{"id":"W2511507983","doi":"10.1016/j.tranpol.2016.07.006","title":"Identifying and characterizing potential electric vehicle adopters in Canada: A two-stage modelling approach","year":2016,"lang":"en","type":"article","venue":"Transport Policy","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":200,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Early adopter; Structural equation modeling; Socioeconomic status; Electric vehicle; Theory of planned behavior; Marketing; Business; Work (physics); Economics; Control (management); Sociology; Computer science; Engineering; Population; Demography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001774667,0.0008599918,0.001199419,0.001965623,0.002010823,0.003462448,0.003194106,0.002426378,0.003645973],"category_scores_gemma":[0.004624662,0.001118312,0.001535723,0.002671261,0.0009890161,0.0015418,0.001281362,0.001790405,0.0003118056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0325336,"about_ca_system_score_gemma":0.04979619,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9903532,"about_ca_topic_score_gemma":0.9863917,"domain_scores_codex":[0.9988254,0.0002163345,0.00004246517,0.0001380742,0.0001584688,0.0006192783],"domain_scores_gemma":[0.9974656,0.001347645,0.0002793584,0.00006577106,0.0005751692,0.0002665604],"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":[0.0002073162,0.0002911043,0.07986642,0.0001275029,0.0001599652,0.0004032412,0.0006384216,0.8857393,0.000554177,0.01882979,0.002748115,0.01043459],"study_design_scores_gemma":[0.00004781458,0.00005729275,0.01628988,0.00003750391,0.0000931885,0.00002523745,0.001469956,0.9772017,0.0002415455,0.002341506,0.002145862,0.00004845342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9544094,0.0006690807,0.02331796,0.003070155,0.00002572857,0.000448251,0.005890634,0.0001582055,0.01201046],"genre_scores_gemma":[0.9785685,0.0007341111,0.007269346,0.000157472,0.000009511707,0.0001573702,0.001941954,0.00002154884,0.01114015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0325336,"threshold_uncertainty_score":0.2360489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009283048863766389,"score_gpt":0.1904416994949732,"score_spread":0.1811586506312068,"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."}}