{"id":"W2946584949","doi":"10.1155/2019/5024253","title":"Evaluating Route and Frequency Design of Bus Lines Based on Data Envelopment Analysis with Network Epsilon-Based Measures","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Jilin Office of Philosophy and Social Science; National Natural Science Foundation of China; Baidu","keywords":"Data envelopment analysis; Inefficiency; Transit (satellite); Rationality; Computer science; Bus network; Transport engineering; Operations research; Transformation (genetics); Public transport; Descriptive statistics; Bus rapid transit; Engineering; Mathematical optimization; Statistics; System bus; Mathematics; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003136738,0.001123146,0.0008899362,0.003229416,0.0004102028,0.001670862,0.0004646134,0.0005187753,0.0006977192],"category_scores_gemma":[0.007343278,0.0003763092,0.00127799,0.002802282,0.0004425369,0.001649566,0.0008143496,0.0005206887,0.0001091315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002373937,"about_ca_system_score_gemma":0.002000733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01094806,"about_ca_topic_score_gemma":0.007494475,"domain_scores_codex":[0.9975986,0.001287161,0.0001625702,0.0002062151,0.0005726773,0.0001727136],"domain_scores_gemma":[0.9976751,0.001403629,0.0003222079,0.0001527305,0.0003923617,0.00005403251],"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":[0.0000466924,0.00006361629,0.008464512,0.00008390962,0.00006394499,0.00003484359,0.00009437524,0.9646802,0.001013341,0.005587705,0.0001057146,0.0197612],"study_design_scores_gemma":[0.000005552438,0.00006502732,0.002333617,0.00001092722,0.00001647025,0.000009417511,0.0001030194,0.993866,0.001257397,0.002001637,0.0003196654,0.00001118538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4274242,0.000273525,0.5671894,0.0001323435,0.00001202278,0.0002247072,0.0003027917,0.0000968945,0.0043442],"genre_scores_gemma":[0.9355519,0.0001950476,0.06338198,0.000009984472,0.000002955863,0.0001591392,0.0002217307,0.0000153628,0.0004619032],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01094806,"threshold_uncertainty_score":0.02176869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1410387997083117,"score_gpt":0.3930766245465125,"score_spread":0.2520378248382008,"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."}}