{"id":"W3033568738","doi":"10.1371/journal.pone.0232201","title":"Linkage disequilibrium vs. pedigree: Genomic selection prediction accuracy in conifer species","year":2020,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Forests; Université Laval; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Forests, Lands and Natural Resource Operations; Genome British Columbia","keywords":"Linkage disequilibrium; Douglas fir; Biology; Picea engelmannii; Population; Best linear unbiased prediction; Selection (genetic algorithm); Association mapping; Tree breeding; Evolutionary biology; Statistics; Botany; Genetics; Mathematics; Allele; Woody plant; Demography; Single-nucleotide polymorphism; Haplotype; Machine learning; Pinus contorta","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.004083177,0.00030024,0.0002829989,0.001035937,0.0003532708,0.0008072682,0.0003245293,0.0003160182,0.001005744],"category_scores_gemma":[0.007476706,0.00011213,0.0002523693,0.0008490778,0.0003912344,0.000526053,0.0003742215,0.0004367945,0.0002498416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004614998,"about_ca_system_score_gemma":0.000272788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007951983,"about_ca_topic_score_gemma":0.01568758,"domain_scores_codex":[0.9991731,0.0003570915,0.000043313,0.0002615269,0.00009859136,0.00006627278],"domain_scores_gemma":[0.9920953,0.006373104,0.0005672047,0.000354683,0.0004191509,0.0001905767],"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":[0.0002972262,0.00004681006,0.9366753,0.00004421537,0.0002154313,0.0000720513,0.0002076462,0.02609766,0.003175713,0.0001902402,0.0003525217,0.03262515],"study_design_scores_gemma":[0.00002956263,0.000193284,0.8418336,0.0000416617,0.0001483984,0.0002473739,0.0002229043,0.1529227,0.002467416,0.001146752,0.0007066374,0.00003985667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937472,0.0003357714,0.004718714,0.0000519038,0.0000051992,0.000005698258,0.0004261328,0.00009719793,0.0006121384],"genre_scores_gemma":[0.997663,0.00004086664,0.001771142,0.00001186618,0.0000032019,0.000003243188,0.000371105,0.00001131595,0.0001241597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007951983,"threshold_uncertainty_score":0.02159411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03390709071563931,"score_gpt":0.2146151647861413,"score_spread":0.1807080740705019,"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."}}