{"id":"W2899383815","doi":"","title":"Performance of Wide Band Gap Devices in Electric Vehicles Converters: A Case Study Evaluation","year":2018,"lang":"en","type":"article","venue":"European Conference on Power Electronics and Applications","topic":"Silicon Carbide Semiconductor Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Converters; Silicon carbide; Gallium nitride; Wide-bandgap semiconductor; Work (physics); Battery (electricity); Electronic engineering; Electric vehicle; Materials science; Computer science; Electrical engineering; Automotive engineering; Engineering; Optoelectronics; Voltage; Power (physics); Mechanical engineering; Physics; Nanotechnology","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":[0.0003390359,0.0001313848,0.0001351609,0.0001686886,0.00005594943,0.00003081571,0.0001658068,0.0000318629,0.0000211382],"category_scores_gemma":[0.00001924179,0.0001312216,0.00001421395,0.0003397088,0.00006802241,0.00008412074,0.00002154483,0.0001785404,0.0000179582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005769277,"about_ca_system_score_gemma":0.00004549969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001171159,"about_ca_topic_score_gemma":0.0001008526,"domain_scores_codex":[0.9991533,0.00005635811,0.0002314844,0.0002329647,0.0001210678,0.0002048984],"domain_scores_gemma":[0.9994427,0.00004883454,0.00005448935,0.0003092274,0.0001161317,0.00002862185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006158607,0.0006661724,0.0917073,0.0001296046,0.0002040252,0.00004978028,0.004927912,0.00042643,0.6461353,0.009215982,0.0003457967,0.2461301],"study_design_scores_gemma":[0.005121983,0.006599701,0.1350112,0.0002703063,0.0002762702,0.0004415131,0.01199892,0.5520632,0.2797598,0.001890668,0.004593396,0.001973134],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915056,0.0005188327,0.00005534086,0.00002495223,0.00001667201,0.0005638265,0.000002341423,0.0001242269,0.00718816],"genre_scores_gemma":[0.9996232,0.0002123191,0.00001256718,0.0000244118,0.0000122888,0.00008424291,0.000002359217,0.00002117294,0.000007425479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5516367,"threshold_uncertainty_score":0.5351061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03105744352653945,"score_gpt":0.2687726365018586,"score_spread":0.2377151929753191,"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."}}