{"id":"W4405567833","doi":"10.1155/atr/3058575","title":"Energy Consumption Prediction Model for Electric Buses Considering Actual Quantifiable Features","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Energy consumption; Energy (signal processing); Electric energy consumption; Computer science; Consumption (sociology); Automotive engineering; Electric energy; Environmental science; Engineering; Mathematics; Statistics; Physics; Electrical engineering","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":[0.0002459738,0.0006611613,0.0003853516,0.0003123542,0.000258634,0.0004816063,0.0006410958,0.0004012266,0.0009896848],"category_scores_gemma":[0.000499882,0.0002541942,0.0004778365,0.0004642235,0.0001630237,0.0007391787,0.0002734079,0.0005343354,0.0002806211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005423591,"about_ca_system_score_gemma":0.0007619576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03182816,"about_ca_topic_score_gemma":0.0228197,"domain_scores_codex":[0.9998593,0.00001739743,0.000009879913,0.00005626037,0.00003411323,0.00002299411],"domain_scores_gemma":[0.9998584,0.00004296614,0.0000179535,0.00001381827,0.00006077879,0.000006060801],"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":[0.00003567462,0.00003706738,0.00522687,0.00002124279,0.00001792743,0.00005583253,0.0000257326,0.9734192,0.001273555,0.0007501483,0.0005029326,0.01863378],"study_design_scores_gemma":[0.000001293791,0.000005676574,0.0007472363,7.959376e-7,0.000003727208,0.000004044017,0.000004834408,0.9987649,0.0001849862,0.000176457,0.0001039126,0.000002138229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4694813,0.0003435267,0.5209981,0.0003549481,0.00006753959,0.0000677408,0.0007828901,0.001291436,0.006612563],"genre_scores_gemma":[0.9853651,0.0001508567,0.01071153,0.00001633774,0.00001150662,0.00006483811,0.0005663452,0.00002203125,0.003091462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03182816,"threshold_uncertainty_score":0.06328583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01532993453810155,"score_gpt":0.2495701286456227,"score_spread":0.2342401941075211,"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."}}