{"id":"W4385286194","doi":"10.1016/j.segan.2023.101114","title":"Effective self-committed V2G for residential complexes","year":2023,"lang":"en","type":"article","venue":"Sustainable Energy Grids and Networks","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Electricity; Revenue; Restructuring; Incentive; Vehicle-to-grid; Electricity market; Scheduling (production processes); Environmental economics; Computer science; Business; Operations research; Electric vehicle; Economics; Finance; Microeconomics; Operations management; Engineering; Electrical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006142032,0.0004284318,0.0003911594,0.0002546114,0.0009514771,0.001614695,0.001318848,0.0008055588,0.008904892],"category_scores_gemma":[0.001930717,0.0001502055,0.000199731,0.0003009418,0.0007733142,0.002070671,0.002550239,0.0006352654,0.001332087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006869049,"about_ca_system_score_gemma":0.0008522276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003037848,"about_ca_topic_score_gemma":0.004456604,"domain_scores_codex":[0.9994049,0.0002062393,0.00001602955,0.00009334792,0.0001367299,0.0001426656],"domain_scores_gemma":[0.9993686,0.000139067,0.00003537955,0.0002205207,0.0001390048,0.00009743802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003947756,0.0003446192,0.00269641,0.0001715988,0.00005482785,0.0008165906,0.0009488041,0.1969735,0.0083116,0.4980255,0.03519088,0.256071],"study_design_scores_gemma":[0.00005479324,0.0001869339,0.0009201216,0.00006604047,0.00002178604,0.0004552236,0.00107465,0.6927084,0.005325921,0.2506394,0.04851371,0.00003287411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1822595,0.0007866524,0.6337494,0.001895487,0.0004752168,0.0002734308,0.0002262694,0.001929237,0.1784047],"genre_scores_gemma":[0.9797704,0.00007900616,0.009647598,0.0001130899,0.0000245213,0.00002727119,0.00006987626,0.0000612993,0.01020694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008904892,"threshold_uncertainty_score":0.02978987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00219802074190633,"score_gpt":0.1866997197470236,"score_spread":0.1845016990051173,"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."}}