{"id":"W4248798710","doi":"10.4095/301375","title":"Marital Status, 2006: Single (by census subdivision)","year":2010,"lang":"en","type":"report","venue":"","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Subdivision; Census; Marital status; Geography; Computer science; Genealogy; Demography; Sociology; History; Archaeology; 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":[0.0007662533,0.0006347916,0.0004267116,0.002885196,0.00118344,0.0009692989,0.001247515,0.0003280425,0.01256953],"category_scores_gemma":[0.003174994,0.0003766716,0.000561395,0.00592158,0.0001495772,0.0005980466,0.0007526022,0.0008204666,0.005967953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004810408,"about_ca_system_score_gemma":0.008997841,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8879532,"about_ca_topic_score_gemma":0.9167361,"domain_scores_codex":[0.9991336,0.00002964274,0.00008196886,0.00007244856,0.0004964235,0.0001859162],"domain_scores_gemma":[0.9975281,0.0000449549,0.0003061423,0.00007098827,0.001783527,0.0002663801],"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":[0.0002276557,0.0001725884,0.3352101,0.0007240296,0.0001312911,0.0001150225,0.0009273179,0.001181572,0.0003373199,0.0009979362,0.6090767,0.05089843],"study_design_scores_gemma":[0.00004685622,0.00004299691,0.8877211,0.0001427259,0.0000300536,0.00008983376,0.0007647512,0.0005508698,0.0002223678,0.000103843,0.1102647,0.00001978576],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.03923446,0.0005928488,0.0002972242,0.0003260412,0.0001194568,0.0003311105,0.9455732,0.0001525842,0.01337311],"genre_scores_gemma":[0.1056684,0.002262314,0.001912479,0.0002735152,0.0000839265,0.0005551919,0.8593858,0.00006658078,0.02979188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1120468,"threshold_uncertainty_score":0.2254134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2002613850194397,"score_gpt":0.4077396177320232,"score_spread":0.2074782327125835,"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."}}