{"id":"W4323569591","doi":"10.3390/f14030520","title":"Prediction of Genetic Gains from Selection in Tree Breeding","year":2023,"lang":"en","type":"article","venue":"Forests","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"South China Agricultural University","keywords":"Selection (genetic algorithm); Biology; Trait; Genetic gain; Heritability; Natural selection; Population; Quantitative trait locus; Genomic selection; Evolutionary biology; Genetics; Genetic variation; Machine learning; Gene; Computer science; Single-nucleotide polymorphism","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004268283,0.0005181985,0.0006752528,0.001104487,0.0002815368,0.0007322674,0.0004328361,0.0004810544,0.0007327216],"category_scores_gemma":[0.006055332,0.0001752833,0.0006466343,0.001332623,0.000452837,0.0007320927,0.0005521065,0.0006992519,0.0001988656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005161478,"about_ca_system_score_gemma":0.0003662881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001893238,"about_ca_topic_score_gemma":0.003201665,"domain_scores_codex":[0.9987707,0.0007192334,0.00004449516,0.000209258,0.0001948204,0.00006147117],"domain_scores_gemma":[0.9956245,0.003462422,0.0003608195,0.0002246492,0.0002363547,0.00009130299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009652432,0.0001951309,0.4656799,0.0003632306,0.001151928,0.0007226928,0.000320823,0.2572655,0.05748298,0.009356471,0.0008562532,0.2056399],"study_design_scores_gemma":[0.0000487034,0.0003434995,0.3885703,0.00008487036,0.0004047707,0.0003263858,0.0001000761,0.5720392,0.0107784,0.02443331,0.002801131,0.00006934128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8151437,0.002522945,0.1791701,0.0002588965,0.00003531351,0.00003720996,0.0007948784,0.0002953033,0.001741582],"genre_scores_gemma":[0.9640477,0.0006101748,0.03417492,0.0000758328,0.00002501523,0.00002446101,0.0006346391,0.0000405956,0.0003666813],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004268283,"threshold_uncertainty_score":0.02257311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02413841446597903,"score_gpt":0.2486522621350936,"score_spread":0.2245138476691145,"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."}}