{"id":"W4321372059","doi":"10.3390/ani13040722","title":"Utilizing Variants Identified with Multiple Genome-Wide Association Study Methods Optimizes Genomic Selection for Growth Traits in Pigs","year":2023,"lang":"en","type":"article","venue":"Animals","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Special Project for Research and Development in Key areas of Guangdong Province; Sun Yat-sen University","keywords":"Genome-wide association study; Biology; Single-nucleotide polymorphism; SNP; Genetics; Genetic association; Selection (genetic algorithm); Best linear unbiased prediction; Beef cattle; Computational biology; Genomic selection; Gene; Genotype; Computer science; Machine learning","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":[0.0008447541,0.0001578053,0.0002031101,0.00007712647,0.0001100153,0.00003872605,0.0001475868,0.0001306268,0.000009409999],"category_scores_gemma":[0.0004877828,0.0001545832,0.00005499024,0.0002461334,0.00001469036,0.000006382096,0.00005106229,0.00007736887,0.00000765493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004326094,"about_ca_system_score_gemma":0.00006372426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004348359,"about_ca_topic_score_gemma":0.0002698173,"domain_scores_codex":[0.9986816,0.0001868454,0.0002639441,0.0004294869,0.0001101291,0.0003280113],"domain_scores_gemma":[0.9993997,0.0002044962,0.0001278739,0.0001145358,0.0001059826,0.00004742294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004661611,0.000187652,0.4544531,0.00003386612,0.0002416396,7.693975e-7,0.0008552562,0.002817545,0.5396489,0.0001374709,0.0004670334,0.0006905715],"study_design_scores_gemma":[0.001617799,0.000860178,0.9850723,0.000006561291,0.00004963882,0.0000010653,0.0006010736,0.0002385775,0.01065897,0.0004872906,0.0002096125,0.0001969044],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9719563,0.0001103759,0.02669284,0.00005228278,0.00008926782,0.000861845,0.0000230784,0.00003549894,0.0001785606],"genre_scores_gemma":[0.9277329,0.00001835476,0.07131656,0.00006385003,0.0001175396,0.0001829656,0.00005626498,0.00003388477,0.0004776513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5306192,"threshold_uncertainty_score":0.6303721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02722979398127215,"score_gpt":0.3037241990382744,"score_spread":0.2764944050570022,"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."}}