{"id":"W2121568877","doi":"10.1016/j.amc.2006.06.030","title":"A note on DEA efficiency assessment using ideal point: An improvement of Wang and Luo’s model","year":2006,"lang":"en","type":"article","venue":"Applied Mathematics and Computation","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":57,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"TOPSIS; Data envelopment analysis; Ranking (information retrieval); Ideal solution; Ideal (ethics); Computation; Ideal point; Computer science; Mathematical optimization; Similarity (geometry); Point (geometry); Mathematics; Operations research; Algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009944635,0.001450339,0.002078352,0.001935838,0.0007085378,0.00245587,0.002824642,0.001384342,0.00414462],"category_scores_gemma":[0.02046345,0.00049907,0.002044754,0.003220486,0.001687848,0.006302385,0.002708636,0.00355612,0.001359499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001221002,"about_ca_system_score_gemma":0.001744178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002593337,"about_ca_topic_score_gemma":0.002536323,"domain_scores_codex":[0.9927951,0.003818649,0.0004265456,0.0008181619,0.00195623,0.0001854138],"domain_scores_gemma":[0.9910876,0.004518434,0.0002718755,0.001879733,0.002115834,0.0001265317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001851348,0.0001671391,0.001328959,0.0004725405,0.0002670346,0.0002304906,0.0002886693,0.08496458,0.003015846,0.677579,0.01165328,0.2198474],"study_design_scores_gemma":[0.00004170914,0.0001770492,0.0009494704,0.0001084702,0.0001399613,0.0001864783,0.00007566449,0.4652914,0.003319261,0.4928225,0.03676264,0.0001254941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002257256,0.0008209094,0.9882422,0.001221597,0.0002562141,0.00004290876,0.00005725116,0.0001092747,0.006992425],"genre_scores_gemma":[0.1969862,0.003050343,0.7880695,0.0007720926,0.0006721832,0.0002566344,0.0001851489,0.0002553844,0.009752503],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009944635,"threshold_uncertainty_score":0.05259287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04995759405450428,"score_gpt":0.3713083180973101,"score_spread":0.3213507240428058,"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."}}