{"id":"W4247226341","doi":"10.32920/ryerson.14648433","title":"Factors Influencing the Diffusion of Battery Electric Vehicles in Urban Areas","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Innovation Diffusion and Forecasting","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Innovation, Science and Economic Development Canada","funders":"","keywords":"Battery (electricity); Purchasing; Electric vehicle; Business; Set (abstract data type); Battery electric vehicle; Environmental economics; Automotive engineering; Transport engineering; Marketing; Computer science; Engineering; Economics","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":[0.0007054576,0.000121619,0.0001695399,0.001199223,0.0004122202,0.000970749,0.000221849,0.0002918162,0.001214152],"category_scores_gemma":[0.004936383,0.0001156065,0.0002346362,0.001606776,0.0004432563,0.0005217519,0.0005767461,0.000343787,0.0001298827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000735282,"about_ca_system_score_gemma":0.0004841635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01331101,"about_ca_topic_score_gemma":0.01537549,"domain_scores_codex":[0.9994461,0.0001984783,0.00004347583,0.00007358805,0.0001327941,0.0001054517],"domain_scores_gemma":[0.9967854,0.001359323,0.001005683,0.0001092725,0.0004381766,0.0003021979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006557113,0.0001405644,0.9812353,0.00004597426,0.00003248924,0.0002275419,0.00356233,0.001695661,0.0006628436,0.00105444,0.0001904414,0.01108696],"study_design_scores_gemma":[0.000003463783,0.00005970033,0.9879656,0.00001551269,0.0000173296,0.00007428735,0.007968653,0.002484371,0.000268758,0.0002188111,0.0009116719,0.0000117302],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988385,0.00004532623,0.0001066835,0.00004875485,0.000001163812,0.000009839882,0.00003472539,0.000001747371,0.0009133221],"genre_scores_gemma":[0.9996706,0.00005942824,0.00008102212,0.000002690088,0.000001591651,0.000003726755,0.00003132998,6.393224e-7,0.0001489672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01331101,"threshold_uncertainty_score":0.02646703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1116585871464055,"score_gpt":0.3361039847248246,"score_spread":0.2244453975784191,"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."}}