{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01162636,0.0006074246,0.0008164354,0.0008869492,0.0004844834,0.001251595,0.0005875629,0.0005610221,0.002058369],"category_scores_gemma":[0.02074234,0.0003839688,0.0009334307,0.001303233,0.0008620581,0.0008123326,0.001690339,0.0009506452,0.0004600466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002658336,"about_ca_system_score_gemma":0.00024101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002787143,"about_ca_topic_score_gemma":0.004288528,"domain_scores_codex":[0.9925149,0.004768953,0.00030723,0.001804927,0.0003776378,0.000226233],"domain_scores_gemma":[0.9732639,0.0169345,0.002582458,0.006111073,0.0007957832,0.0003123433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007558849,0.00009136142,0.9049498,0.0001827003,0.003904934,0.000169435,0.0004826472,0.01750408,0.01261168,0.0006739271,0.0009994975,0.05767401],"study_design_scores_gemma":[0.00004839916,0.0003476368,0.9472556,0.00007862531,0.001302256,0.0004044285,0.0002418707,0.04016611,0.00478184,0.003051982,0.002270187,0.00005109119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9663073,0.0006113712,0.02988403,0.0002281148,0.00003535742,0.00001595052,0.001099702,0.0002175077,0.001600561],"genre_scores_gemma":[0.9923855,0.00007764513,0.006525985,0.00008061514,0.0000162131,0.00001343749,0.000621536,0.00003662477,0.0002423417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01162636,"threshold_uncertainty_score":0.06148684,"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."}}