{"id":"W4385950151","doi":"10.3389/fgene.2023.1118308","title":"Feet and legs malformation in Nellore cattle: genetic analysis and prioritization of GWAS results","year":2023,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Biology; Single-nucleotide polymorphism; Candidate gene; Genome-wide association study; Genetics; Genetic architecture; Best linear unbiased prediction; Intramuscular fat; Genetic correlation; Quantitative trait locus; Beef cattle; Gene; Genetic variation; Selection (genetic algorithm); Genotype; Animal science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001727516,0.0001165187,0.0001878182,0.000305865,0.00002551351,0.00001192865,0.00009351419,0.0001506183,0.000001130561],"category_scores_gemma":[0.00004725804,0.0001286,0.00002734301,0.0006224401,0.0001102079,0.000003646479,0.00007879349,0.00005943059,5.162863e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009296707,"about_ca_system_score_gemma":0.00003307777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002888127,"about_ca_topic_score_gemma":0.0001232354,"domain_scores_codex":[0.9990335,0.00005496018,0.0003499226,0.0002756439,0.0001032241,0.0001828034],"domain_scores_gemma":[0.9996009,0.00001052204,0.00008942932,0.0002139635,0.00003726676,0.00004789396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001178385,0.00003729953,0.9289764,0.00008254423,0.0001000239,0.000001067549,0.001128635,0.04219902,0.004185475,0.00007006944,0.001547611,0.02155408],"study_design_scores_gemma":[0.0007149068,0.0001404137,0.9893217,0.00001272482,0.00006094734,0.000001747008,0.0003476251,0.004258889,0.003569123,0.0009152557,0.000518157,0.000138488],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9775473,0.002058092,0.01978432,0.00005573436,0.0001499676,0.0001868477,0.00004516089,0.000006068582,0.0001664333],"genre_scores_gemma":[0.9464522,0.001971986,0.05113695,0.00002510127,0.00003910475,0.000009208442,0.0002120243,0.0000117035,0.0001417061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06034539,"threshold_uncertainty_score":0.5244156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006074339362986526,"score_gpt":0.2217584750132903,"score_spread":0.2156841356503038,"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."}}