{"id":"W4290998961","doi":"10.3390/genes13081430","title":"An Integrative Genomic Prediction Approach for Predicting Buffalo Milk Traits by Incorporating Related Cattle QTLs","year":2022,"lang":"en","type":"article","venue":"Genes","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Genomic selection; Biology; Heritability; Quantitative trait locus; Bubalus; Trait; Genomic information; Selection (genetic algorithm); Best linear unbiased prediction; Genetics; Genomics; Biotechnology; Dairy cattle; Genome; Computational biology; Statistics; Single-nucleotide polymorphism; Computer science; Mathematics; Machine learning; Gene; Genotype","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002602008,0.0001846094,0.0001498973,0.00003537278,0.0004343411,0.00002175647,0.0002664492,0.0001176357,0.00002132383],"category_scores_gemma":[0.000025155,0.0001853552,0.00007725786,0.00009180485,0.000069277,0.00000619085,0.0000964556,0.0001438791,7.654597e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003114896,"about_ca_system_score_gemma":0.0001047566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002645853,"about_ca_topic_score_gemma":0.00000564601,"domain_scores_codex":[0.9987174,0.000132702,0.0002851991,0.0004955142,0.0001283275,0.000240833],"domain_scores_gemma":[0.9994742,0.00001572104,0.0001490426,0.0002243394,0.00005479341,0.0000818818],"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":[0.0001302336,0.0002009541,0.004363541,0.0000255034,0.0001115005,1.209616e-7,0.00105807,0.03715691,0.9409617,0.0006211678,0.00439581,0.01097454],"study_design_scores_gemma":[0.01150975,0.02254105,0.1629308,0.00005616848,0.0007106899,0.0003731865,0.03675695,0.0512165,0.6196737,0.01345671,0.07693786,0.003836691],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9146364,0.002342393,0.08054146,0.00002547557,0.0002799879,0.0005967446,0.0008087205,0.00004488909,0.0007239426],"genre_scores_gemma":[0.9478825,0.00001344151,0.0480109,0.00008019763,0.0002827406,0.0003938087,0.002618223,0.00004193828,0.0006762984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.321288,"threshold_uncertainty_score":0.7558566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009262314765395173,"score_gpt":0.2260906547144091,"score_spread":0.2168283399490139,"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."}}