{"id":"W4416824454","doi":"10.1101/2025.11.28.691168","title":"Optimizing marker density for maximizing the accuracy of genomic prediction and heritability estimates in three major North American and European spruce species","year":2025,"lang":"","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts; Natural Resources Canada; Université Laval","funders":"Natural Resources Canada; U.S. Forest Service; Canadian Forest Service; Ministère des Ressources Naturelles et de la Faune; Svenska Forskningsrådet Formas; Vetenskapsrådet; Stiftelsen för Strategisk Forskning; Genome Canada","keywords":"Heritability; Selection (genetic algorithm); Single-nucleotide polymorphism; Genetic gain; Tree breeding; Genotyping; Quantitative trait locus; Marker-assisted selection; Genetic marker","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002442413,0.0003644816,0.0003973305,0.0004772928,0.0002564711,0.0004272996,0.0004146864,0.000342964,0.0003368857],"category_scores_gemma":[0.002781114,0.0002061041,0.0003363246,0.0003994421,0.0002659152,0.0002969933,0.0005179911,0.0002809316,0.00008262233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002945233,"about_ca_system_score_gemma":0.0002304291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003620235,"about_ca_topic_score_gemma":0.006149502,"domain_scores_codex":[0.9994304,0.0002523912,0.00003153737,0.0001763557,0.00006657468,0.00004273155],"domain_scores_gemma":[0.9983771,0.001102902,0.000148827,0.0001483361,0.0001746554,0.00004825971],"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":[0.001619218,0.0003942084,0.3790738,0.0002152639,0.0006070953,0.0002900299,0.0006238284,0.1652634,0.365202,0.0009263041,0.0004096924,0.08537523],"study_design_scores_gemma":[0.00008668155,0.0006532512,0.5988885,0.00003939328,0.0004140383,0.0002213859,0.0003304521,0.3144807,0.08190977,0.001322721,0.001583599,0.00006958764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883416,0.0001263291,0.01117948,0.00001484682,0.000001598259,0.000007297319,0.0001152884,0.00006053132,0.0001529391],"genre_scores_gemma":[0.9849443,0.00004529237,0.01455359,0.00001123249,0.00000164109,0.00001647414,0.0002996975,0.00001097569,0.0001167763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003620235,"threshold_uncertainty_score":0.01291692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01159733909661391,"score_gpt":0.2110362685792183,"score_spread":0.1994389294826044,"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."}}