{"id":"W4238654454","doi":"10.4095/301520","title":"Sex Composition (female) by Marital Status, 2006 - Separated (by census subdivision)","year":2010,"lang":"en","type":"report","venue":"","topic":"Demographic Trends and Gender Preferences","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Subdivision; Census; Composition (language); Geography; Demography; Sociology; Archaeology; Art; 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":[0.0002313537,0.0002772475,0.000224502,0.001901757,0.000614123,0.0004946794,0.000633608,0.0001657438,0.008005119],"category_scores_gemma":[0.001067679,0.0001744061,0.0003260613,0.002694415,0.0001354229,0.0003186539,0.0004337884,0.0002879389,0.003130184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001186188,"about_ca_system_score_gemma":0.001633638,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5687671,"about_ca_topic_score_gemma":0.7142026,"domain_scores_codex":[0.9996477,0.00002377083,0.0000367815,0.00004732107,0.0001608478,0.00008343611],"domain_scores_gemma":[0.9992875,0.00002403799,0.0001558661,0.00002595376,0.0003842984,0.0001223328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001386451,0.0000853629,0.9266339,0.0001424236,0.0001079131,0.0001745012,0.001205826,0.0002527656,0.0007851013,0.0003446116,0.04507923,0.02504986],"study_design_scores_gemma":[0.000005796018,0.00002108437,0.9880772,0.00002557997,0.000009494538,0.0001142739,0.0008054046,0.000100506,0.00008330517,0.00002294323,0.0107295,0.00000490779],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.672166,0.002033092,0.000409125,0.0003093632,0.00009969918,0.0002610312,0.2875815,0.0001362198,0.03700392],"genre_scores_gemma":[0.7899718,0.003667299,0.0008771363,0.0003042838,0.00005213244,0.0002155001,0.1575461,0.00006088004,0.04730475],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5687671,"threshold_uncertainty_score":0.8675449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04015468685078779,"score_gpt":0.3465227077873705,"score_spread":0.3063680209365827,"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."}}