{"id":"W2558374656","doi":"10.5539/jmr.v8n6p114","title":"Efficiency Analysis of Public Transportation Subunits Using DEA and Bootstrap Approaches -- Dakar Dem Dikk Case Study","year":2016,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bootstrapping (finance); Data envelopment analysis; Returns to scale; Restructuring; Order (exchange); Scale (ratio); Econometrics; Measure (data warehouse); Process (computing); Efficiency; Computer science; Economics; Mathematics; Operations research; Statistics; Microeconomics; Geography; Finance; Production (economics); Data mining","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":[0.003434102,0.0005095095,0.0008180431,0.002032716,0.0007233925,0.001724677,0.0007759372,0.000807584,0.002196456],"category_scores_gemma":[0.006624411,0.0003015688,0.001494541,0.003231752,0.000829477,0.001202668,0.001014158,0.0008406811,0.0001994423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003179821,"about_ca_system_score_gemma":0.00115454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01659762,"about_ca_topic_score_gemma":0.008802655,"domain_scores_codex":[0.9978803,0.00117335,0.0001229585,0.000221978,0.0003613343,0.0002399234],"domain_scores_gemma":[0.9956051,0.003053628,0.0003945365,0.0003757399,0.00049821,0.00007275891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002385431,0.0002607587,0.02502668,0.0002316568,0.0001778788,0.001336152,0.0004607358,0.865338,0.001621037,0.06980222,0.001029932,0.03447642],"study_design_scores_gemma":[0.00002642145,0.0001474083,0.01465595,0.00004407304,0.0000735517,0.0002126577,0.001262265,0.9634411,0.002335752,0.01448928,0.003273944,0.00003760259],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8675934,0.0004933533,0.114286,0.0004424331,0.0000149379,0.0001924201,0.0007272972,0.00008063385,0.01616965],"genre_scores_gemma":[0.9796592,0.000219645,0.01835506,0.000007651287,0.000003955499,0.00008204348,0.0002594208,0.00001494575,0.001398031],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01659762,"threshold_uncertainty_score":0.03300202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7271122833690667,"score_gpt":0.5332870490161599,"score_spread":0.1938252343529069,"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."}}