{"id":"W2620234624","doi":"","title":"On the Minimization of Regulatory Margin Requirements for Portfolios of Financial Securities","year":2011,"lang":"en","type":"dissertation","venue":"Library and Archives Canada (Government of Canada)","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Margin (machine learning); Business; Actuarial science; Computer science; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.00002064243,0.0001594519,0.0002535597,0.00003851143,0.00008792737,0.00001009688,0.0005511449,0.00004578269,0.00001812551],"category_scores_gemma":[0.00001926433,0.0001363152,0.00004631038,0.0000721358,0.00007403555,0.0002357412,0.00006440836,0.00007694662,7.477938e-10],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004618717,"about_ca_system_score_gemma":0.00182784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001127955,"about_ca_topic_score_gemma":0.008885982,"domain_scores_codex":[0.9980091,0.00004149806,0.0004530585,0.0002099691,0.001124297,0.000162065],"domain_scores_gemma":[0.9987625,0.0003115867,0.000567949,0.0002980708,0.000003649386,0.00005617931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004234925,0.00003031361,0.0004093587,0.0003866824,0.00003925816,0.000003223816,0.0006485856,0.00002951619,0.001861487,0.9795037,0.002663174,0.0140012],"study_design_scores_gemma":[0.0001236353,0.0002552992,0.01608169,0.0007579387,0.00003047189,6.781457e-7,0.00278304,0.003016395,0.9101495,0.06414758,0.002362096,0.0002917238],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7021353,0.00103897,0.004277722,0.001140512,0.002650327,0.00182783,0.0008691186,0.000024743,0.2860355],"genre_scores_gemma":[0.9899226,0.00008878014,0.001473212,0.0002650179,0.00003207899,0.00002354656,0.00002322755,0.00001547395,0.008156097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9153562,"threshold_uncertainty_score":0.5558773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01004027917394761,"score_gpt":0.180298832194349,"score_spread":0.1702585530204014,"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."}}