{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008445607,0.000674714,0.0009817643,0.0004421163,0.0002861933,0.0008225643,0.001279669,0.001010575,0.001778645],"category_scores_gemma":[0.01111513,0.0004172151,0.000693387,0.001336909,0.0004479647,0.001533549,0.000569787,0.001011366,0.000317715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006488459,"about_ca_system_score_gemma":0.001111015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003294121,"about_ca_topic_score_gemma":0.003915157,"domain_scores_codex":[0.9968711,0.002540302,0.00005243236,0.0001528018,0.0002498266,0.0001336613],"domain_scores_gemma":[0.9904094,0.007887432,0.0004131336,0.000756537,0.0004367682,0.00009683414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004034044,0.0001597798,0.01325224,0.00009152199,0.0001766555,0.0001617146,0.00006452802,0.9049984,0.00277463,0.01189561,0.001022513,0.06499897],"study_design_scores_gemma":[0.0001076161,0.0001859361,0.005176038,0.00002411681,0.0000890922,0.00008504344,0.00004867801,0.974131,0.002077806,0.01639594,0.001646642,0.00003193164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3057405,0.001616374,0.6846759,0.002969592,0.00006912471,0.00009698445,0.000524262,0.0006275277,0.003679651],"genre_scores_gemma":[0.83073,0.000710915,0.1666778,0.000328605,0.0000360882,0.0001126639,0.0006568145,0.00005058795,0.0006966483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008445607,"threshold_uncertainty_score":0.04466516,"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."}}