{"id":"W2168329225","doi":"10.1186/1297-9686-46-17","title":"Accuracy of genomic predictions in Bos indicus (Nellore) cattle","year":2014,"lang":"en","type":"article","venue":"Genetics Selection Evolution","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Agricultural Research Service; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo; U.S. Department of Agriculture","keywords":"Biology; Genomic selection; Beef cattle; Selection (genetic algorithm); Evolutionary biology; Biotechnology; Computational biology; Animal science; Genetics; Single-nucleotide polymorphism; Genotype; Gene; Computer science; Artificial intelligence","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":[0.003981408,0.0003945412,0.0002596881,0.0005260376,0.0002360293,0.0007340029,0.0004204039,0.0003807233,0.000685251],"category_scores_gemma":[0.006055668,0.0001449183,0.000219963,0.000308284,0.0002783174,0.0003159121,0.0005589462,0.0003367134,0.000325496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003348227,"about_ca_system_score_gemma":0.0002319738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005977893,"about_ca_topic_score_gemma":0.008203398,"domain_scores_codex":[0.9989257,0.0005291603,0.000048568,0.0002945302,0.0001303536,0.00007160873],"domain_scores_gemma":[0.9960061,0.002716408,0.0004858945,0.0003035838,0.0003936921,0.00009439288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001055665,0.0001151297,0.8445669,0.0001057275,0.0005149103,0.0001301692,0.0004019607,0.04582595,0.007638368,0.000376047,0.001267425,0.09800181],"study_design_scores_gemma":[0.0000402652,0.0002306115,0.7740979,0.00007299128,0.0001848372,0.000197301,0.0001957616,0.2186656,0.004437994,0.0006941095,0.001146193,0.0000366042],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889545,0.0003991131,0.008695788,0.00007525118,0.000007395882,0.000005552196,0.0006825314,0.0001713526,0.001008469],"genre_scores_gemma":[0.9935776,0.00007300605,0.004456034,0.00002767144,0.00000675831,0.000005663806,0.001509942,0.00001804042,0.0003252689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005977893,"threshold_uncertainty_score":0.021056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006546675496359383,"score_gpt":0.2245016521052963,"score_spread":0.2179549766089369,"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."}}