{"id":"W2139820031","doi":"10.4238/2013.october.29.6","title":"Genetic composition of a Brazilian population: the footprint of the Gold Cycle","year":2013,"lang":"en","type":"article","venue":"Genetics and Molecular Research","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nutrasource","funders":"","keywords":"Ancestry-informative marker; Genetic genealogy; Genetic admixture; Population; Genotyping; Demography; Skin color; Biology; Genetic structure; Markov chain Monte Carlo; Geography; Bayesian probability; Genetic variation; Allele; Allele frequency; Genetics; Genotype; Statistics; Mathematics; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001152537,0.0002076042,0.0004185428,0.0008008368,0.0007335948,0.0004418367,0.0003007743,0.0002194502,0.0008710681],"category_scores_gemma":[0.003350218,0.00018624,0.0002202972,0.0008802523,0.000902872,0.0002545068,0.0007731583,0.0001906889,0.00008933869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006165227,"about_ca_system_score_gemma":0.0006888197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05958451,"about_ca_topic_score_gemma":0.1040781,"domain_scores_codex":[0.9993844,0.0002379642,0.00002980179,0.0001601718,0.0001047646,0.00008295908],"domain_scores_gemma":[0.9993364,0.0002137106,0.0001827594,0.0001239216,0.00008568669,0.00005749047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002150127,0.00004033701,0.9215624,0.0000741491,0.0001014374,0.0004767254,0.01086317,0.0003002807,0.01254473,0.003658991,0.0002221631,0.04994071],"study_design_scores_gemma":[0.00001133473,0.0001017686,0.9909684,0.00006154845,0.00007164779,0.0006511867,0.00220899,0.0008409712,0.0005386642,0.001108648,0.003424303,0.00001256339],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970574,0.0003613764,0.0007810696,0.0001078979,0.000003354737,0.00001368183,0.00007177849,0.000005121114,0.001598365],"genre_scores_gemma":[0.998768,0.0002091413,0.0007479031,0.00003253394,0.000002337213,0.000008781135,0.00005466912,0.000004136671,0.0001723448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05958451,"threshold_uncertainty_score":0.1184754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01601772044231654,"score_gpt":0.2895284055946704,"score_spread":0.2735106851523538,"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."}}