{"id":"W4413031726","doi":"10.1016/j.seps.2025.102299","title":"A Bayesian approach for correcting bias of data envelopment analysis estimators using the super-efficiency frontier","year":2025,"lang":"en","type":"article","venue":"Socio-Economic Planning Sciences","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Peking University; University of British Columbia","keywords":"Data envelopment analysis; Estimator; Frontier; Econometrics; Bayesian probability; Statistics; Computer science; Economics; Mathematics; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.02040625,0.0002822835,0.0008593073,0.001517758,0.002073918,0.0007640854,0.005095794,0.0001164804,0.00004401199],"category_scores_gemma":[0.003097683,0.000186487,0.0004057203,0.004225051,0.001538591,0.0007799636,0.0006913804,0.0001883235,0.000005569847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002107283,"about_ca_system_score_gemma":0.001149471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004636199,"about_ca_topic_score_gemma":0.00003043716,"domain_scores_codex":[0.9950011,0.0003716676,0.001538028,0.001541836,0.0009230261,0.0006243562],"domain_scores_gemma":[0.9929177,0.004419423,0.0009887981,0.0014247,0.0001680384,0.00008133986],"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":[0.00001110226,0.00005857484,0.2980631,0.000007720331,0.000316433,4.467492e-7,0.00321383,0.6905332,0.0000592155,0.0006248815,0.001600301,0.005511195],"study_design_scores_gemma":[0.0001538519,0.00002234884,0.002626011,0.00002493086,0.0004337461,0.00000228714,0.02566887,0.9687324,0.000130463,0.001586036,0.0004040053,0.0002150329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3882011,0.0003898165,0.608681,0.0003309056,0.0004812413,0.0002536356,0.00006083452,0.00002813714,0.001573329],"genre_scores_gemma":[0.9190476,0.000002816635,0.08040342,0.0001351283,0.00004757919,0.00001286588,0.00002324118,0.000008122669,0.0003192014],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5308465,"threshold_uncertainty_score":0.9992253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2804531923267279,"score_gpt":0.4505546798110449,"score_spread":0.170101487484317,"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."}}