{"id":"W2001513654","doi":"10.1186/1471-2164-14-368","title":"The genomic architecture and association genetics of adaptive characters using a candidate SNP approach in boreal black spruce","year":2013,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts (Québec); Natural Resources Canada; Canadian Forest Service; Université Laval","funders":"Institut National de la Recherche Agronomique; Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke; Genome Canada; Canada Research Chairs; Fonds Québécois de la Recherche sur la Nature et les Technologies; McGill University; Natural Resources Canada; Université Laval","keywords":"Biology; SNP; Genetic architecture; Genetics; Evolutionary biology; Genome-wide association study; Computational biology; Single-nucleotide polymorphism; Genotype; Quantitative trait locus; Gene","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":[0.0006102514,0.0003017399,0.0002139734,0.0008010223,0.0002933477,0.0003102687,0.000311863,0.0002514985,0.000577568],"category_scores_gemma":[0.0003932622,0.000125461,0.0004896331,0.000491355,0.0003092041,0.00009955196,0.0002776032,0.0002937703,0.00006420311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001886287,"about_ca_system_score_gemma":0.0002397358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002993273,"about_ca_topic_score_gemma":0.006384862,"domain_scores_codex":[0.9997615,0.00004284651,0.00001248563,0.0001254845,0.00003301853,0.00002461043],"domain_scores_gemma":[0.9995345,0.0001966793,0.0001380226,0.00002980354,0.00003681892,0.00006426198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001111317,0.0002415279,0.4987341,0.0001565212,0.0006345494,0.001385279,0.0007015367,0.005250482,0.463497,0.000455306,0.0001351432,0.02769734],"study_design_scores_gemma":[0.00003233255,0.00028506,0.988634,0.00001035496,0.0001669086,0.000666229,0.0001159807,0.005127527,0.004241986,0.0002931151,0.0004115546,0.00001494939],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996696,0.0001630635,0.002826299,0.00001555174,0.000002061781,0.000008025576,0.0001516719,0.00001686913,0.0001204587],"genre_scores_gemma":[0.9948448,0.00007881495,0.004541621,0.00001744643,0.000004538516,0.00001197523,0.0003016717,0.000006563939,0.0001925824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002993273,"threshold_uncertainty_score":0.005951703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0166265620356201,"score_gpt":0.2170943378897653,"score_spread":0.2004677758541452,"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."}}