{"id":"W2801277833","doi":"10.4271/08-07-01-0002","title":"Design, Analysis, and Optimization of a Multi-Speed Powertrain for Class-7 Electric Trucks","year":2018,"lang":"en","type":"article","venue":"SAE International journal of alternative powertrains","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Powertrain; Truck; Automotive engineering; Class (philosophy); Computer science; 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":[0.0001910635,0.000272394,0.0002319125,0.0002977159,0.0002293206,0.0004295019,0.0004436792,0.0002554508,0.001994395],"category_scores_gemma":[0.0002456595,0.0002167405,0.0003247583,0.0002052302,0.0001696595,0.0002935819,0.0002265579,0.0002371618,0.0002677865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000416058,"about_ca_system_score_gemma":0.0006207115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001958662,"about_ca_topic_score_gemma":0.002995533,"domain_scores_codex":[0.9999162,0.00001127119,0.00000333401,0.00001237534,0.00004292345,0.00001387292],"domain_scores_gemma":[0.9999288,0.00001920894,0.00001299672,0.000005906664,0.00002609117,0.000006917612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007771725,0.0000925375,0.001509351,0.0001215819,0.00003234285,0.0001025177,0.00005364313,0.882064,0.04715636,0.002302329,0.0004771162,0.0660104],"study_design_scores_gemma":[0.0000162137,0.0002587504,0.001509519,0.000006650475,0.00001951669,0.00003838759,0.00006134583,0.9828378,0.01178811,0.000593578,0.002860579,0.000009686632],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4902624,0.0002993393,0.4846238,0.0001644178,0.00002609847,0.000212756,0.000131529,0.0003345148,0.02394511],"genre_scores_gemma":[0.943453,0.000171049,0.05092565,0.00001158331,0.000005030671,0.0001291787,0.0001114332,0.00004962173,0.005143265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001994395,"threshold_uncertainty_score":0.006671965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02191516836806946,"score_gpt":0.2862115296059154,"score_spread":0.2642963612378459,"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."}}