246086,0.0001651216,0.0000358711,0.00457838,0.00001259746,0.0004557396,0.00006403161,0.0003169305,0.000002858465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003415668,"about_ca_system_score_gemma":0.0001523662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001123463,"about_ca_topic_score_gemma":0.003008896,"domain_scores_codex":[0.999086,0.00004491316,0.0002339603,0.0002649096,0.00008056022,0.0002896052],"domain_scores_gemma":[0.9996107,0.00005665777,0.0000360017,0.0001556192,0.0001179486,0.00002306357],"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":[0.00003058923,0.0001838554,0.0003824407,0.00002826437,0.0001363721,0.00002127428,0.0002326747,0.8526538,0.07094096,0.001087631,0.0661546,0.008147562],"study_design_scores_gemma":[0.0005113779,0.00006542468,0.0001237071,0.00002093517,0.00001692288,0.00005770464,0.0001355231,0.7046331,0.2911074,0.001278533,0.001808763,0.0002406399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06325044,0.002811822,0.5791343,0.0007429839,0.0008228343,0.00212212,0.00001757671,0.002826713,0.3482712],"genre_scores_gemma":[0.9313934,0.001173946,0.03580244,0.00009500141,0.0001026349,0.0004369846,0.00002686035,0.00007096636,0.03089774],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.868143,"threshold_uncertainty_score":0.6733465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01415983775985645,"score_gpt":0.2385919835471047,"score_spread":0.2244321457872482,"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."}}