{"id":"W1679576089","doi":"10.1109/pesgm.2015.7285829","title":"Electric vehicle capacity forecasting model with application to load levelling","year":2015,"lang":"en","type":"article","venue":"","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; China Scholarship Council; Queen's University; Queen's University Belfast","keywords":"Levelling; Computer science; Scale (ratio); Electric vehicle; Stochastic modelling; Power (physics); Automotive engineering; Operations research; Engineering; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0003788585,0.0004604353,0.0005973408,0.0003339337,0.0004528583,0.0007217364,0.0009870125,0.001004405,0.001737734],"category_scores_gemma":[0.0008924297,0.0003313655,0.0004017032,0.0006475615,0.0003247815,0.0006860469,0.0003460137,0.0007764532,0.0002251597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009281115,"about_ca_system_score_gemma":0.0009696037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05623607,"about_ca_topic_score_gemma":0.0257783,"domain_scores_codex":[0.9998537,0.0000364842,0.000008714887,0.00003864113,0.00003391393,0.00002849577],"domain_scores_gemma":[0.9997076,0.0001280229,0.00004312024,0.00001492813,0.00008781977,0.0000184069],"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":[0.000007899982,0.000003513796,0.000231277,0.000002890877,0.000002787845,0.00001775302,0.000005726437,0.9976715,0.00008408256,0.000519936,0.0001135771,0.001338896],"study_design_scores_gemma":[8.41583e-7,0.000002925431,0.00009102486,7.131335e-7,0.000001219329,0.000002676022,0.000002474581,0.9995592,0.00003736903,0.0002298678,0.00006962843,0.00000206993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.469882,0.00063662,0.4942596,0.001285954,0.000152771,0.0001119164,0.001579316,0.001112216,0.03097968],"genre_scores_gemma":[0.9892299,0.0001286194,0.006479223,0.0000261197,0.00001677418,0.00003871161,0.0002801498,0.00002136195,0.003779209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05623607,"threshold_uncertainty_score":0.1118175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02568541083853237,"score_gpt":0.197876795782428,"score_spread":0.1721913849438956,"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."}}