{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002127698,0.0004371437,0.0007086904,0.001516885,0.0003885777,0.001243642,0.0007040704,0.0007741075,0.0005720935],"category_scores_gemma":[0.006988528,0.0003169103,0.0006217328,0.001404368,0.0002441262,0.001524363,0.00113963,0.001034262,0.00009689414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008629893,"about_ca_system_score_gemma":0.0007069955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01581791,"about_ca_topic_score_gemma":0.01411136,"domain_scores_codex":[0.9994788,0.0002273752,0.0000516345,0.0001384768,0.00005690058,0.00004692465],"domain_scores_gemma":[0.9983304,0.0006531279,0.0004969247,0.0001689175,0.0002426728,0.0001080241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004842692,0.00003160469,0.05571438,0.00003346453,0.00009171358,0.0001837894,0.0002322385,0.9033116,0.0004778906,0.009122213,0.0008217123,0.02993098],"study_design_scores_gemma":[0.000004243682,0.00001381893,0.007709358,0.000009924074,0.00001334323,0.000009403948,0.00005958324,0.9870149,0.0001323371,0.004751659,0.0002712183,0.00001015178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7131358,0.0005072872,0.2809672,0.001240047,0.00007430827,0.00006710031,0.0009763219,0.0001894816,0.002842538],"genre_scores_gemma":[0.9778555,0.000258976,0.02050915,0.00006774892,0.00004075359,0.00002971409,0.000792158,0.00001150888,0.0004344919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01581791,"threshold_uncertainty_score":0.0314517,"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."}}