{"id":"W3189347711","doi":"10.1109/icc42927.2021.9500559","title":"A Dynamic Pricing Based Scheduling Scheme for Electric Vehicles as Mobile Energy Storages","year":2021,"lang":"en","type":"article","venue":"","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Energy storage; Dynamic pricing; Electric vehicle; Scheduling (production processes); Automotive engineering; Real-time computing; Simulation; Power (physics); 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":[0.00007193071,0.0001934895,0.0002105767,0.0001206849,0.000102007,0.00006700271,0.0001224566,0.0001323623,0.0001416688],"category_scores_gemma":[0.0000378604,0.0001913281,0.0001088856,0.000566132,0.000007556958,0.0001106089,0.00001769628,0.0001706013,0.000007033272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001332118,"about_ca_system_score_gemma":0.0001104926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001333769,"about_ca_topic_score_gemma":0.00001677851,"domain_scores_codex":[0.9989585,0.00001408605,0.0002123606,0.0002519937,0.0001399936,0.0004230244],"domain_scores_gemma":[0.999473,0.0001033986,0.00002730998,0.0002112202,0.0001021958,0.00008288284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009754137,0.00002409277,0.0001103886,0.0001439128,0.00006948039,0.00001375276,0.00002869495,0.1668214,0.7754671,0.001827361,0.0005681575,0.05491595],"study_design_scores_gemma":[0.000317668,0.00006385404,0.00008765138,0.00002184516,0.00001344248,0.00001295077,0.00004077424,0.7307417,0.2630736,0.0004146375,0.005011991,0.0001998336],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7540039,0.006009691,0.2373696,0.00008370144,0.0001433927,0.0001670759,0.000004707018,0.0005608167,0.001657149],"genre_scores_gemma":[0.9564719,0.0001591402,0.04231267,0.0002988677,0.00007157089,0.00008037599,0.00002919797,0.00006310714,0.0005132252],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5639204,"threshold_uncertainty_score":0.7802135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003633014301674914,"score_gpt":0.206950566968911,"score_spread":0.2033175526672361,"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."}}