{"id":"W2907185436","doi":"10.1016/j.jtrangeo.2018.12.007","title":"A random utility maximization (RUM) based measure of accessibility to transit: Accurate capturing of the first-mile issue in urban transit","year":2018,"lang":"en","type":"article","venue":"Journal of Transport Geography","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Arup Group (Canada); University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mile; Transit (satellite); Measure (data warehouse); Transport engineering; Computer science; Geographic information system; Last mile (transportation); Gravity model of trade; Public transport; Geography; Engineering; Business; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003385071,0.0005685808,0.0009814145,0.002187166,0.000309083,0.001123394,0.001555909,0.001112994,0.0009092314],"category_scores_gemma":[0.01914281,0.0004070887,0.0005971276,0.001871201,0.0006259579,0.002610454,0.001301444,0.001030089,0.0002695876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007751177,"about_ca_system_score_gemma":0.0005687217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004771376,"about_ca_topic_score_gemma":0.004817007,"domain_scores_codex":[0.9980353,0.001163523,0.00009096889,0.0003926125,0.0001817748,0.0001358507],"domain_scores_gemma":[0.9896069,0.005988192,0.001933532,0.00123343,0.0009410996,0.0002968406],"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":[0.0009696837,0.0006528987,0.2167857,0.0004053044,0.0006129668,0.0003612729,0.0007685181,0.6179315,0.006243478,0.04384478,0.004242475,0.1071814],"study_design_scores_gemma":[0.00000790785,0.0001153528,0.01532791,0.00002182955,0.00004933666,0.0001054086,0.00009694931,0.9758288,0.001152297,0.006729793,0.0005372112,0.00002710114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.495775,0.0006460057,0.497232,0.0005755653,0.00005117063,0.0001333708,0.001486831,0.0005281812,0.003571729],"genre_scores_gemma":[0.9754941,0.00008661936,0.02346411,0.00004107886,0.00002843352,0.00003032153,0.000390313,0.00001804656,0.0004469064],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004771376,"threshold_uncertainty_score":0.01790214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01790094319230431,"score_gpt":0.2704300205986794,"score_spread":0.2525290774063751,"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."}}