{"id":"W2564551776","doi":"10.2135/cropsci2016.06.0526","title":"Prospects for Cost‐Effective Genomic Selection via Accurate Within‐Family Imputation","year":2016,"lang":"en","type":"article","venue":"Crop Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; Medical Research Council Canada; Biotechnology and Biological Sciences Research Council; Genus","keywords":"Genotyping; Genomic selection; Imputation (statistics); Biology; Selection (genetic algorithm); Statistics; Genetics; Genotype; Computer science; Single-nucleotide polymorphism; Artificial intelligence; Mathematics; Gene; Missing data","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003501518,0.0001009578,0.00006872456,0.00002968277,0.0002247169,0.00003671336,0.0002061596,0.00005864961,0.000003485552],"category_scores_gemma":[0.0001565611,0.00007200536,0.00003205613,0.0001480377,0.0003329098,0.00001191986,0.00005164958,0.00002898194,0.00001496936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004510382,"about_ca_system_score_gemma":0.0002052937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003860226,"about_ca_topic_score_gemma":0.000006611152,"domain_scores_codex":[0.9990876,0.00002334281,0.0001188274,0.0004176684,0.0001061755,0.0002464422],"domain_scores_gemma":[0.9994807,0.0000229837,0.00008194211,0.0001513824,0.0001882913,0.0000747018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000772448,0.00001482836,0.0005131494,0.000004983869,0.000005384061,3.76794e-8,0.00006056605,0.0005599487,0.9759845,0.001171789,0.00012128,0.02148633],"study_design_scores_gemma":[0.0006258446,0.0007915422,0.241799,0.00001116587,0.00001054117,0.00001056032,0.00001750314,0.0003907348,0.7489128,0.005637414,0.001603978,0.0001888702],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6534575,0.00005553657,0.3452527,0.00003709909,0.000275493,0.0007000163,0.000006930824,0.00001155669,0.0002032414],"genre_scores_gemma":[0.9802076,0.000004317128,0.01891229,0.0001072155,0.0001761203,0.0001927486,0.000003988511,0.00001080542,0.0003849483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3267501,"threshold_uncertainty_score":0.2936293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01020545234945094,"score_gpt":0.2669129730392765,"score_spread":0.2567075206898256,"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."}}