{"id":"W3182358347","doi":"10.5267/j.ijdns.2021.5.003","title":"Ranking DMUs using a novel combination method for integrating the results of relative closeness benevolent and relative closeness aggressive models","year":2021,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Closeness; Ranking (information retrieval); Weighting; Computer science; Mathematical optimization; Relative standard deviation; Matrix (chemical analysis); Data mining; Mathematics; Econometrics; Statistics; Machine learning; Medicine","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"],"consensus_categories":[],"category_scores_codex":[0.01633017,0.0001367934,0.0003724497,0.0003482948,0.0005015552,0.000487364,0.001915172,0.0000541175,0.000002209746],"category_scores_gemma":[0.0125838,0.00008579572,0.00009234408,0.001443534,0.0006246452,0.002794555,0.0009210213,0.0002536135,1.105928e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007929056,"about_ca_system_score_gemma":0.0005441148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004147539,"about_ca_topic_score_gemma":0.00006297774,"domain_scores_codex":[0.9954416,0.0003640929,0.001252445,0.0005555659,0.002177071,0.0002092031],"domain_scores_gemma":[0.9846411,0.007101877,0.002912089,0.0004626525,0.004805028,0.00007720698],"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":[0.001157393,0.0003899453,0.002973738,0.00001506149,0.0006146525,0.00006237462,0.01168354,0.4487366,0.02824795,0.2400008,0.0002307343,0.2658872],"study_design_scores_gemma":[0.0009833724,0.00006149175,0.0008501234,0.0004659545,0.00008903252,0.0002150847,0.001503366,0.8812954,0.0013045,0.1129411,0.0001921405,0.00009836227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1575791,0.00107994,0.8387043,0.001548295,0.0006883854,0.0001023904,0.000134461,0.000002296138,0.0001608612],"genre_scores_gemma":[0.8327101,0.0001221194,0.1668813,0.00008684315,0.0001637103,8.119754e-7,0.00001066873,0.000005153533,0.00001930274],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.675131,"threshold_uncertainty_score":0.9957336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1816899349107892,"score_gpt":0.4589271016995443,"score_spread":0.2772371667887551,"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."}}