{"id":"W4287595676","doi":"10.1109/isie51582.2022.9831502","title":"Loss Comparison of Electric Vehicle Fuel Cell Integration Methods","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Drivetrain; Inverter; Dual (grammatical number); Automotive engineering; Harmonics; Driving cycle; Voltage source inverter; Voltage; Electric vehicle; Computer science; Power (physics); Engineering; Electrical engineering; Torque; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007759409,0.0003453876,0.0004768317,0.0005043382,0.0002050941,0.00005951077,0.001043077,0.0002386923,0.000334789],"category_scores_gemma":[0.00007518607,0.000388495,0.0002056664,0.001205114,0.00004052552,0.0001598871,0.0001253368,0.002173591,0.00002411627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001555888,"about_ca_system_score_gemma":0.0001612133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005530164,"about_ca_topic_score_gemma":0.0000100786,"domain_scores_codex":[0.9970676,0.000220251,0.00080864,0.0004383735,0.0008527139,0.0006123757],"domain_scores_gemma":[0.9987858,0.0003376069,0.0002950173,0.0003855286,0.0001206583,0.00007537518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006264269,0.0009167083,0.002080627,0.00004310034,0.0004385374,0.00001597587,0.0003903071,0.1767071,0.7009001,0.01846845,0.02663865,0.07277398],"study_design_scores_gemma":[0.001381216,0.001294842,0.00004457043,0.00001240011,0.00005679737,0.00001784517,0.0001411079,0.230369,0.7308546,0.001239359,0.03415766,0.0004305635],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9612793,0.003530927,0.008458844,0.001323727,0.004874516,0.0009005573,0.0001343982,0.001115256,0.01838246],"genre_scores_gemma":[0.9977198,0.0007135521,0.0002827034,0.00007361444,0.0003460628,0.0001743994,0.0001034817,0.00007288516,0.0005134891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07234342,"threshold_uncertainty_score":0.9998567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02574692428326547,"score_gpt":0.2918162822024525,"score_spread":0.266069357919187,"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."}}