{"id":"W4406261552","doi":"10.1109/bibm62325.2024.10822752","title":"Machine Learning-Driven SNP Identification: Enhancing Genomic Selection in Beef Cattle","year":2024,"lang":"en","type":"article","venue":"","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Beef cattle; Identification (biology); Selection (genetic algorithm); SNP; Genomic selection; Computer science; Computational biology; Artificial intelligence; Biology; Machine learning; Genetics; Single-nucleotide polymorphism; Genotype; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002705058,0.00036009,0.0004543911,0.0004244413,0.00019556,0.0004818267,0.0004961774,0.0003516013,0.0004697219],"category_scores_gemma":[0.003175349,0.0001602821,0.0004027904,0.0005688082,0.0002456307,0.0003750762,0.0004451261,0.0005158052,0.0001539483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002138135,"about_ca_system_score_gemma":0.0004389367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00155808,"about_ca_topic_score_gemma":0.002463544,"domain_scores_codex":[0.9993858,0.0003590924,0.00001764582,0.0001031975,0.00008206246,0.00005212687],"domain_scores_gemma":[0.998686,0.0008666788,0.0001609807,0.00007682498,0.0001694562,0.0000400898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009769809,0.0004745307,0.09202892,0.0001683571,0.0002399893,0.0002588016,0.0001931055,0.4969466,0.05014652,0.002403936,0.001099768,0.3550625],"study_design_scores_gemma":[0.00001839536,0.0001254747,0.01672466,0.00001265539,0.00003465761,0.00005352693,0.00004378609,0.9739864,0.006815409,0.001637633,0.0005311426,0.00001627707],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7301956,0.0004847676,0.2674022,0.0004154547,0.00002136503,0.00002729244,0.0002226232,0.0003108132,0.0009198649],"genre_scores_gemma":[0.9263368,0.0001354565,0.07211101,0.0001261823,0.00002365198,0.00003239552,0.0005283035,0.00003447979,0.0006717413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002705058,"threshold_uncertainty_score":0.01430583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00924299818878434,"score_gpt":0.2474660917450738,"score_spread":0.2382230935562894,"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."}}