{"id":"W2999881469","doi":"10.1016/j.apenergy.2019.114471","title":"Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling","year":2020,"lang":"en","type":"article","venue":"Applied Energy","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":85,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Nanyang Technological University","keywords":"Energy consumption; Headway; Acceleration; Energy (signal processing); Computer science; Simulation; Efficient energy use; Automotive engineering; Energy management; Engineering","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":[0.00003086475,0.0001293445,0.0002322121,0.00007914002,0.00006018294,0.00001439499,0.00004428202,0.0000492057,0.00007317451],"category_scores_gemma":[0.00000106514,0.0001134609,0.00003624006,0.0004268512,0.00003304925,0.00009628048,0.00002599366,0.00005572059,6.773619e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001509787,"about_ca_system_score_gemma":0.000009002699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009472719,"about_ca_topic_score_gemma":0.00001707351,"domain_scores_codex":[0.9993523,0.000006471824,0.0001929955,0.000166917,0.0001332395,0.0001480821],"domain_scores_gemma":[0.9997204,0.000008082588,0.00003272033,0.0001032904,0.00003081651,0.0001046863],"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":[0.00006766285,0.0000249738,0.001753952,0.00004190498,0.0003383188,0.000002924328,0.0008680804,0.8245133,0.1652114,0.001262702,0.0000673032,0.005847434],"study_design_scores_gemma":[0.0002985339,0.00005434552,0.0005360111,0.00001205903,0.000186392,0.000001406211,0.0001555877,0.969303,0.02828655,0.00001397066,0.001032536,0.0001196842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.881753,0.0001369847,0.1173902,0.00002505963,0.00001108346,0.00002794396,0.00001902623,0.0001598495,0.0004768827],"genre_scores_gemma":[0.9980078,0.0002604299,0.001474923,0.00002531056,0.00003513777,0.0000338784,0.00008065,0.00002506413,0.00005674727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1447896,"threshold_uncertainty_score":0.4626801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006995649746421736,"score_gpt":0.1725407218759127,"score_spread":0.165545072129491,"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."}}