{"id":"W4200481758","doi":"10.3390/wevj12040257","title":"Parametric Predictions for Pure Electric Vehicles","year":2021,"lang":"en","type":"article","venue":"World Electric Vehicle Journal","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Mean squared error; Artificial neural network; Parametric statistics; Root mean square; Approximation error; Mean absolute percentage error; Mean absolute error; Electric vehicle; Parametric model; Standard deviation; Mean square; Computer science; Algorithm; Mathematics; Statistics; Engineering; Artificial intelligence; Physics; Thermodynamics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004622223,0.0007123757,0.0003445995,0.0004750804,0.0001876499,0.0006532363,0.0008069121,0.0006635876,0.003284947],"category_scores_gemma":[0.002085018,0.0002283817,0.0006204217,0.0003928004,0.000216625,0.0007894908,0.000337448,0.0006087686,0.0007509105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004354643,"about_ca_system_score_gemma":0.0003793824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007494688,"about_ca_topic_score_gemma":0.006019246,"domain_scores_codex":[0.9997727,0.00003704323,0.00001385315,0.00006080234,0.00009038258,0.00002526751],"domain_scores_gemma":[0.9994092,0.0002561678,0.00004434851,0.00008772391,0.000192948,0.000009613692],"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":[0.00003961507,0.00002512819,0.00115011,0.00004872607,0.000007787046,0.00004325479,0.00001978868,0.9813601,0.001603649,0.0006511963,0.0006558929,0.01439472],"study_design_scores_gemma":[0.000002773281,0.00003188514,0.000793447,0.000006253189,0.000004484853,0.0000183983,0.00001350234,0.9956315,0.001723481,0.0008130058,0.0009534303,0.000007863951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6308439,0.0006573725,0.3266496,0.0003077682,0.0001699102,0.0001664673,0.004373175,0.001748972,0.03508292],"genre_scores_gemma":[0.9866303,0.0001256988,0.008983837,0.00001856158,0.000008112017,0.00006775378,0.0009698039,0.00003952594,0.003156295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007494688,"threshold_uncertainty_score":0.01490211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01548788492317251,"score_gpt":0.2645495092449379,"score_spread":0.2490616243217653,"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."}}