{"id":"W3136524022","doi":"10.1111/jbg.12545","title":"Cross‐validation of best linear unbiased predictions of breeding values using an efficient leave‐one‐out strategy","year":2021,"lang":"en","type":"article","venue":"Journal of Animal Breeding and Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Genome Prairie; National Institute of Food and Agriculture; Genome Alberta; Genome Canada","keywords":"Covariance; Mathematics; Cross-validation; Statistics; Covariance matrix; Data set; Set (abstract data type); Random effects model; Pattern recognition (psychology); Computer science; Artificial intelligence","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":[0.02382504,0.002338029,0.00240531,0.001868129,0.0008740566,0.001138714,0.002088147,0.001368186,0.002335025],"category_scores_gemma":[0.03474361,0.0009894393,0.001943341,0.0009621792,0.0009139939,0.001192087,0.001773947,0.002316008,0.001380645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000830352,"about_ca_system_score_gemma":0.001974916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006564828,"about_ca_topic_score_gemma":0.007503085,"domain_scores_codex":[0.9913914,0.004773444,0.0005203771,0.001799494,0.001100109,0.0004151499],"domain_scores_gemma":[0.9764909,0.01562668,0.0009529488,0.003192056,0.003439518,0.0002981],"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":[0.001220557,0.0006507891,0.01763217,0.0003755839,0.001656818,0.0002095683,0.0003917138,0.5397967,0.02809632,0.003105169,0.004231622,0.402633],"study_design_scores_gemma":[0.00004466593,0.0001631724,0.005078746,0.00003931569,0.00007604463,0.00005192068,0.00003360907,0.9844024,0.007551454,0.00159289,0.0009199119,0.00004581568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08666583,0.0006465459,0.9071857,0.0000627617,0.00005939693,0.00019911,0.0003133146,0.003819838,0.001047593],"genre_scores_gemma":[0.5303506,0.0002118224,0.4623836,0.0002864053,0.00004321447,0.0008006561,0.002912175,0.001095984,0.00191549],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02382504,"threshold_uncertainty_score":0.1260004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06593677533761795,"score_gpt":0.3232907194138963,"score_spread":0.2573539440762783,"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."}}