{"id":"W4404564609","doi":"10.1109/eeeic/icpseurope61470.2024.10751245","title":"Electric Vehicle Flexibility Harnessing Through Local Energy Community Operation Optimization: Maximizing Local Energy Utilization","year":2024,"lang":"en","type":"article","venue":"","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Flexibility (engineering); Energy (signal processing); Electric vehicle; Computer science; Automotive engineering; Engineering; Power (physics); Economics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001922595,0.0002691003,0.0002221484,0.0001239869,0.0003717768,0.0002911529,0.0001679117,0.0002395526,0.0003464871],"category_scores_gemma":[0.00001188999,0.0002544817,0.00009708711,0.001032795,0.00005987427,0.0008702718,0.00005263699,0.0004404988,0.000007585539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003787367,"about_ca_system_score_gemma":0.00008179917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007032943,"about_ca_topic_score_gemma":0.0001245669,"domain_scores_codex":[0.9985585,0.0001565446,0.0003949324,0.0002775553,0.0002319898,0.0003805361],"domain_scores_gemma":[0.9993879,0.00008284035,0.00002393695,0.0003392573,0.00008842947,0.00007764946],"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":[0.000007527402,0.00001952436,0.00001069125,0.00006536352,0.00003683245,0.000004142686,0.0001606948,0.8006535,0.002070325,0.01931373,0.0009030809,0.1767546],"study_design_scores_gemma":[0.0001667551,0.00005504507,0.00005268927,0.00004094399,0.0000266783,0.00002997715,0.0001401701,0.9468762,0.04346596,0.002303423,0.00656699,0.0002751149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005191004,0.003413705,0.9805686,0.00009913168,0.0003292304,0.00006324566,0.000003040213,0.001111829,0.009220172],"genre_scores_gemma":[0.9946029,0.0004501687,0.00406022,0.0003135364,0.000170635,0.00001606371,0.000143425,0.00006806431,0.0001749826],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9894119,"threshold_uncertainty_score":0.9999908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02246256561232464,"score_gpt":0.2425963818941105,"score_spread":0.2201338162817858,"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."}}