{"id":"W2949773839","doi":"10.48550/arxiv.1311.5686","title":"High Performance Risk Aggregation: Addressing the Data Processing Challenge the Hadoop MapReduce Way","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); Exploit; Distributed File System; Big data; Process (computing); Set (abstract data type); Data set; Scope (computer science); Distributed computing; Database; Data mining; Parallel computing; Operating system","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":["sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.002873453,0.0003822196,0.0003838215,0.0002274264,0.001560686,0.001084995,0.006081264,0.0003137384,0.0002809837],"category_scores_gemma":[0.0005903505,0.0002249125,0.0001276383,0.001131185,0.000469604,0.001560199,0.003210023,0.001095533,0.000402789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000864621,"about_ca_system_score_gemma":0.0003224652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004361602,"about_ca_topic_score_gemma":0.00008088772,"domain_scores_codex":[0.9963331,0.0006384626,0.0005596331,0.001487723,0.0005795809,0.0004014612],"domain_scores_gemma":[0.9930064,0.0006572276,0.001440204,0.004108022,0.0006680653,0.0001200642],"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":[0.00004795431,0.00006133605,0.006936885,0.00002651131,0.00006561825,0.00001816957,0.001368321,0.8021601,0.000001529404,0.002872861,0.007067962,0.1793727],"study_design_scores_gemma":[0.0002590378,0.00002350299,0.009683902,0.0001648221,0.000168407,0.000005416114,0.001172862,0.9569346,0.00003070343,0.02305184,0.00812606,0.000378886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9191169,0.001415464,0.06898174,0.001721965,0.001559183,0.0008970237,0.0001301501,0.0001553295,0.0060223],"genre_scores_gemma":[0.9856247,0.008604334,0.0005747099,0.00009154451,0.0004453058,0.000003332774,0.00009840894,0.00002904893,0.004528612],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1789938,"threshold_uncertainty_score":0.999952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3497576000742727,"score_gpt":0.2873531752415266,"score_spread":0.06240442483274611,"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."}}