{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007077692,0.0003450161,0.0005406471,0.001285675,0.0007656161,0.0004370467,0.0003036116,0.000543593,0.001732495],"category_scores_gemma":[0.0003928386,0.0002631876,0.0006275202,0.0009619233,0.0001628881,0.0001382935,0.00038175,0.0005585116,0.0003966739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002197362,"about_ca_system_score_gemma":0.000518156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006055025,"about_ca_topic_score_gemma":0.02590563,"domain_scores_codex":[0.9996752,0.00005476732,0.00001888911,0.0001020701,0.00008455303,0.00006447938],"domain_scores_gemma":[0.9998079,0.00005002665,0.00003175593,0.00002598948,0.00004429618,0.00004012864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0006383709,0.0001930017,0.03440774,0.0001471234,0.0003631436,0.0006430403,0.000681335,0.0004531526,0.9456199,0.0003887285,0.0008164651,0.01564809],"study_design_scores_gemma":[0.0001888874,0.0004245185,0.8927415,0.00005569514,0.00113625,0.00151782,0.0006392077,0.003391593,0.07457295,0.0004563202,0.02482016,0.0000551186],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868767,0.0007298971,0.004537671,0.00008208401,0.0000278266,0.00009495796,0.006548192,0.00005028718,0.001052247],"genre_scores_gemma":[0.953618,0.0005310004,0.01710807,0.0002733681,0.00003458871,0.0002072148,0.0240113,0.00009939148,0.00411709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006055025,"threshold_uncertainty_score":0.01203954,"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."}}