{"id":"W2041607486","doi":"10.2527/jas.2009-2606","title":"The efficiency of genome-wide selection for genetic improvement of net merit1","year":2011,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Selection (genetic algorithm); Trait; Biology; Best linear unbiased prediction; Statistics; Genomic selection; Genetic gain; Index (typography); Genetics; Quantitative trait locus; Genotype; Genetic variation; Single-nucleotide polymorphism; Mathematics; Computer science; Gene; Machine learning","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":[0.0005116262,0.00006037598,0.00009469566,0.00003387377,0.00009999831,0.000007140888,0.0004009448,0.00003015251,0.000006170455],"category_scores_gemma":[0.000118444,0.00003979187,0.00007206937,0.0001320064,0.0003641426,0.000004845859,0.00004610201,0.00004064872,1.664133e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008798092,"about_ca_system_score_gemma":0.000228634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008525983,"about_ca_topic_score_gemma":0.000004040059,"domain_scores_codex":[0.9991933,0.0000119437,0.0003266772,0.000108352,0.000196989,0.000162701],"domain_scores_gemma":[0.9991103,0.00001753481,0.0003628111,0.0001034605,0.0003511391,0.00005479527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002121629,0.00005855655,0.002648581,0.00000856628,0.00001221284,3.741441e-8,0.0001293419,0.00008947896,0.9944944,0.0003685627,0.00003403617,0.001944069],"study_design_scores_gemma":[0.0002097685,0.00711146,0.2608504,0.000005781481,0.00001750548,0.00001098903,0.0001090327,0.0000367194,0.7304596,0.0006305047,0.0005093667,0.00004887003],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9752824,0.0004550324,0.02373005,0.00002043607,0.0001233459,0.0001240381,0.00000241247,6.078317e-7,0.0002617364],"genre_scores_gemma":[0.967625,0.00004538116,0.03220559,0.00001956486,0.00007274171,0.000002053638,1.454777e-7,0.000003699623,0.00002577064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2640348,"threshold_uncertainty_score":0.1622666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01365171539939604,"score_gpt":0.2422712811725613,"score_spread":0.2286195657731653,"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."}}