{"id":"W4407092362","doi":"10.3390/ani15030408","title":"Performance Comparison of Genomic Best Linear Unbiased Prediction and Four Machine Learning Models for Estimating Genomic Breeding Values in Working Dogs","year":2025,"lang":"en","type":"article","venue":"Animals","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"American Kennel Club Canine Health Foundation","keywords":"Heritability; Best linear unbiased prediction; Trait; Biology; Statistics; Population; Machine learning; Genetics; Mathematics; Computer science; Demography; Selection (genetic algorithm)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.008963804,0.001344825,0.001157039,0.001239406,0.0003806411,0.001035252,0.001104548,0.0009945447,0.0006420509],"category_scores_gemma":[0.01101288,0.0003687109,0.000982671,0.000610309,0.0003390472,0.0008695692,0.0008220287,0.001082423,0.0004838318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006112753,"about_ca_system_score_gemma":0.001113013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01061159,"about_ca_topic_score_gemma":0.005786734,"domain_scores_codex":[0.9977937,0.001384161,0.0001060963,0.0004084739,0.0001632343,0.0001443536],"domain_scores_gemma":[0.9912922,0.007016835,0.0003935241,0.0003857202,0.0006853776,0.0002263136],"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":[0.001681417,0.0003739086,0.1362946,0.0001825925,0.0008137708,0.0001505823,0.0001930276,0.6559278,0.001723849,0.0007291893,0.002456653,0.1994725],"study_design_scores_gemma":[0.00002390166,0.0002228718,0.009898299,0.00003087028,0.0000724744,0.0000413081,0.00004848848,0.988409,0.0005389814,0.0005179104,0.0001793736,0.00001636587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8805001,0.002732512,0.111092,0.0007438765,0.0001172604,0.00007465448,0.0008919275,0.001671365,0.002176455],"genre_scores_gemma":[0.975843,0.0002876648,0.02155684,0.0001277598,0.00003073098,0.00005169591,0.001435707,0.0000547677,0.0006119439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01061159,"threshold_uncertainty_score":0.04740572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04611561107721111,"score_gpt":0.2854643362425148,"score_spread":0.2393487251653037,"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."}}