{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001457909,0.0000697405,0.00009192754,0.00002482875,0.00002315936,0.00001239358,0.0001683346,0.00003235671,0.0002365313],"category_scores_gemma":[0.00003605252,0.00006390012,0.00002751361,0.0001120095,0.00004605972,0.00001743234,0.0001761609,0.00004391973,0.00004974708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001274225,"about_ca_system_score_gemma":0.00009235628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001327712,"about_ca_topic_score_gemma":0.00009279958,"domain_scores_codex":[0.9993463,0.00002632331,0.0001415,0.000221534,0.00009157325,0.0001727644],"domain_scores_gemma":[0.9996537,0.000009413146,0.0000630481,0.0002063474,0.00003684724,0.00003064874],"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":[0.00008621073,0.0002742069,0.2602646,0.0000304136,0.00001384467,0.000002670733,0.0006360264,0.0001054151,0.7372152,0.001126118,0.0001804177,0.00006497894],"study_design_scores_gemma":[0.001904052,0.000368983,0.2320461,0.00008315329,0.00001013794,0.00003879166,0.002472349,0.00067084,0.7591106,0.0001889104,0.002753518,0.0003525745],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970091,0.0000223501,0.0006954905,0.00002524772,0.0001338973,0.00009940176,0.000188794,0.000001613437,0.0018241],"genre_scores_gemma":[0.9985335,0.0000150657,0.0003305926,0.00006227198,0.00002521235,0.000007012309,0.0001116566,0.000004843489,0.0009097704],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02821848,"threshold_uncertainty_score":0.2605771,"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."}}