{"id":"W2988567901","doi":"10.1109/jsac.2019.2951989","title":"Decentralized PEV Power Allocation With Power Distribution and Transportation Constraints","year":2019,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Kernel density estimation; Electric vehicle; Scalability; Mathematical optimization; Grid; Scheduling (production processes); Plug-in; Power (physics); Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001197812,0.0001390199,0.0001555185,0.00008472172,0.0001083035,0.00005982176,0.0002175798,0.00009523543,0.00009814712],"category_scores_gemma":[0.0000161972,0.0001224694,0.0000249577,0.0004909273,0.00006006608,0.0002107737,0.000003414857,0.0006215238,0.000008036188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001544013,"about_ca_system_score_gemma":0.00006287319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004938715,"about_ca_topic_score_gemma":0.00006055413,"domain_scores_codex":[0.9991624,0.0000697962,0.0002906602,0.000104064,0.0001623488,0.0002107415],"domain_scores_gemma":[0.9992284,0.00009246072,0.00008290129,0.0003282175,0.0001831342,0.00008490369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001546323,0.001793291,0.4481751,0.0002354717,0.001602802,0.00007407364,0.01321297,0.1459571,0.1764674,0.06955955,0.01379126,0.1275847],"study_design_scores_gemma":[0.003945208,0.0005750485,0.9608557,0.0004515956,0.0000689273,0.00038781,0.0004356122,0.01285224,0.004358316,0.001214026,0.01419257,0.0006629883],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912761,0.0003458585,0.006674323,0.000424563,0.000100365,0.0002311131,0.00003421179,0.00007641255,0.0008370306],"genre_scores_gemma":[0.9972576,0.0009524415,0.001554045,0.00005884153,0.000009390506,0.000008173837,0.0001290411,0.00002041113,0.00001006941],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5126805,"threshold_uncertainty_score":0.4994157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005887803986968835,"score_gpt":0.2193384990626467,"score_spread":0.2134506950756778,"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."}}