{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005471549,0.0002070145,0.000216768,0.0009926258,0.0004327213,0.0001121939,0.0003190365,0.0002156119,0.00001054011],"category_scores_gemma":[0.00008387504,0.0002000665,0.00008756166,0.002795432,0.0001158263,0.0001523229,0.00001449538,0.000565475,0.00001091413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001881524,"about_ca_system_score_gemma":0.00003340795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001090308,"about_ca_topic_score_gemma":0.00005592593,"domain_scores_codex":[0.9979134,0.00003022938,0.0003267716,0.0003532008,0.0004616917,0.0009147481],"domain_scores_gemma":[0.9993534,0.0001842395,0.00004333504,0.0002427743,0.0001274261,0.00004882298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001114647,0.00009523648,0.02741689,0.001237969,0.0002636005,0.00003281457,0.002037773,0.00716918,0.08631954,0.09825744,0.008236703,0.7688214],"study_design_scores_gemma":[0.002698476,0.001328746,0.1551595,0.001023543,0.0001065602,0.000004415762,0.0318738,0.4352545,0.1043572,0.2105328,0.05567612,0.00198436],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9769945,0.0007907699,0.01033457,0.001052055,0.0001505577,0.001343415,0.00003961018,0.009100445,0.0001940897],"genre_scores_gemma":[0.9952429,0.002428191,0.001180913,0.000003555024,0.00004163601,0.0008434579,0.0001366745,0.00005188189,0.00007082576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.766837,"threshold_uncertainty_score":0.8158476,"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."}}