{"id":"W2896801084","doi":"10.1155/2018/1074817","title":"Exploring the Energy Efficiency of Electric Vehicles with Driving Behavioral Data from a Field Test and Questionnaire","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Tsinghua University; University of Oxford; National Natural Science Foundation of China; Beijing Jiaotong University; National Natural Science Foundation of China-Zhejiang Joint Fund for the Integration of Industrialization and Informatization; China Scholarship Council","keywords":"Beijing; Energy consumption; Transport engineering; Popularity; Driving test; Consumption (sociology); Engineering; Simulation; Environmental economics; Computer science; Psychology; Geography; China","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":[0.00006617972,0.0000601159,0.0000907621,0.00004816832,0.0000537946,0.00000821416,0.0001146603,0.00001661185,0.000003797503],"category_scores_gemma":[0.00001058846,0.0000400224,0.00001274245,0.0001514519,0.00002072952,0.0005463957,0.000002032692,0.00009727654,1.054483e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007787992,"about_ca_system_score_gemma":0.00001600608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003000916,"about_ca_topic_score_gemma":0.0001657785,"domain_scores_codex":[0.999532,0.000005018715,0.0002130628,0.0000599373,0.0001171339,0.00007280592],"domain_scores_gemma":[0.9996275,0.00006256887,0.00009089427,0.0001154462,0.00007321501,0.00003034293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001928647,0.0001347079,0.1570785,0.00007164935,0.00005408646,0.00002179683,0.005048131,0.05421539,0.4616264,0.00006185847,0.0000522413,0.3214424],"study_design_scores_gemma":[0.0005436571,0.0007667968,0.8316095,0.0005663589,0.00007737584,0.0000135792,0.0003651157,0.01588059,0.1496907,0.00006022866,0.0003002522,0.000125875],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865842,0.0004018503,0.01284969,0.00003161593,0.00008811754,0.00002099,0.000005028081,0.00001150141,0.000006981697],"genre_scores_gemma":[0.997645,0.0005841413,0.001648994,0.000005485282,0.0001004843,0.000001356102,0.000004770268,0.000008260693,0.00000154322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.674531,"threshold_uncertainty_score":0.1632066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02290357647119887,"score_gpt":0.2470494432559768,"score_spread":0.224145866784778,"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."}}