{"id":"W4402540940","doi":"10.1093/jas/skae234.210","title":"161 Development, deployment, and calibration of genomic selection tools to improve performance traits for Canadian beef cattle","year":2024,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Agriculture and Agri-Food Canada","funders":"","keywords":"Beef cattle; Genomic selection; Selection (genetic algorithm); Software deployment; Calibration; Biotechnology; Biology; Animal science; Computer science; Statistics; Mathematics; Genetics; Genotype; Artificial intelligence; Gene; Single-nucleotide polymorphism; Software engineering","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":[0.005192079,0.000858953,0.000499228,0.002117789,0.001121029,0.001184223,0.001441132,0.0004266648,0.001627802],"category_scores_gemma":[0.006926341,0.0002962431,0.000710413,0.003351519,0.000457766,0.000299483,0.00100316,0.0004942337,0.0004367788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006541676,"about_ca_system_score_gemma":0.009295194,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.696238,"about_ca_topic_score_gemma":0.7563832,"domain_scores_codex":[0.9973911,0.000552834,0.00008045827,0.0005165957,0.001164031,0.0002949296],"domain_scores_gemma":[0.996069,0.0007551962,0.0003313754,0.0003324285,0.002221907,0.0002901581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001395614,0.0004819486,0.3839245,0.0003346417,0.000728906,0.0003356641,0.001083075,0.1008792,0.08997373,0.00279287,0.009982723,0.4080871],"study_design_scores_gemma":[0.0002318889,0.0005405004,0.7004112,0.0001134412,0.0004076811,0.0001747114,0.0007345231,0.2447079,0.02880263,0.001052426,0.0226207,0.0002024191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8448007,0.0007722871,0.1317379,0.0004635307,0.0000603853,0.0005417459,0.01239663,0.002516369,0.006710454],"genre_scores_gemma":[0.7534088,0.0003872242,0.2267322,0.0001734519,0.00002327066,0.0003311722,0.01592006,0.0002395778,0.002784092],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.303762,"threshold_uncertainty_score":0.6111019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01512294501080505,"score_gpt":0.2522951341853695,"score_spread":0.2371721891745645,"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."}}