{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000695232,0.0001668902,0.0001339366,0.00007036026,0.000045978,0.00001994974,0.0003373302,0.0001424683,0.00003623127],"category_scores_gemma":[0.000040319,0.0001323586,0.00004098859,0.0000888891,0.00005035164,0.000002629705,0.0002454618,0.00006305457,0.00006992235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000363822,"about_ca_system_score_gemma":0.00009499501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004079703,"about_ca_topic_score_gemma":0.00007874846,"domain_scores_codex":[0.9988955,0.00001419136,0.0002186753,0.0004251445,0.00008190335,0.0003645571],"domain_scores_gemma":[0.9994299,0.000010329,0.00004786918,0.0004079989,0.0000221377,0.00008174733],"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":[0.00002338049,0.00002072872,0.004399617,0.000008070787,0.00001358032,6.400224e-7,0.00006123721,0.000236206,0.7668944,0.0002321481,0.001336949,0.2267731],"study_design_scores_gemma":[0.001365572,0.0007387377,0.1758335,0.00007811363,0.00001762552,0.000009614268,0.0002386346,0.000003746877,0.7514037,0.006558896,0.06315795,0.0005938187],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9723154,0.0008488475,0.0246618,0.000735606,0.000205478,0.0004500126,0.000007867945,0.00004032538,0.0007346692],"genre_scores_gemma":[0.9767104,0.00007049614,0.0209221,0.000271758,0.0001745067,0.0001034573,0.000004948364,0.00002672075,0.001715544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2261792,"threshold_uncertainty_score":0.5397426,"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."}}