{"id":"W1999363884","doi":"10.1186/1471-2164-15-1048","title":"Genomic selection accuracies within and between environments and small breeding groups in white spruce","year":2014,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":122,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts; Parks Canada; Canadian Forest Service; Université Laval; Natural Resources Canada","funders":"Natural Resources Canada; Génome Québec; Embrapa Recursos Genéticos e Biotecnologia; Genome Canada; McGill University","keywords":"Pedigree chart; Biology; Selection (genetic algorithm); Regression; Predictive modelling; Phenotypic trait; White (mutation); Evolutionary biology; Genetics; Heritability; Phenotype; Statistics; Gene; Machine learning; Mathematics; Computer science","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":[0.0001977027,0.000137917,0.000128973,0.00002977215,0.00006633945,0.00002800623,0.00009100698,0.0001294383,0.000002160864],"category_scores_gemma":[0.00002665769,0.0001481086,0.00001897981,0.00002816238,0.00007497907,0.000003298563,0.0001269882,0.00008474952,0.000002747988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001747617,"about_ca_system_score_gemma":0.00002059435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001231773,"about_ca_topic_score_gemma":0.0001824871,"domain_scores_codex":[0.9992285,0.00004791671,0.0001844037,0.0003188944,0.00004134387,0.0001789889],"domain_scores_gemma":[0.9997081,0.00001916976,0.00007243191,0.0001261076,0.000005518179,0.00006867301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003471782,0.00001273182,0.7814684,0.0000236725,0.00001626828,4.826294e-8,0.0004100338,0.001396155,0.2134131,0.0004272911,0.00001277676,0.002784849],"study_design_scores_gemma":[0.0005458581,0.0002466635,0.9854152,0.000006585342,0.00002104195,0.00001049211,0.0001389646,0.0002310752,0.0101206,0.001265493,0.001790629,0.0002073837],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9804639,0.0003283921,0.01879626,0.00001842706,0.00006341117,0.0001425615,0.000006510252,0.000004940576,0.0001756421],"genre_scores_gemma":[0.9631572,0.00006937089,0.03624829,0.00005530325,0.0002291557,0.000006835994,0.00002144034,0.00001970606,0.0001926383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2039468,"threshold_uncertainty_score":0.6039693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01652674287583474,"score_gpt":0.2126837900893813,"score_spread":0.1961570472135465,"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."}}