{"id":"W3217385682","doi":"10.1016/j.tra.2021.11.003","title":"Autonomous vehicle parking policies: A case study of the City of Toronto","year":2021,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Downtown; Toll; Cruise; Transport engineering; Parking guidance and information; Business; Computer science; Traffic congestion; Geography; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003240145,0.0004999571,0.0002445749,0.0005651219,0.002389567,0.0009883713,0.0009786567,0.001113693,0.002743962],"category_scores_gemma":[0.001289602,0.0002194983,0.0004828129,0.001962743,0.00104472,0.0005052198,0.0006029501,0.0005388041,0.0002043898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01481377,"about_ca_system_score_gemma":0.00653081,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.875273,"about_ca_topic_score_gemma":0.9350664,"domain_scores_codex":[0.9995804,0.0001206696,0.00001493628,0.00004637087,0.00007589583,0.0001616785],"domain_scores_gemma":[0.9988654,0.0004709721,0.0001106073,0.00006209984,0.0002276792,0.0002632372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","study_design_scores_codex":[0.001118968,0.0018241,0.2437093,0.0005748303,0.0003109731,0.02644482,0.009677938,0.6398906,0.006213113,0.02451748,0.01872086,0.02699711],"study_design_scores_gemma":[0.0003770052,0.0008129823,0.2531512,0.0001397111,0.0002168135,0.001338224,0.06133404,0.6500492,0.003257726,0.003245174,0.02586264,0.0002152064],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923466,0.00009191054,0.0008790691,0.0003164317,0.000009817093,0.0000829831,0.0007177833,0.00002400045,0.005531352],"genre_scores_gemma":[0.9965186,0.0001371885,0.0008639101,0.00002518209,0.000003800999,0.00002260471,0.0004189386,0.000006341845,0.002003428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.124727,"threshold_uncertainty_score":0.2509229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1213703476883803,"score_gpt":0.4271650736886484,"score_spread":0.3057947260002681,"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."}}