{"id":"W3123920892","doi":"","title":"Performance analysis of power train electric vehicle transmission two speed with reverse engineering method","year":2020,"lang":"en","type":"article","venue":"Mechanical Engineering Research","topic":"Magnetic Bearings and Levitation Dynamics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Automotive engineering; Transmission (telecommunications); Reverse engineering; Power transmission; Power (physics); Engineering; Computer science; Electrical engineering; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008358203,0.0002020478,0.0004181103,0.0005517011,0.00003718765,0.00003044586,0.0002820416,0.0001021911,0.0002303982],"category_scores_gemma":[0.0001461972,0.0001887888,0.0001208287,0.003648701,0.00001170711,0.0000999726,0.00003494721,0.000707733,0.000008420141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006873885,"about_ca_system_score_gemma":0.00002661311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001498675,"about_ca_topic_score_gemma":9.006945e-7,"domain_scores_codex":[0.9981623,0.00004120603,0.0003478788,0.0002967807,0.000634622,0.0005171698],"domain_scores_gemma":[0.9990466,0.0002767415,0.00002203617,0.0002339372,0.000124477,0.0002962178],"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":[0.00002801861,0.00001299753,0.00002297219,0.0001755795,0.0001742749,0.000006052973,0.0002227368,0.6945184,0.2998705,0.0005870222,0.00001583721,0.004365555],"study_design_scores_gemma":[0.0004373836,0.0002720272,0.00124636,0.00004647125,0.0001021804,0.000002112581,0.00002566011,0.9797831,0.01701623,0.000002638415,0.0008562176,0.0002096687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5849753,0.00009409239,0.413877,0.0001970059,0.00003925819,0.0002375728,0.000006188616,0.0002825886,0.0002909865],"genre_scores_gemma":[0.9688864,0.00004778069,0.03090345,0.00001571074,0.00003471076,0.00001222497,0.000007053289,0.00006089981,0.00003178879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3839111,"threshold_uncertainty_score":0.7698583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02401741101597616,"score_gpt":0.2826268990332857,"score_spread":0.2586094880173095,"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."}}