{"id":"W2767845506","doi":"10.1016/j.jtrangeo.2017.10.010","title":"Evaluating public transit services for operational efficiency and access equity","year":2017,"lang":"en","type":"article","venue":"Journal of Transport Geography","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":81,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; The Scarborough Hospital","funders":"National Institute for Transportation and Communities","keywords":"Public transport; Equity (law); Data envelopment analysis; Transport engineering; Performance measurement; Business; Operational efficiency; Transit (satellite); Service (business); Computer science; Environmental economics; Engineering; Marketing; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01218123,0.0001892148,0.0005481461,0.0009282187,0.001464693,0.00199712,0.002889327,0.00009519375,0.0001194693],"category_scores_gemma":[0.0006511579,0.0001331684,0.0006550791,0.0005581777,0.0003287373,0.002697678,0.00007164421,0.0002177257,0.00000194255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001309818,"about_ca_system_score_gemma":0.0002201063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004351399,"about_ca_topic_score_gemma":0.0001779053,"domain_scores_codex":[0.9952327,0.0001034924,0.001401392,0.0004189009,0.002494219,0.0003492758],"domain_scores_gemma":[0.9951589,0.0005245833,0.001507489,0.0006086181,0.001969687,0.0002307089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002242023,0.0003213194,0.9064069,0.00007634125,0.000240029,0.00002453102,0.0008772216,0.008971549,0.001830326,0.0009892624,0.0000986646,0.07993966],"study_design_scores_gemma":[0.001914756,0.0005039675,0.9546159,0.0001082207,0.0003704226,0.00005552737,0.0002438586,0.0214782,0.0003923076,0.01680282,0.003188152,0.0003258761],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9151374,0.0006158039,0.07818498,0.004787235,0.0004485908,0.0001938455,0.00003532903,0.000009602905,0.0005871967],"genre_scores_gemma":[0.9939363,0.00004608266,0.005530202,0.0002676902,0.0001765636,0.000004270619,0.000003760078,0.00001029536,0.00002484272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07961378,"threshold_uncertainty_score":0.9998353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2337456089732012,"score_gpt":0.4915047657282167,"score_spread":0.2577591567550155,"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."}}