{"id":"W2609002022","doi":"10.1080/10920277.2017.1283236","title":"The Optimal Write-Down Coefficients in a Percentage for a Catastrophe Bond","year":2017,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bond; Statistics; Mathematics; Econometrics; Actuarial science; Psychology; Economics; Finance","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":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002186375,0.0001797133,0.000345602,0.0001974026,0.002262904,0.002540679,0.0017821,0.00003639226,0.00005555911],"category_scores_gemma":[0.002629195,0.0001070557,0.0001986678,0.0002901451,0.0005563955,0.0005064712,0.0001459482,0.0003651494,0.00005254564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008038299,"about_ca_system_score_gemma":0.0002951863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002559286,"about_ca_topic_score_gemma":0.001357952,"domain_scores_codex":[0.9971057,0.0001434826,0.0007873529,0.0003435154,0.001071475,0.0005485312],"domain_scores_gemma":[0.9967892,0.0006355857,0.001245322,0.0007600688,0.0003109427,0.0002589466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008400397,0.0001092911,0.2934796,4.479676e-7,0.00002520986,0.00004780816,0.0008999737,0.009785631,0.00002646701,0.00006071536,0.01718422,0.6775406],"study_design_scores_gemma":[0.002851112,0.0006640644,0.7771618,0.00001199152,0.00003544771,0.0001429083,0.002345779,0.01880936,0.00002657712,0.0002927575,0.1973066,0.0003516166],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9534447,0.00003238371,0.04283304,0.00128721,0.001461092,0.0003399045,0.0000740555,0.000009662268,0.0005179442],"genre_scores_gemma":[0.9936184,0.000267298,0.00488319,0.0001572276,0.0006679629,0.00001346821,0.00001002389,0.00001713601,0.0003653528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.677189,"threshold_uncertainty_score":0.999036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04525110107078818,"score_gpt":0.3746986786434841,"score_spread":0.3294475775726959,"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."}}