{"id":"W4406072036","doi":"10.1016/j.geits.2024.100250","title":"Interpretable machine learning models for predicting Ebus battery consumption rates in cold climates with and without diesel auxiliary heating","year":2025,"lang":"en","type":"article","venue":"Green Energy and Intelligent Transportation","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Battery (electricity); Diesel fuel; Consumption (sociology); Environmental science; Automotive engineering; Engineering; Thermodynamics; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001223935,0.0001504623,0.0001839448,0.0002086323,0.00009539982,0.00002666587,0.00005680936,0.00008467278,0.000004301044],"category_scores_gemma":[0.00001248676,0.0001410267,0.00001591292,0.0001383004,0.00006750254,0.0003230053,0.000008952302,0.0001713987,1.013756e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003975159,"about_ca_system_score_gemma":0.000007502771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003104165,"about_ca_topic_score_gemma":0.002072596,"domain_scores_codex":[0.9992039,0.00001530482,0.0002602101,0.0002210456,0.0000724971,0.0002270735],"domain_scores_gemma":[0.9997022,0.0001318021,0.00003084497,0.0000722454,0.0000372131,0.00002569768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001481256,0.00001389717,0.520546,0.0004890405,0.00005892,0.000001892684,0.0004381028,0.4504927,0.003203334,0.001834912,0.000003501543,0.02276959],"study_design_scores_gemma":[0.0004024899,0.00009987407,0.006564434,0.0004461276,0.00002184172,0.000001314196,0.000337547,0.9611223,0.02947507,0.001225706,0.0001464593,0.0001567882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5655686,0.001180084,0.4328126,0.00003943132,0.00002247338,0.000149203,0.00001385225,0.0001567158,0.00005697806],"genre_scores_gemma":[0.9949761,0.002336201,0.002268763,0.00002995757,0.000006173274,0.0001425929,0.0001029127,0.00002106094,0.0001162632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5139816,"threshold_uncertainty_score":0.5750902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02057149294610725,"score_gpt":0.2596703685819444,"score_spread":0.2390988756358371,"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."}}