{"id":"W2357074492","doi":"","title":"Quantitative Method of Manager Selection for the Social Security Fund","year":2009,"lang":"en","type":"article","venue":"Science Technology and Engineering","topic":"Evaluation Methods in Various Fields","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Analytic hierarchy process; Selection (genetic algorithm); Investment management; Social security; Sharpe ratio; Investment (military); Business; Manager of managers fund; Process (computing); Preference; Actuarial science; Investment fund; Hierarchy; Quarter (Canadian coin); Corporate governance; Computer science; Finance; Operations research; Economics; Microeconomics; Portfolio; Engineering; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001422138,0.00004673316,0.00006397409,0.000139876,0.0002666232,0.000008323237,0.000178759,0.00006719081,0.00002184924],"category_scores_gemma":[0.0002952161,0.0000365636,0.00001406532,0.001121257,0.0003359109,0.0001091816,0.00004794153,0.0001095739,0.000001281208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002893678,"about_ca_system_score_gemma":0.000006203339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005645893,"about_ca_topic_score_gemma":0.000003970068,"domain_scores_codex":[0.9995117,0.00001042805,0.00008146846,0.000145942,0.0001115246,0.0001389846],"domain_scores_gemma":[0.9997439,0.0001277875,0.00003313404,0.00006983605,0.00001374102,0.00001155501],"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":[0.00001170851,0.00001927324,0.001980465,0.00001045578,0.000008381562,1.48459e-7,0.001644316,0.01728044,0.3935397,0.4905147,0.0000986538,0.09489172],"study_design_scores_gemma":[0.0002168341,0.0002996886,0.03918552,0.000007605089,0.00002997757,0.00001394436,0.0009093728,0.7428395,0.08250875,0.1324721,0.001340027,0.0001767385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1650926,0.00003192858,0.8323563,0.00160383,0.00006313989,0.0001341521,4.899918e-7,0.00005505809,0.0006624413],"genre_scores_gemma":[0.7139328,0.000003477678,0.2859977,0.00003064866,0.000005168553,0.00000759404,4.27235e-8,0.000001426616,0.00002108568],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7255591,"threshold_uncertainty_score":0.2050677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03242224871437216,"score_gpt":0.3771672933235019,"score_spread":0.3447450446091297,"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."}}