{"id":"W1975740324","doi":"10.5038/2375-0901.13.4.2","title":"Using GIS for Measuring Transit Stop Accessibility Considering Actual Pedestrian Road Network","year":2010,"lang":"en","type":"article","venue":"Journal of Public Transportation","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Transport engineering; Pedestrian; Transit (satellite); Service (business); Computer science; Level of service; Public transport; Engineering; Business","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":[0.0007807267,0.0005550737,0.0003686448,0.003838286,0.000204215,0.001062742,0.0003228312,0.0003144919,0.001652826],"category_scores_gemma":[0.00317505,0.0001927644,0.0003026054,0.004261704,0.0002160092,0.001079311,0.0005663151,0.0001644821,0.0004276345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000284471,"about_ca_system_score_gemma":0.0003821101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003577497,"about_ca_topic_score_gemma":0.004060473,"domain_scores_codex":[0.999141,0.0003312525,0.00009071688,0.00009007807,0.0003009287,0.0000458744],"domain_scores_gemma":[0.9985732,0.0006151757,0.000186829,0.0001482572,0.0004112122,0.00006538592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007595186,0.0003648244,0.4601425,0.0008149242,0.0003556616,0.0006915858,0.002149009,0.09673216,0.05193342,0.009124928,0.003532406,0.373399],"study_design_scores_gemma":[0.00004716131,0.0007495356,0.4210911,0.000111206,0.0002825035,0.001366595,0.004775611,0.5208049,0.03122314,0.005636912,0.01375994,0.0001513847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8179002,0.0003713753,0.1597151,0.00008459169,0.00003635306,0.0002945736,0.005344037,0.001642597,0.01461111],"genre_scores_gemma":[0.8871906,0.0002017789,0.1093325,0.000007704622,0.00001219911,0.0001342781,0.0022239,0.00002696823,0.0008702393],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003838286,"threshold_uncertainty_score":0.007113338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1339573082689822,"score_gpt":0.3545365041646295,"score_spread":0.2205791958956473,"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."}}