{"id":"W2899304741","doi":"10.1016/j.ejor.2018.10.044","title":"Data envelopment analysis and big data","year":2018,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":98,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"National Natural Science Foundation of China; Grantová Agentura České Republiky; Pennsylvania State University","keywords":"Data envelopment analysis; Cardinality (data modeling); Set (abstract data type); Dimension (graph theory); Data set; Mathematical optimization; Computer science; Function (biology); Point (geometry); Scale (ratio); Computation; Standard deviation; Mathematics; Statistics; Data mining; Algorithm","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":["metaresearch","scholarly_communication","open_science"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.08716878,0.00009797078,0.0002874672,0.001886173,0.0006417206,0.001279756,0.006000709,0.00001707805,0.0007515657],"category_scores_gemma":[0.02200318,0.00006440389,0.0000565845,0.003903005,0.0006470743,0.000945685,0.003358688,0.0004051732,0.0005014474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003555484,"about_ca_system_score_gemma":0.0005744823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001296999,"about_ca_topic_score_gemma":0.0001035459,"domain_scores_codex":[0.9891543,0.003437767,0.001152186,0.0006668714,0.005310028,0.0002788527],"domain_scores_gemma":[0.9914483,0.001942473,0.0002921707,0.002364642,0.003699127,0.0002532602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002527933,0.000432919,0.0398113,0.000003961771,0.001667436,0.0005947604,0.00177454,0.0008589783,0.004748493,0.001331864,0.3505497,0.5979733],"study_design_scores_gemma":[0.0005430737,0.0004115176,0.240724,0.00002868058,0.0001940445,0.0001189726,0.0006738202,0.07235913,0.0002736837,0.0005557688,0.6839064,0.0002110042],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6791431,0.001438408,0.2511603,0.02413759,0.001073444,0.0002454305,0.000645966,0.00001616628,0.04213957],"genre_scores_gemma":[0.9856035,0.0001018472,0.01114085,0.000187416,0.001151729,1.13816e-7,0.00006948681,0.00001024368,0.001734845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5977623,"threshold_uncertainty_score":0.999757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6518505430652791,"score_gpt":0.5397122734197641,"score_spread":0.112138269645515,"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."}}