{"id":"W2080128529","doi":"10.1016/j.omega.2011.07.012","title":"Generalized symmetric weight assignment technique: Incorporating managerial preferences in data envelopment analysis using a penalty function","year":2012,"lang":"en","type":"article","venue":"Omega","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Data envelopment analysis; Generalization; Decision maker; Preference; Navy; Workforce management; Computer science; Function (biology); Operations research; Mathematical optimization; Penalty method; Mathematics; Workforce; Economics; Statistics","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":[0.01264911,0.0002675247,0.0006682529,0.003661688,0.0002950241,0.0004103059,0.00149722,0.0001387152,0.0004116867],"category_scores_gemma":[0.001344913,0.0002007266,0.0001673634,0.01415882,0.0000926457,0.001268108,0.0008326365,0.0002199693,0.0000814087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003119949,"about_ca_system_score_gemma":0.0001378817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003739086,"about_ca_topic_score_gemma":0.0002883673,"domain_scores_codex":[0.9939243,0.0008810977,0.001334465,0.0009695688,0.002338793,0.0005518329],"domain_scores_gemma":[0.9966682,0.0005448298,0.0008308786,0.001626699,0.0001565399,0.0001728882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000247348,0.001455406,0.8397003,0.00002684402,0.001332844,0.00003000787,0.001171807,0.02554433,0.02042479,0.005416842,0.002667859,0.1019816],"study_design_scores_gemma":[0.002330045,0.0001833328,0.2991515,0.0001243995,0.003722562,0.00002126784,0.001668868,0.63868,0.006785312,0.01657584,0.02841509,0.002341842],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6896102,0.0004449143,0.306153,0.0001292182,0.0006584894,0.0004397668,0.00002492758,0.00006556533,0.002473813],"genre_scores_gemma":[0.9632339,0.00001538235,0.03603914,0.00007365734,0.0001961188,0.00003533516,0.00008682731,0.00001300488,0.0003066296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6131356,"threshold_uncertainty_score":0.8185394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1647779207372587,"score_gpt":0.3796764557013751,"score_spread":0.2148985349641165,"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."}}