{"id":"W4384502033","doi":"10.1016/j.rineng.2023.101283","title":"Electric vehicles survey and a multifunctional artificial neural network for predicting energy consumption in all-electric vehicles","year":2023,"lang":"en","type":"article","venue":"Results in Engineering","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Artificial neural network; Electric energy consumption; Energy consumption; Electric vehicle; Automotive engineering; Engineering; Function (biology); Computer science; Simulation; Artificial intelligence; Electric energy; Electrical 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":[0.0006752286,0.0001985605,0.0002235032,0.0007470398,0.00004693659,0.00003813963,0.0001474581,0.0001715685,8.536356e-7],"category_scores_gemma":[0.0008088206,0.0002314124,0.00002869454,0.001501266,0.00001649928,0.0001545581,0.00006513717,0.0004008184,0.000002809851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002022306,"about_ca_system_score_gemma":0.00001137334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005427628,"about_ca_topic_score_gemma":0.0002437621,"domain_scores_codex":[0.9982609,0.0000354905,0.0004317459,0.0003262682,0.0001789362,0.0007666332],"domain_scores_gemma":[0.9984962,0.001237809,0.00003057596,0.0001576108,0.0000269643,0.00005079428],"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":[0.0001060181,0.000008989051,0.01247961,0.00007243895,0.00001741517,0.00001244704,0.00003826351,0.9237593,0.02755772,0.00005028605,0.0001311207,0.0357664],"study_design_scores_gemma":[0.0005396053,0.00003898207,0.150596,0.00004388704,0.000001971115,0.000004344773,0.00001123837,0.8448223,0.003548032,0.0001042399,0.0001090619,0.0001803369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9884597,0.0005993298,0.009503472,0.00003847478,0.000197315,0.0002466223,0.00003616234,0.0009101598,0.000008768494],"genre_scores_gemma":[0.9982514,0.0005202041,0.0007702131,0.000005743188,0.0001277609,0.0001626661,0.00009535436,0.00005708747,0.000009519173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1381164,"threshold_uncertainty_score":0.9436725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04107956639872871,"score_gpt":0.2771206937557911,"score_spread":0.2360411273570624,"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."}}