{"id":"W4285294354","doi":"10.1515/demo-2022-0108","title":"Technical and allocative inefficiency in production systems: a vine copula approach","year":2022,"lang":"en","type":"article","venue":"Dependence Modeling","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Russian Science Foundation; University of Sydney","keywords":"Vine copula; Copula (linguistics); Pairwise comparison; Econometrics; Inefficiency; Estimator; Computer science; Mathematics; Economics; Statistics; 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":[],"consensus_categories":[],"category_scores_codex":[0.009128972,0.0001700982,0.0003764189,0.000822268,0.0004992075,0.0001907254,0.0008443569,0.00006106062,0.00002084778],"category_scores_gemma":[0.00272594,0.0001460694,0.00006242078,0.002900549,0.0001056584,0.0003503751,0.0005446168,0.000517764,0.00001149412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002189154,"about_ca_system_score_gemma":0.0001298753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004028146,"about_ca_topic_score_gemma":0.00007222289,"domain_scores_codex":[0.9947777,0.0005664733,0.0009062517,0.001135568,0.002268403,0.0003456189],"domain_scores_gemma":[0.9985343,0.0002476585,0.000227631,0.0006643446,0.0002509066,0.00007514484],"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":[0.00001942057,0.0001519798,0.001844803,0.000005404677,0.000002406979,0.000008754158,0.0008382864,0.9931269,0.001482828,0.001280881,0.00003585982,0.00120252],"study_design_scores_gemma":[0.0001293128,0.00004783767,0.0002215026,0.00001558697,0.00001079379,0.0001224488,0.005329474,0.9923763,0.00002148608,0.001505951,0.00004660688,0.0001727416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.648046,0.0009378018,0.3492675,0.0003572842,0.0002246201,0.0003716874,0.000003849043,0.00006249641,0.0007287742],"genre_scores_gemma":[0.9967863,0.00001102932,0.002776955,0.00004845484,0.00004026946,0.0001231047,0.000004357847,0.00001243364,0.0001971204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3487402,"threshold_uncertainty_score":0.5956539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.101218872996424,"score_gpt":0.3566794780145832,"score_spread":0.2554606050181592,"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."}}