{"id":"W4254988154","doi":"10.32920/ryerson.14664114","title":"Interrogating Variables Affecting Consumers' EV Purchasing Decision","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Innovation, Science and Economic Development Canada","funders":"","keywords":"Purchasing; Order (exchange); Affect (linguistics); Marketing; Variable (mathematics); Purchasing decision; Business; Variables; Decision tree; Process (computing); Advertising; Operations research; Computer science; Engineering; Mathematics; Psychology","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":[0.001789465,0.0001744723,0.0001925717,0.0008169226,0.0003004648,0.001292731,0.0002635854,0.0004811741,0.002097432],"category_scores_gemma":[0.008912846,0.0001733209,0.0002917759,0.001237617,0.0005793141,0.00128911,0.0004435285,0.0005727474,0.0002453093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000638365,"about_ca_system_score_gemma":0.0004610162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009327387,"about_ca_topic_score_gemma":0.0110187,"domain_scores_codex":[0.9993429,0.0003145466,0.00003977788,0.0001016112,0.0001180689,0.00008306297],"domain_scores_gemma":[0.9888285,0.008583997,0.001540167,0.0002988302,0.0005882424,0.0001602861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001445194,0.0001377769,0.971715,0.00007031574,0.00007129899,0.0002442374,0.008784813,0.002161207,0.000943611,0.001459444,0.0002993652,0.01396851],"study_design_scores_gemma":[0.000007251737,0.0001251125,0.9646043,0.0000430038,0.00005514514,0.0001707399,0.01955607,0.009699146,0.001293471,0.002242661,0.002164383,0.00003881041],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997758,0.00005207314,0.0006213745,0.00007433171,0.000002449154,0.000008941177,0.0001187666,0.000003136667,0.001360945],"genre_scores_gemma":[0.9991462,0.00004076407,0.000457828,0.00001452847,0.000002226803,0.000007898986,0.0001538933,0.000001976292,0.0001746268],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009327387,"threshold_uncertainty_score":0.01854616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008151354348899638,"score_gpt":0.2277740281441924,"score_spread":0.2196226737952927,"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."}}