{"id":"W1966453412","doi":"10.3141/2419-09","title":"Park-and-Ride Access Station Choice Model for Cross-Regional Commuting","year":2014,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Park and ride; Transport engineering; Transit (satellite); Geography; Service (business); Public transport; Survey data collection; Business; Engineering; Marketing","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":[0.003084301,0.001173241,0.001907631,0.001142372,0.0009295617,0.002666703,0.004577867,0.002140033,0.02064612],"category_scores_gemma":[0.003633079,0.0009634083,0.001911486,0.001549579,0.001508442,0.001605678,0.00147946,0.00230788,0.001925212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003585938,"about_ca_system_score_gemma":0.001932418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06699121,"about_ca_topic_score_gemma":0.05851942,"domain_scores_codex":[0.9983212,0.0008014949,0.00005647032,0.0003484001,0.0001074414,0.0003648551],"domain_scores_gemma":[0.9967048,0.001937267,0.0004969271,0.0001787999,0.0002545888,0.0004276754],"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":[0.00123395,0.0009114415,0.04869146,0.0002375068,0.0004594214,0.001418292,0.001442155,0.8562161,0.001655999,0.07503052,0.003333062,0.009370074],"study_design_scores_gemma":[0.0001555836,0.0001791343,0.006998453,0.00001542905,0.00007684245,0.0000831006,0.0005170037,0.9852663,0.00009629107,0.00545736,0.001106586,0.00004786245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8675128,0.0004707226,0.1168348,0.001187288,0.00008153771,0.000463694,0.002998613,0.0001932039,0.01025739],"genre_scores_gemma":[0.9677079,0.0003365786,0.008903183,0.00007161997,0.00003361191,0.0003447051,0.001277214,0.00002756852,0.02129756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06699121,"threshold_uncertainty_score":0.1332026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2342430146047022,"score_gpt":0.4991918190588881,"score_spread":0.2649488044541859,"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."}}