{"id":"W4361228097","doi":"10.1101/2023.03.28.534607","title":"<i>k</i> -mer-based GWAS enhances the discovery of causal variants and candidate genes in soybean","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Soybean genetics and cultivation","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Alliance de recherche numérique du Canada; Canadian Field Crop Research Alliance; Genome Canada; Syngenta Canada; Saskatchewan Pulse Growers; Fonds de recherche du Québec – Nature et technologies; Grain Farmers of Ontario; Génome Québec","keywords":"Genome-wide association study; Single-nucleotide polymorphism; Biology; Genetics; Candidate gene; Genetic association; Locus (genetics); Population; Indel; Computational biology; Gene; Genotype; Medicine","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":[0.0004803665,0.0002685259,0.0003119583,0.00003642652,0.0001368052,0.0001642809,0.0003714769,0.0002154378,0.00001368573],"category_scores_gemma":[0.00008020804,0.0001150358,0.00007147055,0.0004524463,0.0001461128,0.00007812598,0.0002514115,0.0002399385,0.000005388959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003271809,"about_ca_system_score_gemma":0.00008578979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008712167,"about_ca_topic_score_gemma":0.0003761879,"domain_scores_codex":[0.9983985,0.0001256877,0.0003746146,0.0005444238,0.0002583036,0.0002984317],"domain_scores_gemma":[0.9991488,0.0001703048,0.0002870319,0.0002116877,0.0001157223,0.00006649463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001690417,0.00004901021,0.03363191,0.00005229796,0.00002433272,0.000005787037,0.000008805925,0.00005022096,0.9659947,0.00009620225,0.00005577263,0.00001401645],"study_design_scores_gemma":[0.0001208165,0.00004634624,0.6856694,0.0001542098,0.00002275643,2.766872e-9,0.00001720585,0.0002178624,0.3133131,0.00001041436,0.0001924943,0.0002354291],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976028,0.0006179952,0.00002952799,0.0005924319,0.000391441,0.0003753413,0.0003296055,0.00005866415,0.000002241416],"genre_scores_gemma":[0.9990913,0.0003791172,0.0001087529,0.0001011428,0.000231575,0.00007457056,0.000001824435,0.000006342608,0.000005368551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6526816,"threshold_uncertainty_score":0.4691022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02018276960607705,"score_gpt":0.2142743445808955,"score_spread":0.1940915749748185,"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."}}