{"id":"W2794076441","doi":"10.1111/jbg.12317","title":"Comparing deregression methods for genomic prediction of test‐day traits in dairy cattle","year":2018,"lang":"en","type":"article","venue":"Journal of Animal Breeding and Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Animal Health Institute; University of Guelph","funders":"Agriculture and Agri-Food Canada; Canadian Dairy Commission; Dairy Farmers of Canada","keywords":"Best linear unbiased prediction; Biology; Genomic selection; Population; Statistics; Linear regression; Dairy cattle; Animal science; Genetics; Selection (genetic algorithm); Mathematics; Single-nucleotide polymorphism; Demography; Computer science; Machine learning; Genotype","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":[0.0005033361,0.00009883913,0.0001945935,0.00007042322,0.00004952833,0.00001053348,0.0001203956,0.0001117116,0.00000246623],"category_scores_gemma":[0.000140472,0.00008760163,0.00006970306,0.00005174606,0.0001123892,0.000003835063,0.00004972909,0.00008434844,1.349276e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008493549,"about_ca_system_score_gemma":0.00005694184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001796618,"about_ca_topic_score_gemma":0.000003974797,"domain_scores_codex":[0.9992259,0.00004254056,0.0003812606,0.0001356741,0.00007196022,0.0001426842],"domain_scores_gemma":[0.9994032,0.00006649397,0.0002300197,0.00006733988,0.0001654059,0.00006752356],"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":[0.0002321222,0.00005769368,0.0385373,0.00003526114,0.00003342225,1.309268e-7,0.0003551949,0.0001452084,0.9446497,0.00005591829,0.0003072444,0.0155908],"study_design_scores_gemma":[0.001116425,0.006811252,0.7867007,0.00009943746,0.00006343799,0.00008031534,0.0002028035,0.0008428097,0.2018374,0.0006322703,0.001505121,0.0001080713],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9520553,0.002688506,0.04479853,0.00003281292,0.0001959714,0.00008396682,0.00001033543,0.000001605576,0.0001330066],"genre_scores_gemma":[0.825361,0.0001036223,0.1739708,0.0000145622,0.0005155524,0.000001127076,0.00000213609,0.00001071642,0.00002042079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7481633,"threshold_uncertainty_score":0.3572291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04276396934404398,"score_gpt":0.3224355303282628,"score_spread":0.2796715609842189,"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."}}