{"id":"W2955824638","doi":"10.1093/gigascience/giz073","title":"A large interactive visual database of copy number variants discovered in taurine cattle","year":2019,"lang":"en","type":"article","venue":"GigaScience","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Alberta; Agriculture and Agri-Food Canada","funders":"Genome Alberta; Science Foundation Ireland; Department of Agriculture, Food and the Marine, Ireland; Compute Canada; Genome Canada; Western Canada Research Grid; University of California, Santa Cruz","keywords":"Copy-number variation; Bovine genome; Genome; Biology; Whole genome sequencing; Structural variation; Genetics; Genotype; Computational biology; Database; Gene; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001090127,0.00108925,0.0008315658,0.006033405,0.0005108275,0.00160081,0.001505276,0.0009673568,0.01592488],"category_scores_gemma":[0.00397823,0.000475609,0.0006059538,0.004217025,0.0002320353,0.001240686,0.001855695,0.000663447,0.004000639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007498115,"about_ca_system_score_gemma":0.0007470264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004272713,"about_ca_topic_score_gemma":0.007491698,"domain_scores_codex":[0.9992895,0.00009324336,0.00008169788,0.0002344116,0.0002250398,0.00007601612],"domain_scores_gemma":[0.9977812,0.001047927,0.0003280421,0.0003084242,0.0002871036,0.0002473105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.006110161,0.0004166885,0.04887425,0.009810399,0.001064741,0.00312448,0.003666836,0.008114457,0.1240745,0.004717125,0.443568,0.3464584],"study_design_scores_gemma":[0.001034291,0.0006134209,0.1588291,0.001655883,0.0010488,0.004231043,0.001183746,0.02011669,0.06337646,0.01135867,0.7361133,0.000438567],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0680884,0.004747284,0.02510471,0.0004350641,0.0001147354,0.0002708659,0.8417632,0.04978061,0.009695108],"genre_scores_gemma":[0.1044728,0.001868672,0.04776397,0.0003206199,0.00009826649,0.000562712,0.8367048,0.004749828,0.00345841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01592488,"threshold_uncertainty_score":0.05327398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004909729039155327,"score_gpt":0.2583804413287948,"score_spread":0.2534707122896395,"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."}}