{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003933374,0.0003503527,0.0003457725,0.0003388874,0.0003332279,0.000380281,0.0003773588,0.0003488029,0.002002058],"category_scores_gemma":[0.0007062128,0.0001137001,0.0003449134,0.000243841,0.0002066551,0.0003440918,0.0002469954,0.000246347,0.0002092613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002513588,"about_ca_system_score_gemma":0.000271972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004463792,"about_ca_topic_score_gemma":0.001735899,"domain_scores_codex":[0.9997786,0.000071278,0.000007790898,0.00003494437,0.00007953964,0.00002799559],"domain_scores_gemma":[0.9996697,0.000141463,0.00003052596,0.00003059591,0.0001172908,0.0000104984],"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":[0.0007598631,0.0001400726,0.004752218,0.0003463333,0.00006833044,0.000210943,0.0002165543,0.8226551,0.04352333,0.007383144,0.001049992,0.1188941],"study_design_scores_gemma":[0.000006368284,0.0001593744,0.0009877898,0.000003436867,0.00001428713,0.00003085705,0.00002383221,0.9945897,0.003744622,0.0001443837,0.0002897147,0.000005746569],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6250429,0.0004997741,0.351614,0.0001667092,0.00005895355,0.00005103528,0.00005396613,0.0004424963,0.0220702],"genre_scores_gemma":[0.9904774,0.00009098835,0.006463181,0.000007065682,0.00000696093,0.0000162076,0.00003481322,0.00001855958,0.002884762],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004463792,"threshold_uncertainty_score":0.008875608,"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."}}