{"id":"W4238181502","doi":"10.4095/301310","title":"Age Structure, 2006 - Oldest Old by Census Subdivision (80 years of age and older)","year":2010,"lang":"en","type":"report","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Subdivision; Age structure; Geography; Demography; Gerontology; Genealogy; History; Archaeology; Medicine; Sociology; Population","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007111108,0.0009379328,0.0005882899,0.004791193,0.001820442,0.001270934,0.001930878,0.0005178005,0.01189162],"category_scores_gemma":[0.005521685,0.0003799349,0.0008005293,0.009146521,0.0002900176,0.000971175,0.001204436,0.001554329,0.005872575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008344249,"about_ca_system_score_gemma":0.01672746,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.940257,"about_ca_topic_score_gemma":0.9475355,"domain_scores_codex":[0.9989777,0.00003931305,0.0001199543,0.000131274,0.0004767132,0.0002550949],"domain_scores_gemma":[0.9943306,0.00006778481,0.0005398263,0.00009600815,0.00450909,0.0004566602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001905977,0.00007769693,0.2439206,0.0007852334,0.000107007,0.00007930345,0.0005605544,0.0005378718,0.0002246966,0.00115223,0.7299454,0.02241882],"study_design_scores_gemma":[0.00005079878,0.00003740384,0.9090613,0.0003100537,0.000046,0.0001619609,0.0007960529,0.0004410325,0.00009407029,0.0002302127,0.08873943,0.00003169353],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.02011545,0.001140516,0.0002618734,0.0004580276,0.000181996,0.0002582618,0.9647117,0.0002154572,0.01265674],"genre_scores_gemma":[0.0783124,0.002501889,0.001775766,0.0005979426,0.0001184286,0.0006006985,0.8999921,0.0001203509,0.0159804],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05974299,"threshold_uncertainty_score":0.1201896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01994488845022652,"score_gpt":0.3099132836312891,"score_spread":0.2899683951810625,"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."}}