{"id":"W4366196674","doi":"10.48550/arxiv.2304.06769","title":"A Multi-Battery Model for the Aggregate Flexibility of Heterogeneous Electric Vehicles","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Office of Naval Research; Cornell Atkinson Center for Sustainability, Cornell University; David R. Atkinson Center for a Sustainable Future , Cornell University","keywords":"Flexibility (engineering); Battery (electricity); Computer science; Aggregate (composite); Electric vehicle; Set (abstract data type); Electricity; Mathematical optimization; Engineering; Mathematics; Electrical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001668984,0.000310627,0.0003590817,0.0001678903,0.0001013963,0.00002518503,0.0006747413,0.0003200422,0.000005951318],"category_scores_gemma":[0.0000272132,0.0002837033,0.000342162,0.0004052914,0.0000619003,0.00006041463,0.0002384775,0.0005327376,0.000007853527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001646549,"about_ca_system_score_gemma":0.00007057934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006627463,"about_ca_topic_score_gemma":0.00005849092,"domain_scores_codex":[0.9987504,0.00003108717,0.0002527541,0.0005083088,0.00006533995,0.0003920771],"domain_scores_gemma":[0.998775,0.0001895956,0.0001309811,0.0007202434,0.0001114801,0.00007272325],"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":[0.00003907026,0.00001853416,0.0002870625,0.0002948914,0.0002307221,0.00001188132,0.00008932927,0.9954798,0.002035743,0.0002256611,0.0002852009,0.001002155],"study_design_scores_gemma":[0.000354624,0.00002966788,0.000557103,0.00004509028,0.0001532778,0.000002185467,0.00001363928,0.9854976,0.003772256,0.009271075,0.00003084404,0.0002726565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6758736,0.0004484786,0.3226013,0.00002110146,0.0001795815,0.0004683608,0.0001076249,0.0002834235,0.00001654296],"genre_scores_gemma":[0.9979182,0.0007698659,0.0006437193,0.00002727782,0.00005821371,0.0000056622,0.0000228264,0.00006896411,0.0004852549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3220446,"threshold_uncertainty_score":0.9999615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07910812318227842,"score_gpt":0.1990351138618003,"score_spread":0.1199269906795219,"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."}}