{"id":"W2972465811","doi":"10.1111/age.12853","title":"Accuracies of genomic prediction for twenty economically important traits in Chinese Simmental beef cattle","year":2019,"lang":"en","type":"article","venue":"Animal Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Beef cattle; Biology; Genomic selection; Best linear unbiased prediction; Biotechnology; Animal science; Population; Regression; Statistics; Selection (genetic algorithm); Genetics; Mathematics; Single-nucleotide polymorphism; Genotype; Demography; 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":[0.00009856275,0.0001520641,0.0001841014,0.00003510189,0.00002139107,0.000008611666,0.0001827157,0.0001347038,0.00004623032],"category_scores_gemma":[0.00001395246,0.0001495205,0.00009412538,0.00003624547,0.00005674733,0.000003167894,0.00007380957,0.00005435345,0.000007166438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001547119,"about_ca_system_score_gemma":0.0000786252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007698833,"about_ca_topic_score_gemma":0.00004197239,"domain_scores_codex":[0.9990053,0.00001726922,0.0003873243,0.0003080805,0.0000637604,0.0002182376],"domain_scores_gemma":[0.9995815,0.00001702194,0.0001244017,0.0001865549,0.00003729704,0.00005319305],"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":[0.0002799109,0.000114049,0.2135248,0.00003637829,0.00004223947,7.41745e-8,0.000163382,0.001668701,0.782747,0.0003150971,0.0001875074,0.000920885],"study_design_scores_gemma":[0.001022139,0.001518166,0.9522576,0.000005563312,0.00001731085,0.000004471215,0.0001017236,0.0002886897,0.04228097,0.0005406147,0.001798787,0.0001640104],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975193,0.0007502539,0.0004121362,0.00002796841,0.0001797791,0.0004953427,0.0002761488,0.000005124296,0.0003339086],"genre_scores_gemma":[0.99146,0.00007649845,0.007882351,0.00006846793,0.0001561651,0.0000228434,0.0001664883,0.00002414039,0.000143043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.740466,"threshold_uncertainty_score":0.6097268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008494082064640092,"score_gpt":0.2417413895367873,"score_spread":0.2332473074721473,"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."}}