{"id":"W2346836642","doi":"","title":"How Policy Can Build the Plug-in Electric Vehicle Market: Insights from Respondent-Based Preferences and Constraints (REPAC) Model","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Respondent; Market share; Electric vehicle; Baseline (sea); Variety (cybernetics); Subsidy; Mandate; Economics; Environmental economics; Marketing; Business; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002490003,0.0009998368,0.001338964,0.0008594372,0.0006868182,0.003757375,0.002068369,0.002859612,0.01875568],"category_scores_gemma":[0.01017979,0.001077518,0.001561224,0.001401541,0.001064002,0.004545728,0.001819376,0.004248787,0.001516175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002713656,"about_ca_system_score_gemma":0.002644738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03916915,"about_ca_topic_score_gemma":0.02363232,"domain_scores_codex":[0.9986373,0.0007380188,0.00003433758,0.0002354714,0.0001026886,0.0002521981],"domain_scores_gemma":[0.9958637,0.002943875,0.0003754599,0.0001901376,0.0002901763,0.0003366137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009423517,0.000165389,0.004009575,0.0001017623,0.00008150926,0.0002294637,0.0003214627,0.5254115,0.0002623166,0.4510681,0.009263888,0.008990876],"study_design_scores_gemma":[0.00006251573,0.00005044509,0.000827303,0.00003477806,0.00002924587,0.00004603137,0.0002487265,0.7509434,0.00006652431,0.2401558,0.007490601,0.00004463411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2686757,0.002047072,0.518939,0.0406946,0.0004606477,0.0004472929,0.008425825,0.0008384439,0.1594715],"genre_scores_gemma":[0.9002583,0.001499941,0.04956983,0.002154248,0.0002240767,0.0004933337,0.002138268,0.0002287077,0.04343323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03916915,"threshold_uncertainty_score":0.07788235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02390342926562885,"score_gpt":0.2939835931681157,"score_spread":0.2700801639024869,"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."}}