{"id":"W779745502","doi":"10.1016/j.techfore.2015.06.012","title":"Effect of socio-economic factors on EV/HEV/PHEV adoption rate in Ontario","year":2015,"lang":"en","type":"article","venue":"Technological Forecasting and Social Change","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; University of Waterloo","funders":"","keywords":"Electric vehicle; Greenhouse gas; Business; Total cost of ownership; Environmental economics; Market penetration; Fuel efficiency; Environmental science; Automotive engineering; Transport engineering; Agricultural economics; Engineering; Economics; Marketing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002852657,0.0001350221,0.0002434212,0.00008162396,0.00004596701,0.0000100178,0.00007032698,0.000302563,0.00001225987],"category_scores_gemma":[0.00003591595,0.0001049281,0.00004037583,0.00008444818,0.0000626792,0.00005226854,0.00002061858,0.0003635926,0.000002498581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000283091,"about_ca_system_score_gemma":0.000008656116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001480004,"about_ca_topic_score_gemma":0.001655924,"domain_scores_codex":[0.9994352,0.00002930546,0.0001440655,0.0001242038,0.0000582477,0.0002089843],"domain_scores_gemma":[0.9998061,0.00006850279,0.00003838198,0.00004866843,0.000008053409,0.00003029193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002625509,0.00006620191,0.4163033,0.0003026493,0.00009370111,0.00002884827,0.01694328,0.001525996,0.002334798,0.008238211,0.0004204248,0.55348],"study_design_scores_gemma":[0.01135592,0.01626053,0.7598807,0.0006359115,0.0002116788,0.00003679604,0.005747249,0.1052362,0.05293483,0.03931259,0.004852857,0.00353482],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987559,0.0000696342,0.0000170269,0.00003503398,0.00007899285,0.0001461971,0.000004276309,0.0001998704,0.0006930112],"genre_scores_gemma":[0.9998471,0.000008629564,0.00002910784,0.000009983116,0.00005775773,0.00001637926,0.000007816063,0.00001117005,0.00001207201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5499452,"threshold_uncertainty_score":0.4278845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05652831431976902,"score_gpt":0.2352809315003466,"score_spread":0.1787526171805776,"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."}}