{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001310318,0.0001090244,0.0001156621,0.00002707167,0.00007134628,0.00003183462,0.0001303011,0.0001454752,0.000001782702],"category_scores_gemma":[0.00001711124,0.00009467491,0.00004101285,0.00005186286,0.00007002654,0.000002810565,0.0001082207,0.00008832897,0.000001478538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005315624,"about_ca_system_score_gemma":0.00008049417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002459381,"about_ca_topic_score_gemma":0.0002697382,"domain_scores_codex":[0.9992992,0.00006919506,0.0001779278,0.000191579,0.00008224731,0.0001798554],"domain_scores_gemma":[0.9995383,0.00001678725,0.000172252,0.0001724376,0.0000556432,0.00004458021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001943494,0.00003645963,0.6035738,0.00004834826,0.0001377077,4.225471e-7,0.001974551,0.03374529,0.354174,0.0001466297,0.0001746692,0.005793788],"study_design_scores_gemma":[0.0006683186,0.00006962859,0.9823399,0.000004673088,0.00003653464,0.000006183391,0.0007373788,0.006811806,0.007555821,0.0003358715,0.001225851,0.0002081013],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977475,0.000317409,0.001343073,0.00003359204,0.00005470231,0.0002788106,0.00003441498,0.000002003746,0.0001884959],"genre_scores_gemma":[0.9938474,0.0001776044,0.005699439,0.00005298182,0.0000742772,0.000003260161,0.00005195192,0.00001078542,0.00008233814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.378766,"threshold_uncertainty_score":0.3860731,"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."}}