{"id":"W2300234412","doi":"","title":"Modelling the Spatial Distribution of Hybrid-Electric Vehicles in Windsor, Ontario","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Metropolitan area; Geography; Census tract; Distribution (mathematics); Spatial analysis; Windsor; Spatial distribution; Population; Economic geography; Cartography; Econometrics; Regional science; Economics; Demography; Environmental science; Mathematics","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":[0.000315075,0.0003553487,0.0002970964,0.0006885044,0.0009352253,0.001480739,0.00136149,0.0004415241,0.002859154],"category_scores_gemma":[0.001360263,0.0003856595,0.0005832465,0.001605844,0.0005785652,0.0004598546,0.0006439443,0.0002721588,0.0003181973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01576843,"about_ca_system_score_gemma":0.008687695,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9846503,"about_ca_topic_score_gemma":0.984004,"domain_scores_codex":[0.9997361,0.00003119727,0.00001066892,0.00006745058,0.00004310818,0.0001113824],"domain_scores_gemma":[0.999487,0.0001405955,0.00007512716,0.00003027848,0.0002110533,0.00005590051],"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":[0.0001664981,0.000105003,0.374031,0.00007943645,0.00009076307,0.0005643365,0.0009993034,0.5954307,0.0010749,0.006993305,0.00439211,0.01607251],"study_design_scores_gemma":[0.0000294814,0.0000252035,0.1640478,0.00003981399,0.00003288774,0.00006869499,0.001601872,0.8280542,0.000240357,0.001252409,0.00457311,0.00003412331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858636,0.0002710642,0.003889524,0.0002792077,0.000009778196,0.00006740229,0.003545732,0.00004784185,0.006025765],"genre_scores_gemma":[0.9922592,0.0002168449,0.0014308,0.00001241295,0.000003830206,0.00002773981,0.001496491,0.00001320508,0.004539649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01576843,"threshold_uncertainty_score":0.1144086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06559287489032437,"score_gpt":0.3662512889339438,"score_spread":0.3006584140436194,"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."}}