{"id":"W4243499621","doi":"10.4095/301492","title":"Sex Composition (male) by Age, 2006 - Golden Years by Census Subdivision (65 - 79 years old)","year":2010,"lang":"en","type":"report","venue":"","topic":"Demographic Trends and Gender Preferences","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Subdivision; Composition (language); Demography; Genealogy; Geography; Biology; History; Archaeology; Art; Sociology; Literature; 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":[0.0002730182,0.0004201028,0.000261879,0.002594247,0.0006060587,0.0004824463,0.0007067373,0.0001794977,0.008745856],"category_scores_gemma":[0.001170509,0.0001946388,0.0004326327,0.003347999,0.0001258369,0.0003474891,0.0004309059,0.0003587026,0.004572994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001646002,"about_ca_system_score_gemma":0.002626195,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6474353,"about_ca_topic_score_gemma":0.7706888,"domain_scores_codex":[0.9995511,0.00002298803,0.00004185826,0.0000521247,0.0002213122,0.0001106447],"domain_scores_gemma":[0.9990661,0.00002325266,0.0001436731,0.0000257007,0.0006334731,0.0001078656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000149449,0.00009410067,0.7925327,0.0003156868,0.0001258275,0.0001902075,0.0009919822,0.0003934166,0.0009436367,0.0005317418,0.1591059,0.04462529],"study_design_scores_gemma":[0.000009650664,0.00002820462,0.9728133,0.00005786975,0.00001782878,0.0001503828,0.0008350062,0.0001308666,0.0001436266,0.0000398609,0.0257658,0.000007748427],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.312198,0.002181914,0.0004985459,0.0003548814,0.0001836405,0.0004378531,0.6321573,0.0002698122,0.05171801],"genre_scores_gemma":[0.5251138,0.005759548,0.001407629,0.0004693959,0.00008307242,0.0005603459,0.3956048,0.00009496981,0.07090648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3525647,"threshold_uncertainty_score":0.709282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03916843319129757,"score_gpt":0.3272350160172798,"score_spread":0.2880665828259822,"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."}}