{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003110874,0.0004207408,0.0002430676,0.0009237917,0.0002290926,0.0004783483,0.0002547535,0.0002863226,0.0006516532],"category_scores_gemma":[0.004318323,0.0001026133,0.0004769972,0.0004556185,0.000232219,0.0003022216,0.0003741489,0.0002202403,0.0001908483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004260042,"about_ca_system_score_gemma":0.0003207166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01094624,"about_ca_topic_score_gemma":0.01082011,"domain_scores_codex":[0.9993021,0.0003094331,0.00004106716,0.0001778507,0.0001102109,0.00005925277],"domain_scores_gemma":[0.9979092,0.001380624,0.0001792251,0.000157037,0.0003085233,0.00006521756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006151149,0.00004735046,0.7808278,0.00008920002,0.000429749,0.0001674478,0.0003485609,0.0353501,0.01000021,0.0007552744,0.0006656685,0.1707036],"study_design_scores_gemma":[0.00003494554,0.0001624349,0.7795774,0.00004152138,0.0003761743,0.0001788361,0.0002263343,0.2107583,0.0063469,0.001167598,0.001087898,0.00004175634],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878737,0.0004222062,0.01027183,0.00005063463,0.000005646021,0.000004300603,0.0003130439,0.00009211235,0.0009665207],"genre_scores_gemma":[0.9962409,0.000102921,0.002730836,0.00001304531,0.000003966346,0.000004434072,0.0006432333,0.000008976159,0.0002517362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01094624,"threshold_uncertainty_score":0.02176505,"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."}}