{"id":"W4242534416","doi":"10.32920/ryerson.14662176","title":"Rearrangement Algorithm in Risk Aggregation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Risk measure; Measure (data warehouse); Aggregate (composite); Discretization; Expected shortfall; Exponential function; Econometrics; Portfolio; Actuarial science; Investment (military); Coherent risk measure; Economics; Mathematics; Spectral risk measure; Computer science; Financial economics; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003204214,0.0001846895,0.000364695,0.0005151356,0.00005913569,0.0006138245,0.0005142555,0.00027442,0.001753347],"category_scores_gemma":[0.001113413,0.0001451595,0.000152432,0.0007500054,0.00002731026,0.000198329,0.0005552028,0.0004321428,0.000186479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008129133,"about_ca_system_score_gemma":0.0001858359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001536593,"about_ca_topic_score_gemma":0.000960694,"domain_scores_codex":[0.9963451,0.0004304379,0.000907573,0.0007908585,0.001337394,0.000188648],"domain_scores_gemma":[0.9977725,0.0002587923,0.0005221098,0.000949967,0.0004231989,0.00007344176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003945941,0.00004903834,0.02185536,0.000001709443,0.0000108614,0.00001914112,0.0006585998,0.05084055,0.000002219992,0.00006138779,0.004859197,0.921638],"study_design_scores_gemma":[0.0007556895,0.00003950138,0.1130664,0.0001514207,0.00004624528,0.00000583853,0.002635058,0.7797072,0.0005854315,0.06296697,0.0393057,0.0007345522],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1559273,0.001232389,0.7900383,0.0007436691,0.002746223,0.00068867,0.00005072786,0.00008909916,0.04848361],"genre_scores_gemma":[0.4485496,0.03415237,0.4861591,0.0004873619,0.0007148606,0.0001934486,0.0007090113,0.00006364402,0.02897059],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9209034,"threshold_uncertainty_score":0.9991592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07475718654506924,"score_gpt":0.3766298443416548,"score_spread":0.3018726577965856,"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."}}