{"id":"W4294919625","doi":"10.1186/s12711-022-00749-z","title":"Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency","year":2022,"lang":"en","type":"article","venue":"Genetics Selection Evolution","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Guelph; Ste. Anne's Hospital","funders":"Ontario Ministry of Research and Innovation; Wageningen University and Research; Agricultural Research Service; Genome Alberta; Scotland’s Rural College; Ministry of Agriculture, Food and Rural Affairs; Agriculture Victoria; Dairy Australia; Gardiner Foundation; Ontario Ministry of Agriculture, Food and Rural Affairs; Genome Canada; Ontario Genomics; Aarhus Universitet; U.S. Department of Agriculture","keywords":"Biology; Genomic selection; Phenotype; Selection (genetic algorithm); Genetics; Computational biology; Genomics; Evolutionary biology; Biotechnology; Genome; Genotype; Machine learning; Gene; Computer science; Single-nucleotide polymorphism","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002690165,0.0001403299,0.0001661632,0.00009230918,0.0001939621,0.000004657797,0.0003298857,0.00008419037,0.0001242899],"category_scores_gemma":[0.00008926155,0.0001216022,0.00008037268,0.0003123279,0.0001300611,0.000002851691,0.00019066,0.0001133613,7.244798e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005612016,"about_ca_system_score_gemma":0.0003038811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003260472,"about_ca_topic_score_gemma":0.00008422048,"domain_scores_codex":[0.9986072,0.0001601383,0.0004726851,0.0003034232,0.0002606875,0.0001958533],"domain_scores_gemma":[0.9989727,0.00003053632,0.0004121061,0.0003619121,0.0001815699,0.00004113928],"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":[0.0003455638,0.0002109509,0.02234078,0.00004835221,0.00008879753,5.974466e-8,0.000336074,0.1166522,0.8579073,0.0005343805,0.0001624543,0.001373079],"study_design_scores_gemma":[0.0009754704,0.002267688,0.8764203,0.00001316827,0.0001624572,0.00004195175,0.0003812586,0.006351585,0.111186,0.001482867,0.0005290207,0.0001882776],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9615841,0.001008381,0.03636431,0.00001755074,0.0002841815,0.0003793915,0.0001450377,0.000009262672,0.0002077813],"genre_scores_gemma":[0.9952235,0.00006038201,0.004199453,0.00001716077,0.0001258435,0.00003865646,0.00004809875,0.00002329094,0.0002636616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8540795,"threshold_uncertainty_score":0.4958793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01078275315287042,"score_gpt":0.2222614350808922,"score_spread":0.2114786819280218,"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."}}