{"id":"W2114143990","doi":"10.1071/an11117","title":"Integration of genomic information into beef cattle and sheep genetic evaluations in Australia","year":2011,"lang":"en","type":"article","venue":"Animal Production Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Genetics","funders":"","keywords":"Genomic selection; Beef cattle; Selection (genetic algorithm); Genomic information; Biology; SNP; Biotechnology; Genetic gain; Single-nucleotide polymorphism; Best linear unbiased prediction; Genetic variation; Genetics; Computational biology; Genome; Computer science; Genotype; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003228695,0.0000587902,0.00005129175,0.0000956226,0.00006102777,0.0000114102,0.0001153334,0.00003720967,0.00002025062],"category_scores_gemma":[0.0001237133,0.0000568248,0.00001104532,0.0001782174,0.0003193977,0.00003573946,0.00004642133,0.00003961668,0.000009195786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001287374,"about_ca_system_score_gemma":0.00008360258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001800509,"about_ca_topic_score_gemma":0.00006963541,"domain_scores_codex":[0.9993775,0.00001997213,0.0001895913,0.0001931619,0.0001168274,0.0001028967],"domain_scores_gemma":[0.9995993,0.000001803664,0.00007196397,0.000154708,0.0001395972,0.00003257698],"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":[0.00003276665,0.00002550583,0.005369297,0.000008302733,0.000002048588,1.201871e-8,0.002023153,0.0002272328,0.983027,0.001268244,0.00004349647,0.00797295],"study_design_scores_gemma":[0.00007463521,0.0002371063,0.8017815,0.000003894538,0.000003746583,0.000005357018,0.0002096447,0.00003513408,0.1960554,0.001465465,0.00007281418,0.00005524681],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959764,0.00006223874,0.003291875,0.00004216492,0.000139789,0.0001627848,0.000001372981,0.000003185755,0.0003202266],"genre_scores_gemma":[0.9649994,0.00001189103,0.03485866,0.00001704987,0.00004039655,0.00001029924,0.000005442726,0.000002239524,0.00005466858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7964122,"threshold_uncertainty_score":0.2317248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03941006396354005,"score_gpt":0.3034317283596384,"score_spread":0.2640216643960984,"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."}}