{"id":"W2128761671","doi":"10.5267/j.msl.2012.12.010","title":"Measuring liquidity risk in Social Security using VaR technique","year":2013,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Market liquidity; Computer science; Social security; Business; Econometrics; Risk analysis (engineering); Finance; Economics","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":[],"consensus_categories":[],"category_scores_codex":[0.00203053,0.0001696217,0.0002575427,0.0007796175,0.0005114893,0.0002196069,0.000645915,0.00004709479,0.00009194673],"category_scores_gemma":[0.00003473836,0.0002057044,0.00008106799,0.001248837,0.0002557533,0.000982385,0.0003214335,0.000213343,0.0003924763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004247137,"about_ca_system_score_gemma":0.000004435245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001610881,"about_ca_topic_score_gemma":0.00001755239,"domain_scores_codex":[0.9980578,0.00002173242,0.0004840936,0.0006364285,0.0001523894,0.0006474909],"domain_scores_gemma":[0.9993206,0.000008022813,0.000273299,0.0003326959,0.00001399486,0.00005133639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000236288,0.0003877739,0.6821854,0.0002533042,0.00006429083,0.00007439953,0.002902047,0.003443105,0.003512272,0.293543,0.005171295,0.008439519],"study_design_scores_gemma":[0.000713907,0.00003112394,0.8938001,0.00005241661,0.00001170719,0.000001166028,0.000307339,0.01404501,0.0006522989,0.07937389,0.01018684,0.0008241713],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9521956,0.00003744258,0.02856152,0.001179303,0.0003509134,0.0009179706,0.000008244202,0.0000574442,0.01669151],"genre_scores_gemma":[0.9956132,0.00005531781,0.002875001,0.001144094,0.0001009641,0.0001502162,9.357065e-7,0.00001408723,0.00004622303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2141691,"threshold_uncertainty_score":0.8388383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03532416401906329,"score_gpt":0.2206123599158678,"score_spread":0.1852881958968045,"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."}}