{"id":"W2891245798","doi":"10.1016/j.meatsci.2018.09.010","title":"Genomic selection for meat quality traits in Nelore cattle","year":2018,"lang":"en","type":"article","venue":"Meat Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Heritability; Biology; Trait; Beef cattle; Population; Tenderness; Best linear unbiased prediction; Selection (genetic algorithm); Meat tenderness; SNP; Genomic selection; Single-nucleotide polymorphism; Genetic correlation; Statistics; Genetics; Animal science; Genetic variation; Genotype; Mathematics; Medicine; Gene; Computer 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008880753,0.0003599281,0.0003148694,0.0006649541,0.0005026667,0.000537726,0.0004023659,0.000391434,0.001801516],"category_scores_gemma":[0.0005244588,0.0002184711,0.0002367109,0.0005230478,0.0005794753,0.0001607845,0.0003718589,0.0004066958,0.0001200197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000466639,"about_ca_system_score_gemma":0.0002788577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00556923,"about_ca_topic_score_gemma":0.01758532,"domain_scores_codex":[0.9994895,0.0001904399,0.00002180001,0.0001560254,0.00006846806,0.00007368971],"domain_scores_gemma":[0.9995726,0.000172063,0.00009198784,0.00002812216,0.00003369269,0.0001015817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.004663291,0.0006579488,0.1621574,0.0000683713,0.0004471292,0.001553086,0.001809038,0.0006292224,0.8115563,0.0009765789,0.0001898296,0.01529165],"study_design_scores_gemma":[0.0001154802,0.0004965375,0.9829359,0.00002230822,0.0001845798,0.0007873477,0.001046221,0.001476568,0.01154191,0.0002059185,0.001163441,0.0000237491],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99949,0.00003165831,0.0001548811,0.00002235086,0.000001249036,0.00000216337,0.00002577804,0.000002153297,0.0002697046],"genre_scores_gemma":[0.998329,0.00004263649,0.0005827167,0.00003862874,0.000002672255,0.000005127293,0.00009504867,0.000007759033,0.0008965033],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00556923,"threshold_uncertainty_score":0.01107359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02671259907430716,"score_gpt":0.3029965956126296,"score_spread":0.2762839965383224,"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."}}