{"id":"W2973837589","doi":"10.3168/jds.2019-16820","title":"Inclusion of herdmate data improves genomic prediction for milk-production and feed-efficiency traits within North American dairy herds","year":2019,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agricultural Research Service; National Institute of Food and Agriculture; U.S. Department of Agriculture","keywords":"Herd; Genomic selection; Biotechnology; Population; Selection (genetic algorithm); Best linear unbiased prediction; Dairy cattle; Biology; Statistics; Computer science; Mathematics; Animal science; Machine learning; Genetics; Medicine; Genotype","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005416301,0.0003928486,0.0004852886,0.0005373496,0.0005726183,0.0006731581,0.0005056409,0.0004434526,0.0003469159],"category_scores_gemma":[0.007263562,0.0002233146,0.0004806835,0.0006244929,0.0003771037,0.0005296011,0.0005453897,0.0004214816,0.0001018007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001065226,"about_ca_system_score_gemma":0.001145708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1589742,"about_ca_topic_score_gemma":0.2874947,"domain_scores_codex":[0.9984151,0.0008564776,0.00005453058,0.0004298421,0.0001598521,0.00008425556],"domain_scores_gemma":[0.9965778,0.002111908,0.0003110931,0.0003883932,0.0004786863,0.000132086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003690495,0.0002027291,0.8964313,0.0000286964,0.0003499439,0.00007900422,0.0002782973,0.06382493,0.00228582,0.0001041287,0.0005659522,0.0354802],"study_design_scores_gemma":[0.00002272591,0.0002405836,0.8032547,0.00002150313,0.0001714095,0.00005099174,0.0003067217,0.1932097,0.001672684,0.000217031,0.0008044711,0.00002753225],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978209,0.0000593875,0.001506544,0.00004208647,0.000002054853,0.000008047862,0.0002871503,0.00004055314,0.000233334],"genre_scores_gemma":[0.9947671,0.00004807067,0.003704173,0.0000351358,0.000002696442,0.000007938272,0.001235842,0.000005620044,0.0001933713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1589742,"threshold_uncertainty_score":0.3160977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01238526104718169,"score_gpt":0.2525062270192354,"score_spread":0.2401209659720537,"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."}}