{"id":"W2908556754","doi":"10.1038/s41437-018-0172-0","title":"Genomic selection of juvenile height across a single-generational gap in Douglas-fir","year":2019,"lang":"en","type":"article","venue":"Heredity","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Forests; Université Laval; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Forests, Lands and Natural Resource Operations; Genome British Columbia","keywords":"Douglas fir; Biology; Selection (genetic algorithm); Statistics; Regression; Best linear unbiased prediction; Genomic selection; Population; Bayes' theorem; Mathematics; Animal science; Computer science; Machine learning; Demography; Genetics; Bayesian probability; Botany; Genotype; Single-nucleotide polymorphism","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.00109461,0.0003426596,0.0002202438,0.0004530915,0.0003818565,0.0003299922,0.00024618,0.0001803974,0.0003747436],"category_scores_gemma":[0.001202843,0.000111201,0.0004277988,0.0003375214,0.0002361356,0.0001155449,0.0003812728,0.0003333767,0.00006468238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004942698,"about_ca_system_score_gemma":0.0004046354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02480898,"about_ca_topic_score_gemma":0.07516764,"domain_scores_codex":[0.9996941,0.0001216822,0.00001030158,0.0001117147,0.00003470976,0.00002764046],"domain_scores_gemma":[0.9993574,0.0003699114,0.00006533775,0.00007832368,0.00007152587,0.00005759474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000536993,0.0001357723,0.8002898,0.00007354426,0.0006054959,0.0005930582,0.001197741,0.03119328,0.1204101,0.000663803,0.0002272914,0.04407319],"study_design_scores_gemma":[0.000008941966,0.0001341668,0.9793199,0.0000059728,0.00008779218,0.0001715946,0.0002855801,0.01568971,0.003650083,0.0001783177,0.0004485596,0.00001949374],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998461,0.00002633607,0.001253014,0.000005086421,0.000001048181,0.000002258478,0.0001007855,0.00001782924,0.0001326396],"genre_scores_gemma":[0.99714,0.00002125602,0.002221476,0.00001192049,9.077225e-7,0.000004507388,0.000444254,0.00001067813,0.0001449446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02480898,"threshold_uncertainty_score":0.04932916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01668585666821469,"score_gpt":0.2477334621785554,"score_spread":0.2310476055103407,"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."}}