{"id":"W2341552706","doi":"10.1080/03155986.2016.1149313","title":"An improved cross-ranking method in data envelopment analysis","year":2016,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Ranking (information retrieval); Data envelopment analysis; Matrix (chemical analysis); Computer science; Statistics; Confidence interval; Mathematics; Data mining; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0423679,0.0001290918,0.0003371734,0.002841667,0.0005327076,0.003385277,0.001385401,0.0001147773,0.000180449],"category_scores_gemma":[0.00590704,0.00007500216,0.00005301257,0.003593522,0.0001595353,0.009081658,0.0004264959,0.0001773519,0.0002536008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001533697,"about_ca_system_score_gemma":0.0004607188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009421915,"about_ca_topic_score_gemma":0.0004667486,"domain_scores_codex":[0.9932536,0.0006682069,0.001728205,0.0004465741,0.00351213,0.0003912224],"domain_scores_gemma":[0.9937472,0.002422166,0.0002479425,0.001205584,0.002207594,0.0001694521],"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":[0.0002947896,0.0001668093,0.3315988,0.00005357196,0.0004880055,0.000006059088,0.0104637,0.1101774,0.003274342,0.0800004,0.00282447,0.4606517],"study_design_scores_gemma":[0.0005472543,0.00003928327,0.07284623,0.00002445069,0.00001144085,0.00000502577,0.001348835,0.8625702,0.0001007648,0.0002831234,0.06205867,0.0001647524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.292366,0.0001326068,0.6976056,0.001860141,0.0002844021,0.000984445,0.0004099907,0.00004755079,0.006309329],"genre_scores_gemma":[0.9949079,0.00002032352,0.003836674,0.0001172157,0.00005741585,0.00004838098,0.0001614484,0.000003979252,0.0008467143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7523927,"threshold_uncertainty_score":0.9976493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2656549513857942,"score_gpt":0.5425122075028956,"score_spread":0.2768572561171014,"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."}}