{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000414662,0.00008554714,0.00008056097,0.0000355025,0.0001913464,0.00002185174,0.0002745674,0.00006320004,0.0000163008],"category_scores_gemma":[0.000139357,0.00007981378,0.00003001954,0.0002031568,0.0004090109,0.000005036433,0.00005070729,0.00004560439,0.00001133973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001812226,"about_ca_system_score_gemma":0.0001703407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004057684,"about_ca_topic_score_gemma":0.0004865887,"domain_scores_codex":[0.9990547,0.00003090576,0.0001406485,0.0003827406,0.0001166268,0.0002743269],"domain_scores_gemma":[0.9996295,0.00001171989,0.00003555342,0.0001523171,0.0001065196,0.0000643754],"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":[0.00004583183,0.00003010386,0.01095389,0.000006921354,0.000002725723,1.304952e-8,0.0002794144,0.0001605226,0.9807829,0.00325003,0.0003274727,0.004160136],"study_design_scores_gemma":[0.0003133451,0.0003238682,0.6134334,0.000003819855,0.000003310922,0.000003724539,0.00007307089,0.00007243561,0.3807075,0.0013629,0.003575116,0.0001275458],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796917,0.00007911419,0.01777901,0.00007257957,0.0002269548,0.0001991976,0.000007565074,0.000007826934,0.001936047],"genre_scores_gemma":[0.9795426,0.000002805204,0.01931218,0.0001514023,0.0002668619,0.00001648812,0.000005157436,0.000007109123,0.0006954238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6024795,"threshold_uncertainty_score":0.3254711,"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."}}