{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006893366,0.0003456842,0.0002288865,0.0005100758,0.0001605265,0.0003832987,0.0003239469,0.0001638906,0.0008817741],"category_scores_gemma":[0.0005740646,0.0001468471,0.0001929236,0.0002537421,0.0002351069,0.0001073254,0.0002889919,0.0002591039,0.0001526275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002449427,"about_ca_system_score_gemma":0.0001227906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002225516,"about_ca_topic_score_gemma":0.004441984,"domain_scores_codex":[0.999662,0.0001275333,0.0000134311,0.0001209953,0.00004881003,0.00002709234],"domain_scores_gemma":[0.9994424,0.0002326436,0.0001114361,0.0001020154,0.00005266138,0.00005885287],"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":[0.0004496795,0.0002975189,0.3976254,0.00007900438,0.0004090657,0.0007894959,0.0006047458,0.00546562,0.5620456,0.001232729,0.0002584697,0.03074265],"study_design_scores_gemma":[0.00001268652,0.0001599314,0.9779146,0.000007289145,0.00009027908,0.0003697952,0.0001130318,0.01326344,0.007386058,0.0001714827,0.0004960659,0.00001540633],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984971,0.00002920477,0.001214272,0.000003628634,6.983233e-7,0.000002348412,0.0000545256,0.00001962609,0.0001787087],"genre_scores_gemma":[0.9988574,0.00001855655,0.00074908,0.00001232657,9.769728e-7,0.000004044368,0.0001422948,0.0000125244,0.000202765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002225516,"threshold_uncertainty_score":0.004425108,"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."}}