{"id":"W2187912871","doi":"10.1111/nph.13762","title":"Genetic architecture of wood properties based on association analysis and co‐expression networks in white spruce","year":2015,"lang":"en","type":"article","venue":"New Phytologist","topic":"Forest ecology and management","field":"Environmental Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Genomics; Natural Resources Canada; Canadian Forest Service; Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Genome Canada","keywords":"Biology; Genetic architecture; Genetic association; Trait; Single-nucleotide polymorphism; Association mapping; Gene; Quantitative trait locus; Computational biology; Xylem; Population; Genetics; Genome-wide association study; Expression quantitative trait loci; Candidate gene; Evolutionary biology; White (mutation); Botany; Genotype; 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":[0.0002026498,0.00007516178,0.0001490142,0.00006557185,0.00002661097,0.000007680592,0.00009117786,0.00008315595,0.00009195527],"category_scores_gemma":[0.00006427998,0.00005788021,0.00002711075,0.0002618931,0.00008778089,0.00002990515,0.00005671236,0.00009471447,0.00001033432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001007447,"about_ca_system_score_gemma":0.000008480502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003232878,"about_ca_topic_score_gemma":0.003417184,"domain_scores_codex":[0.9993132,0.00008682024,0.0001281995,0.0001841102,0.0001437998,0.0001438904],"domain_scores_gemma":[0.9996775,0.00002829217,0.0001015498,0.0001382914,0.000003850051,0.00005056268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004554708,0.00003639564,0.6381725,0.000002557946,0.000009904139,0.000001864579,0.00007356227,0.3590883,0.00009003879,0.000005767509,0.001652201,0.0008213204],"study_design_scores_gemma":[0.0004531792,0.0001377919,0.9732094,0.000009308769,0.00004249382,1.776628e-7,0.00002112034,0.02459005,0.0005429449,0.0003653405,0.0005546637,0.00007350928],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831437,0.00006073041,0.01017057,0.0006632396,0.00005750277,0.0001991041,0.000001861563,0.00001739298,0.005685868],"genre_scores_gemma":[0.9981012,0.00000565981,0.001283935,0.0002273716,0.00001319426,0.000009295236,0.000004547942,0.000003196962,0.0003515714],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3350369,"threshold_uncertainty_score":0.2360286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01165755917030892,"score_gpt":0.21713291904438,"score_spread":0.2054753598740711,"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."}}