{"id":"W6966317216","doi":"10.3886/e164641","title":"Data and Code for: Global Life Insurers during a Low Interest Rate Environment","year":2022,"lang":"en","type":"dataset","venue":"ICPSR Data Holdings","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Booth University College","funders":"","keywords":"Interest rate; Life insurance; Debt; Fragility; Stock (firearms); Interest rate risk; Financial crisis; European union","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007940642,0.00145522,0.0008477374,0.003036058,0.0006315272,0.002370083,0.001814462,0.002230349,0.08594351],"category_scores_gemma":[0.006362826,0.0005705547,0.001005772,0.005698664,0.0003981516,0.001686597,0.001958973,0.001730286,0.1081757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001314814,"about_ca_system_score_gemma":0.001842084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03774154,"about_ca_topic_score_gemma":0.05407101,"domain_scores_codex":[0.9991997,0.0001056959,0.0001224231,0.0002253497,0.0001974986,0.0001493252],"domain_scores_gemma":[0.9976016,0.0005663045,0.0004202443,0.0004968246,0.000665519,0.0002495454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003225002,0.00001157768,0.001189042,0.0002483522,0.00001386002,0.00001944463,0.0000200668,0.0001719644,0.00004683369,0.0003531498,0.9967482,0.001145312],"study_design_scores_gemma":[0.0002059426,0.00001368332,0.009650782,0.0003299036,0.00001941066,0.00007758571,0.0001175042,0.0004968306,0.0002010569,0.001088555,0.987762,0.00003674374],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009263285,0.0000239968,0.0000244739,0.00006671235,0.0000164056,0.000004919527,0.9992655,0.0001211875,0.0003842351],"genre_scores_gemma":[0.0003916423,0.0000354021,0.0001245557,0.00005835378,0.000008810463,0.00003867583,0.9986351,0.00005592615,0.0006516127],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08594351,"threshold_uncertainty_score":0.2875097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1007693642459523,"score_gpt":0.3175230584669221,"score_spread":0.2167536942209697,"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."}}