{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005523122,0.00006961136,0.00007374882,0.00006056576,0.00002067219,0.000003744166,0.00008220652,0.00009880751,0.00001396673],"category_scores_gemma":[0.00003175014,0.00007256321,0.00003267596,0.000167895,0.00002851482,0.000001606698,0.00003565418,0.00004463986,0.00001092328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000708973,"about_ca_system_score_gemma":0.00002798862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008289904,"about_ca_topic_score_gemma":0.001239146,"domain_scores_codex":[0.9994255,0.00002073248,0.0001563886,0.0001810713,0.00008038832,0.0001358709],"domain_scores_gemma":[0.9998028,0.000009560761,0.00004038601,0.0000960737,0.00002184576,0.00002932281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004167432,0.00003228024,0.7832476,0.00001091636,0.0000209576,4.366013e-7,0.0001471295,0.006886892,0.2017522,0.0002035325,0.001918379,0.00573798],"study_design_scores_gemma":[0.000325023,0.0002697676,0.9806266,0.00001381889,0.000007036535,0.000001433667,0.00003086917,0.0007373292,0.01495732,0.002437845,0.0005359184,0.00005704966],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951732,0.0001066647,0.00384588,0.00001678892,0.000151042,0.00009802709,0.00003699022,0.00001802495,0.0005534369],"genre_scores_gemma":[0.9963848,0.00002284189,0.002955121,0.00001145872,0.000226268,0.00001424416,0.0001146522,0.00001189932,0.0002587409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1973789,"threshold_uncertainty_score":0.2959042,"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."}}