{"id":"W2477758523","doi":"10.5377/ceiba.v54i1.2776","title":"Looking Ahead: Applying new Genomic Technologies to Accelerate Genetic Improvement in Beef Cattle","year":2016,"lang":"en","type":"article","venue":"Ceiba","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Biology; Context (archaeology); Biotechnology; Selection (genetic algorithm); Beef cattle; Genomics; Identification (biology); Livestock; Genomic selection; Molecular breeding; Computational biology; Metagenomics; Dairy cattle; Gene; Genetics; Genome; Computer science; Ecology","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":[0.002945297,0.0004767415,0.000602992,0.0009640956,0.0002297872,0.001593026,0.0006463295,0.0009842968,0.004466217],"category_scores_gemma":[0.002159556,0.0002257702,0.0006480918,0.001190105,0.0004875537,0.001285197,0.0006576324,0.001185061,0.0008865694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006525145,"about_ca_system_score_gemma":0.0007277094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001582062,"about_ca_topic_score_gemma":0.002751227,"domain_scores_codex":[0.9994122,0.0002029202,0.00002498851,0.0001262941,0.0001663514,0.00006732361],"domain_scores_gemma":[0.9992394,0.0003041113,0.0001453247,0.0000753862,0.0001318035,0.0001039645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007774656,0.0002544916,0.01162069,0.00135062,0.0002274819,0.0003118762,0.0003598983,0.004078157,0.424677,0.01155627,0.005294276,0.5394917],"study_design_scores_gemma":[0.0007420587,0.005057697,0.1842025,0.001322493,0.001211783,0.002074107,0.001906207,0.02433743,0.3419389,0.06857458,0.3682881,0.0003439696],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4207039,0.1244953,0.3768918,0.04233897,0.00273314,0.0007068896,0.00599559,0.002984768,0.02314952],"genre_scores_gemma":[0.4420054,0.05315186,0.4825856,0.007781705,0.0007944044,0.0002148201,0.003568274,0.0002438855,0.009654072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004466217,"threshold_uncertainty_score":0.01557642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01235838797156352,"score_gpt":0.2366990528586097,"score_spread":0.2243406648870462,"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."}}