{"id":"W4289523030","doi":"10.1111/eva.13463","title":"Genomic selection reveals hidden relatedness and increased breeding efficiency in western redcedar polycross breeding","year":2022,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Forests; FPInnovations; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Genome British Columbia; Genome Canada","keywords":"Biology; Genetic gain; Heritability; Pedigree chart; Tree breeding; Selection (genetic algorithm); Genetic diversity; Genetic correlation; Trait; Progeny testing; Dominance (genetics); Evolutionary biology; Genetics; Genetic variation; Ecology; Demography; Gene; Woody plant; Machine learning; Population","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":[0.000205684,0.0001191643,0.0001012432,0.00007189632,0.000516298,0.00001424261,0.0001916457,0.00007986653,0.00003133046],"category_scores_gemma":[0.00001502578,0.0001476535,0.00003203108,0.0002846523,0.00008799901,0.000006507647,0.0002184303,0.0001759274,0.000006018616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008515159,"about_ca_system_score_gemma":0.00009416322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008033233,"about_ca_topic_score_gemma":0.00001325734,"domain_scores_codex":[0.9989473,0.00007003814,0.0002355206,0.0004066868,0.0001246129,0.0002158985],"domain_scores_gemma":[0.9996023,0.0000213567,0.00008267952,0.0001921169,0.00003484964,0.00006674983],"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":[0.0001240979,0.0002920432,0.375105,0.00002872806,0.00003711302,6.006858e-7,0.0003327458,0.004361175,0.6035997,0.0121426,0.001465332,0.002510886],"study_design_scores_gemma":[0.000652159,0.0003052152,0.9802391,0.000008716367,0.0000215176,0.000154352,0.0002893444,0.0003236904,0.0005971382,0.003880059,0.01324227,0.0002864737],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989636,0.001587713,0.007301127,0.0001863966,0.00007126668,0.0004732323,0.00006312491,0.00002845104,0.0006527061],"genre_scores_gemma":[0.9935185,0.00003585622,0.004828292,0.00008893952,0.0001746972,0.0004988007,0.0001657115,0.0000194539,0.0006697546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6051341,"threshold_uncertainty_score":0.6021133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008891617538831902,"score_gpt":0.2350567981410872,"score_spread":0.2261651806022553,"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."}}