{"id":"W2952077562","doi":"10.48550/arxiv.1311.5685","title":"Data Challenges in High-Performance Risk Analytics","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":"Analytics; Pipeline (software); Risk management; Risk analysis (engineering); Data analysis; Portfolio; Data science; Computer science; Business; Finance; Data mining","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001993098,0.0002945079,0.0005042235,0.0008019757,0.0001167345,0.0001735013,0.003670654,0.000389851,0.000246001],"category_scores_gemma":[0.0005395308,0.0002758123,0.00008263721,0.001059314,0.0001272112,0.0009044611,0.002810429,0.0007079429,0.0007961672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001000276,"about_ca_system_score_gemma":0.0001743361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006363007,"about_ca_topic_score_gemma":0.0007055926,"domain_scores_codex":[0.9968581,0.0003277336,0.0005292212,0.001618442,0.0003307243,0.0003357564],"domain_scores_gemma":[0.9946976,0.0004238202,0.0007194842,0.003705231,0.0003061544,0.0001476545],"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":[0.00002444538,0.00006014739,0.1083264,0.000009780401,0.00003232444,0.00004326706,0.0001086311,0.8697368,2.07711e-7,0.004878591,0.001620003,0.01515939],"study_design_scores_gemma":[0.0003217871,0.00002575626,0.08970676,0.00003933458,0.00006237654,9.736463e-7,0.0002587884,0.8642707,0.000004153078,0.03988858,0.005090921,0.0003298549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9785902,0.0004626495,0.01336121,0.0001484298,0.0006781174,0.0002856544,0.0002192787,0.00006315377,0.006191297],"genre_scores_gemma":[0.9149415,0.08198483,0.0005590988,0.00001838862,0.00009598467,5.275343e-7,0.0001219582,0.00001696942,0.002260692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08152217,"threshold_uncertainty_score":0.9999818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3791707720152268,"score_gpt":0.2781383826847389,"score_spread":0.1010323893304879,"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."}}