{"id":"W1984428589","doi":"10.1111/j.1439-0388.2007.00633.x","title":"Application of robust procedures for estimation of breeding values in multiple‐trait random regression test‐day model","year":2007,"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":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Best linear unbiased prediction; Outlier; Statistics; Mathematics; Random effects model; Trait; Regression; Linear regression; Ice calving; Residual; Regression analysis; Linear model; Econometrics; Lactation; Selection (genetic algorithm); Biology; Computer science; Pregnancy; Algorithm; Medicine; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02006207,0.001536658,0.002215595,0.001799031,0.0008232468,0.001493323,0.003991313,0.001362389,0.004540694],"category_scores_gemma":[0.06317495,0.001180052,0.003199722,0.001604035,0.001251224,0.001414109,0.002180442,0.004165623,0.001293571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001225003,"about_ca_system_score_gemma":0.002323967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007325327,"about_ca_topic_score_gemma":0.005615761,"domain_scores_codex":[0.9889516,0.008092722,0.0004741166,0.00116165,0.001022705,0.000297106],"domain_scores_gemma":[0.9620773,0.02933084,0.002186064,0.003722142,0.002463426,0.0002202687],"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":[0.0003661348,0.0001558528,0.006273904,0.0005553372,0.001456636,0.0004452177,0.0004342737,0.6124574,0.007459439,0.13749,0.005216231,0.2276896],"study_design_scores_gemma":[0.00003835954,0.0001171385,0.002077973,0.00004068689,0.00006096018,0.00008380179,0.00003293621,0.9490494,0.001816782,0.04253604,0.004063088,0.00008287976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001339314,0.00004295399,0.9980658,0.00002028879,0.00001041965,0.00003955119,0.00008109187,0.0002959735,0.0001045002],"genre_scores_gemma":[0.06457199,0.0001491688,0.9316726,0.00007161989,0.00004636832,0.0009563381,0.0008383581,0.0007408839,0.0009527595],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02006207,"threshold_uncertainty_score":0.1060997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02375944888929322,"score_gpt":0.2793797225881096,"score_spread":0.2556202736988164,"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."}}