{"id":"W4399756553","doi":"10.1007/s11295-024-01653-x","title":"A meta-analysis on the effects of marker coverage, status number, and size of training set on predictive accuracy and heritability estimates from genomic selection in tree breeding","year":2024,"lang":"en","type":"article","venue":"Tree Genetics & Genomes","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Université Laval","funders":"","keywords":"Biology; Heritability; Selection (genetic algorithm); Genomic selection; Tree (set theory); Set (abstract data type); Evolutionary biology; Marker-assisted selection; Computational biology; Statistics; Machine learning; Genetics; Genetic marker; Computer science; Mathematics; Gene","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":[0.0002441442,0.0002395087,0.0004997841,0.00005037593,0.00004646781,0.00003085022,0.0001220375,0.0001218959,0.00004598501],"category_scores_gemma":[0.0001890395,0.0001794822,0.0002013029,0.000215723,0.0001648316,0.000004252723,0.00008388655,0.000131017,6.41505e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001768023,"about_ca_system_score_gemma":0.00006769397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008697938,"about_ca_topic_score_gemma":0.0001935303,"domain_scores_codex":[0.9986288,0.0001581963,0.0003376715,0.0004966095,0.0001536455,0.0002250706],"domain_scores_gemma":[0.9982847,0.001250081,0.0001131894,0.0002482412,0.00004000096,0.00006380393],"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":[0.001261224,0.000364661,0.2243738,0.0006187877,0.06695228,0.000004451602,0.01304395,0.01466576,0.6164201,0.0008703526,0.0001267302,0.06129785],"study_design_scores_gemma":[0.000487216,0.001154974,0.9491366,0.00002294689,0.01103544,0.000002497079,0.0003514929,0.00120976,0.0314667,0.004788251,0.0001221183,0.0002219882],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9811795,0.01708905,0.0007319225,0.00004561746,0.00005256579,0.0003695855,0.0002174194,0.000008264038,0.0003061124],"genre_scores_gemma":[0.9942653,0.001041295,0.0044638,0.00004041423,0.00004640898,0.00004044123,0.00002947265,0.00002302265,0.00004987232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7247628,"threshold_uncertainty_score":0.731907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02645912490280571,"score_gpt":0.2719783425458254,"score_spread":0.2455192176430196,"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."}}