{"id":"W2183901607","doi":"","title":"An iterative method of estimating excess death rates and mortality ratios.","year":2000,"lang":"en","type":"article","venue":"PubMed","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Statistics; Mortality rate; Excess mortality; Mathematics; Population; Econometrics; Demography; Medicine; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002511781,0.0001221558,0.0002331059,0.0000730082,0.0003434574,0.0001517817,0.0002128327,0.00005658794,0.000107057],"category_scores_gemma":[0.0001119054,0.0001165919,0.00005212751,0.0003183778,0.0002439922,0.0005617028,0.00002176369,0.00008007338,0.000001756537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002806567,"about_ca_system_score_gemma":0.00002395369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003362214,"about_ca_topic_score_gemma":0.001342377,"domain_scores_codex":[0.9980602,0.0006144907,0.0003005818,0.0003006765,0.0003721115,0.0003519722],"domain_scores_gemma":[0.9992974,0.0001217119,0.0001354806,0.000221713,0.00009059716,0.0001331115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001641197,0.0001267365,0.5935758,0.00004688173,0.00009611468,0.000005055518,0.01196225,0.0003353612,0.00000764693,0.01481443,0.00008891365,0.3789243],"study_design_scores_gemma":[0.0001921204,0.00001278583,0.9893804,0.000006194594,0.00004473081,3.246585e-7,0.0009167994,0.001428624,0.0001791157,0.006907507,0.0007825771,0.000148852],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9597603,0.0001187772,0.003747874,0.0001468585,0.0001259332,0.0008978481,0.00001634269,0.00007121702,0.03511486],"genre_scores_gemma":[0.9841906,0.00006371274,0.0148738,0.000123735,0.0001364555,0.0002718001,0.000006113138,0.000009036209,0.0003247429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3958045,"threshold_uncertainty_score":0.5082684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04543926280626245,"score_gpt":0.363815909410572,"score_spread":0.3183766466043095,"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."}}