{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004381594,0.0002474269,0.0003547871,0.001172009,0.0003097402,0.0007472909,0.0003983163,0.0002704859,0.0009547863],"category_scores_gemma":[0.007311307,0.0002389881,0.0003180338,0.001363355,0.0004214815,0.0005310663,0.001146482,0.0003820695,0.0001646071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001525399,"about_ca_system_score_gemma":0.00173495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05582363,"about_ca_topic_score_gemma":0.0995657,"domain_scores_codex":[0.9977829,0.001179888,0.0001197033,0.0002803234,0.0005603994,0.0000769468],"domain_scores_gemma":[0.9972326,0.0009259679,0.0003206965,0.0002696266,0.001005773,0.000245316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007026035,0.0004376693,0.2898673,0.0002976875,0.0002278411,0.002010496,0.006103586,0.02016215,0.03334599,0.001893844,0.001559582,0.6433913],"study_design_scores_gemma":[0.0000664708,0.0008878245,0.9407327,0.0002247655,0.0001644773,0.0006613399,0.001697612,0.03620365,0.005404721,0.002358473,0.01151929,0.00007864155],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9713405,0.000611358,0.02154943,0.0004677303,0.00002031714,0.0001804474,0.0006262984,0.0001262356,0.0050778],"genre_scores_gemma":[0.917996,0.0006177788,0.07746089,0.0001317485,0.00001283226,0.00009348464,0.0009397797,0.00004143939,0.00270603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05582363,"threshold_uncertainty_score":0.1109974,"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."}}