{"id":"W4416273650","doi":"10.1016/j.insmatheco.2025.103183","title":"A one-step approach for determining the optimal aggregate capital reserve and allocation","year":2025,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Actua; Royal Bank of Canada; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Capital allocation line; Aggregate (composite); Capital (architecture); Function (biology); Optimal allocation; Yield (engineering)","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.00110836,0.00009303677,0.0002070275,0.00008177605,0.0002288779,0.0003863483,0.0002158649,0.00005785902,0.00000150756],"category_scores_gemma":[0.0003935611,0.00006794887,0.000040028,0.0001000685,0.0000873754,0.0002222015,0.00007553407,0.0000511753,0.000001826139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001216551,"about_ca_system_score_gemma":0.00002879459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008245606,"about_ca_topic_score_gemma":0.00001909273,"domain_scores_codex":[0.9991011,0.00001642107,0.0004347819,0.0002428934,0.00007667302,0.0001281432],"domain_scores_gemma":[0.9988108,0.0005381631,0.0002311662,0.0002738948,0.0001128104,0.00003311195],"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":[0.0001781992,0.0003845844,0.09401225,0.0002606245,0.0001991551,4.570861e-7,0.0138689,0.06617504,0.00004926576,0.2433059,0.00169959,0.579866],"study_design_scores_gemma":[0.0005091944,0.00003178287,0.01142759,0.00002024542,0.00001692602,0.000002494211,0.001132307,0.9462383,0.00006420823,0.03911376,0.001324593,0.0001185701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8813294,0.000190617,0.1158183,0.0003402104,0.00007830811,0.0003271706,0.00002171253,0.000008763895,0.001885522],"genre_scores_gemma":[0.8913394,0.0008472156,0.1072288,0.0001147447,0.00003074199,0.00005309902,0.000007824483,0.000008610492,0.0003695177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8800633,"threshold_uncertainty_score":0.3725566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06640886120401635,"score_gpt":0.3158241150233445,"score_spread":0.2494152538193282,"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."}}