{"id":"W4416410499","doi":"10.1016/j.energy.2025.139256","title":"Unraveling energy demand in battery electric bus operations through an explainable machine learning approach using real-world cold-climate data","year":2025,"lang":"en","type":"article","venue":"Energy","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données","keywords":"Battery (electricity); Energy consumption; Software deployment; Regenerative brake; Public transport; Energy (signal processing); Diesel fuel; Electric vehicle; Efficient energy use","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023896,0.0002468389,0.0002954848,0.0003715438,0.0002794901,0.0001235557,0.0003923411,0.0001207199,0.00002925792],"category_scores_gemma":[0.000018354,0.0002576467,0.00002938653,0.001448172,0.00001327016,0.0006995251,0.0001438832,0.0003280952,6.043753e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001902529,"about_ca_system_score_gemma":0.00007013806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006191859,"about_ca_topic_score_gemma":0.002562337,"domain_scores_codex":[0.9984034,0.0001139637,0.0003679324,0.000423194,0.0001322328,0.0005592701],"domain_scores_gemma":[0.9993278,0.00005021946,0.00003238076,0.0005013526,0.00003253151,0.00005576637],"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":[0.000005882659,0.00002987129,0.0007170428,0.00004035359,0.00003578842,0.00001030082,0.00008813248,0.9534665,0.02002,0.02321618,0.0001634791,0.002206519],"study_design_scores_gemma":[0.0003068462,0.00001957073,0.0001965176,0.00004149792,0.00002454667,0.000008480565,0.00004592196,0.9767988,0.01176215,0.0002444022,0.01029232,0.0002589363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2484501,0.005513947,0.7181869,0.00007303608,0.0003993846,0.0001628178,0.00003272324,0.000595919,0.02658512],"genre_scores_gemma":[0.9872243,0.002510946,0.008877577,0.0001817069,0.000156792,0.00002013137,0.0004186777,0.00005821909,0.000551698],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7387741,"threshold_uncertainty_score":0.9999876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0169112166194785,"score_gpt":0.2453694718078473,"score_spread":0.2284582551883688,"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."}}