{"id":"W2038964038","doi":"10.5851/kosfa.2013.33.6.715","title":"Whole Genome Resequencing of Heugu (Korean Black Cattle) for the Genome-Wide SNP Discovery","year":2013,"lang":"en","type":"article","venue":"Korean Journal for Food Science of Animal Resources","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"National Institute of Animal Science; National Research Foundation of Korea; Rural Development Administration; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Single-nucleotide polymorphism; Biology; Genetics; dbSNP; Genome; Bovine genome; Whole genome sequencing; Reference genome; SNP; Genomics; SNP array; DNA sequencing; SNP genotyping; Computational biology; Gene; Genotype","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":[0.0009175019,0.0001745854,0.000224776,0.00009788735,0.0004416466,0.0001335571,0.001065923,0.00007393853,0.00001083014],"category_scores_gemma":[0.0004851146,0.0001170186,0.0002265998,0.0001881428,0.001365519,0.00003728907,0.0001518802,0.0001200486,0.000001454038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002266496,"about_ca_system_score_gemma":0.0002280256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000188672,"about_ca_topic_score_gemma":0.00001793047,"domain_scores_codex":[0.9983467,0.00003349416,0.0004584745,0.000325972,0.0003660085,0.0004693201],"domain_scores_gemma":[0.9985217,0.0001389456,0.0003771982,0.0003512625,0.0004471099,0.0001638018],"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":[0.0002687666,0.00004570817,0.0006563244,0.00004741041,0.00007115385,9.414698e-8,0.00112612,0.001738712,0.9936618,0.001338927,0.0003619908,0.0006829693],"study_design_scores_gemma":[0.001911289,0.02439573,0.3560335,0.0001240465,0.0001833301,0.00009228139,0.01158022,0.0003066494,0.5678135,0.01705148,0.01976492,0.0007429736],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888338,0.001175578,0.00831411,0.000405147,0.0001162384,0.0005120412,0.0001403433,0.000003249828,0.0004994958],"genre_scores_gemma":[0.9915444,0.00003490844,0.007558885,0.00006400311,0.0003330094,0.00001500918,0.00001017789,0.00002175081,0.0004178777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4258483,"threshold_uncertainty_score":0.5031312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02596474250371957,"score_gpt":0.2675375988763319,"score_spread":0.2415728563726123,"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."}}