{"id":"W4365457543","doi":"10.1016/j.trc.2023.104123","title":"Do incentives make a difference? Understanding smart charging program adoption for electric vehicles","year":2023,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Incentive; Incentive program; Electric vehicle; Population; Electricity; Business; Mixed logit; Environmental economics; Economics; Engineering; Logistic regression; Microeconomics; Computer science; Electrical engineering; Environmental health","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":[0.003213091,0.0001651358,0.0003284227,0.0007115669,0.0007476912,0.003715527,0.0009313444,0.001618513,0.0073775],"category_scores_gemma":[0.02630307,0.0002908835,0.0003672524,0.0009255065,0.001264114,0.007317411,0.001084345,0.002392179,0.0004224275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002378438,"about_ca_system_score_gemma":0.001898659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01199517,"about_ca_topic_score_gemma":0.01465421,"domain_scores_codex":[0.9977393,0.0007373725,0.00009990778,0.0003217002,0.000411864,0.0006898412],"domain_scores_gemma":[0.9795387,0.01307378,0.004406051,0.0005506068,0.001579286,0.0008515335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005608852,0.001329377,0.4944816,0.0003917369,0.0002835215,0.0005574372,0.01201698,0.01260645,0.002436247,0.3295473,0.006081513,0.1397071],"study_design_scores_gemma":[0.0001335322,0.0004518486,0.58881,0.0006655785,0.0002401285,0.0003659431,0.04950584,0.03833416,0.002759349,0.2640629,0.0545104,0.0001603172],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9390872,0.0008533215,0.004947976,0.01443215,0.00005208108,0.00004204578,0.000240851,0.00002254851,0.0403218],"genre_scores_gemma":[0.998459,0.0001747068,0.0003365565,0.0002377198,0.00001633356,0.000007259396,0.00004116228,0.000006541433,0.0007208562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01199517,"threshold_uncertainty_score":0.0246802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07769845844321716,"score_gpt":0.3345338964853167,"score_spread":0.2568354380420995,"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."}}