{"id":"W3194943772","doi":"10.3390/risks9090151","title":"Coherent Mortality Forecasting for Less Developed Countries","year":2021,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Society of Actuaries","keywords":"Developing country; Convergence (economics); Developed country; China; Development economics; Term (time); Mortality rate; Population; Population projection; Socioeconomic status; Projections of population growth; Economics; Geography; Econometrics; Economic growth; Demography; Population growth","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":[0.001423936,0.0001534921,0.0002533094,0.00005385777,0.0009211649,0.0002043432,0.0002641408,0.0001040417,0.0001432519],"category_scores_gemma":[0.0003419836,0.000161153,0.0001544902,0.0003752043,0.0002385194,0.0001823355,0.00008745532,0.0001114486,0.00002169729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001201354,"about_ca_system_score_gemma":0.0003188223,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003532833,"about_ca_topic_score_gemma":0.04190336,"domain_scores_codex":[0.9980069,0.0001921099,0.0003396452,0.0003775898,0.0005527715,0.0005309558],"domain_scores_gemma":[0.9988211,0.0001916963,0.0001605163,0.0002830768,0.0004376837,0.000105958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002658509,0.0001443932,0.8499333,0.0001972212,0.0002666952,0.00005095567,0.007053989,0.00008023935,0.00001078281,0.1012298,0.008628979,0.03237709],"study_design_scores_gemma":[0.0007818269,0.0000266615,0.4497508,0.00007533739,0.0001599297,0.000001336647,0.01279014,0.000397332,0.0005085102,0.01254625,0.5224183,0.0005436242],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9483143,0.0005459737,0.005832706,0.000871684,0.001315601,0.0008762119,0.0000890962,0.0001697215,0.0419847],"genre_scores_gemma":[0.9952077,0.0003410998,0.002212155,0.0003358726,0.0003589852,0.0001579942,0.00003388113,0.00001984303,0.001332438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5137894,"threshold_uncertainty_score":0.9755794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2902981720674551,"score_gpt":0.4170142000064221,"score_spread":0.126716027938967,"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."}}