{"id":"W4396676633","doi":"10.1139/gen-2023-0126","title":"Genomic prediction in <i>Brassica napus</i>: evaluating the benefit of imputed whole-genome sequencing data","year":2024,"lang":"en","type":"article","venue":"Genome","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Bundesministerium für Bildung und Forschung","keywords":"Imputation (statistics); Biology; Genotyping; Genetics; Linkage disequilibrium; Genome; Single-nucleotide polymorphism; Population; Computational biology; SNP; SNP genotyping; Genome-wide association study; Tag SNP; Genomics; Whole genome sequencing; Genetic association; Genotype; Statistics; Missing data; Gene; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004553098,0.0004999138,0.0004083544,0.0003919964,0.0003665062,0.0006439946,0.0007651319,0.0006980211,0.0007345501],"category_scores_gemma":[0.008801915,0.000205269,0.0007312281,0.0006076029,0.000438864,0.0004320067,0.0004652111,0.0006515761,0.0001885192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005645246,"about_ca_system_score_gemma":0.0004464804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01007007,"about_ca_topic_score_gemma":0.009492557,"domain_scores_codex":[0.9988798,0.0007088844,0.0000423584,0.0002412162,0.00007905692,0.00004854789],"domain_scores_gemma":[0.9919938,0.006314877,0.0004919802,0.0006519556,0.0003998001,0.0001475732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001940136,0.0003386602,0.1509503,0.0002548393,0.0009478609,0.0006445755,0.0003447873,0.7734768,0.02071457,0.001609007,0.0006611143,0.04811736],"study_design_scores_gemma":[0.00008721626,0.000476412,0.0702615,0.00003507002,0.0001578673,0.0002122605,0.0001243288,0.915852,0.009575361,0.002144756,0.001028212,0.00004506723],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9606197,0.0003031923,0.03751548,0.0001322406,0.000009916474,0.00002215871,0.0008623401,0.0002204879,0.0003144456],"genre_scores_gemma":[0.9680829,0.00009507209,0.02847352,0.00004701556,0.000006401758,0.00002234518,0.002997385,0.00003057573,0.0002447894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01007007,"threshold_uncertainty_score":0.02407938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0378944927327962,"score_gpt":0.2864396976257796,"score_spread":0.2485452048929834,"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."}}