{"id":"W4410080509","doi":"10.1139/gen-2025-0010","title":"Single-cross prediction with imputed multiomic data: a case study in rapeseed","year":2025,"lang":"en","type":"article","venue":"Genome","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Bundesministerium für Bildung und Forschung","keywords":"Biology; Imputation (statistics); Linkage disequilibrium; Genotyping; Genetics; Computational biology; SNP genotyping; SNP; Single-nucleotide polymorphism; Genomics; Genetic association; Population; Tag SNP; Genome; Genotype; Gene; Missing data; Statistics; 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":[0.009744241,0.0007375839,0.0007036187,0.0007320669,0.0008527227,0.001186249,0.001573037,0.001480784,0.000810728],"category_scores_gemma":[0.01848406,0.0003373583,0.001221853,0.001651788,0.0007117002,0.0007151479,0.0008025072,0.001052424,0.0002339794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006937287,"about_ca_system_score_gemma":0.0006939565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01457717,"about_ca_topic_score_gemma":0.0120871,"domain_scores_codex":[0.9966575,0.002115251,0.00013155,0.0006372192,0.0003147968,0.0001437115],"domain_scores_gemma":[0.9735587,0.02221931,0.001078138,0.001780529,0.001031477,0.0003317552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001885552,0.001766917,0.5878646,0.0003397142,0.001319239,0.01896258,0.002268458,0.2692201,0.008470095,0.003642949,0.002001236,0.1022586],"study_design_scores_gemma":[0.0001886654,0.001262189,0.1613236,0.00007894111,0.0006221952,0.006539545,0.001814154,0.8051898,0.01235805,0.006752475,0.00371231,0.0001580367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9436981,0.0002552148,0.05445097,0.0003500964,0.00001173273,0.0000562116,0.0004691102,0.0002159901,0.0004926155],"genre_scores_gemma":[0.9508192,0.0001239473,0.04715506,0.00008640537,0.00001575737,0.00003091954,0.001102988,0.00007655814,0.0005891391],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01457717,"threshold_uncertainty_score":0.0515331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02154194537292785,"score_gpt":0.2795622758966454,"score_spread":0.2580203305237175,"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."}}