{"id":"W2951405180","doi":"10.48550/arxiv.1310.2274","title":"Accounting for Secondary Uncertainty: Efficient Computation of Portfolio Risk Measures on Multi and Many Core Architectures","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Aggregate (composite); Portfolio; Computation; Event (particle physics); Fraction (chemistry); Systematic risk; Multi-core processor; Core (optical fiber); Uncertainty analysis; Data mining; Econometrics; Parallel computing; Algorithm; Finance; Simulation; Mathematics","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":[0.001142028,0.0006993368,0.0008660417,0.0005530653,0.0004382473,0.001457317,0.0009856011,0.0006767561,0.001676823],"category_scores_gemma":[0.003873337,0.0003944902,0.0006116389,0.0007375993,0.000454518,0.001259648,0.0009939957,0.00100831,0.0003041643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008972889,"about_ca_system_score_gemma":0.001412643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007128917,"about_ca_topic_score_gemma":0.005195107,"domain_scores_codex":[0.9994549,0.000171194,0.00003358716,0.00007476922,0.000185729,0.00007971799],"domain_scores_gemma":[0.9988423,0.000561421,0.0001076445,0.0001654412,0.0002323357,0.00009089107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001470496,0.00004886477,0.002090577,0.00003696707,0.00005646167,0.0001216033,0.00005799333,0.9314482,0.002279548,0.009406687,0.0009782483,0.05332772],"study_design_scores_gemma":[0.000005159764,0.000007706753,0.0001381865,0.000001266334,0.000002392158,0.000007133891,0.000004671191,0.9958326,0.0002903871,0.003578422,0.0001307914,0.000001219282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1618129,0.0004073203,0.8324687,0.0004292903,0.0000520717,0.00005630454,0.00007480625,0.0006645825,0.004033941],"genre_scores_gemma":[0.721136,0.000227759,0.2763587,0.00007948564,0.00003911755,0.00008255793,0.0001522006,0.00008980622,0.001834307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007128917,"threshold_uncertainty_score":0.01417488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1465893257275752,"score_gpt":0.2756830555994775,"score_spread":0.1290937298719023,"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."}}