{"id":"W2294106464","doi":"","title":"Stochastic Data Envelopment Analysis: A Random Efficiency Perspective","year":2013,"lang":"fr","type":"article","venue":"Les Cahiers du GERAD","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; McGill University","funders":"","keywords":"Data envelopment analysis; Ranking (information retrieval); Bayesian probability; Frequentist inference; Computer science; Econometrics; Perspective (graphical); Mathematics; Statistics; Bayesian inference; Artificial intelligence","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":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.005868649,0.0007380935,0.00152547,0.002178206,0.001487802,0.001525187,0.004783441,0.0005039576,0.006122258],"category_scores_gemma":[0.007589392,0.0006308668,0.0008688861,0.009755151,0.002554185,0.00142432,0.0008829442,0.0009942724,0.005568232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001060767,"about_ca_system_score_gemma":0.0006617929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006543207,"about_ca_topic_score_gemma":0.0009924518,"domain_scores_codex":[0.9895089,0.001405122,0.00176544,0.002791644,0.003168307,0.001360539],"domain_scores_gemma":[0.9894638,0.003951645,0.0008191576,0.00396395,0.001172266,0.0006292003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004179833,0.004848772,0.006393204,0.00007639681,0.01435518,0.0002543093,0.1059595,0.2102375,0.000577963,0.2196599,0.06596923,0.3712501],"study_design_scores_gemma":[0.002738441,0.0001988173,0.007517459,0.00008781383,0.005852232,0.00004453299,0.02463341,0.8739761,0.0000399914,0.06138093,0.02193534,0.001594934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1257874,0.01956139,0.8132619,0.03002295,0.00250146,0.001167448,0.0002034923,0.0001619766,0.007331962],"genre_scores_gemma":[0.9636681,0.0003753568,0.01161737,0.0008040309,0.0004562625,0.0000624229,0.00007839488,0.00004795348,0.02289011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8378807,"threshold_uncertainty_score":0.9998121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04352935928923564,"score_gpt":0.3280250220894007,"score_spread":0.2844956628001651,"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."}}