{"id":"W4322622596","doi":"10.1101/2023.02.27.530265","title":"Improving the annotation of the cattle genome by annotating transcription start sites in a diverse set of tissues and populations using CAGE sequencing","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Research Executive Agency; European Commission; Université de Liège; Foreign, Commonwealth and Development Office; Scotland’s Rural College; International Livestock Research Institute; Biotechnology and Biological Sciences Research Council; Bill and Melinda Gates Foundation; Alberta Livestock and Meat Agency; University of Edinburgh; Alberta Agriculture and Forestry; Agriculture and Agri-Food Canada; Wellcome Trust","keywords":"Biology; Enhancer; Genome; Annotation; Gene Annotation; Computational biology; Genetics; Population; Gene; Transcriptome; Genomics; Transcription factor; Gene expression","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":[0.001021327,0.0005770227,0.0009146216,0.002402468,0.0008158226,0.001086111,0.0005897836,0.0005560033,0.005982053],"category_scores_gemma":[0.001746111,0.000380063,0.0008944656,0.002989049,0.0002076466,0.0005595075,0.0008926411,0.001008844,0.002554696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005177305,"about_ca_system_score_gemma":0.0008366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008233305,"about_ca_topic_score_gemma":0.0221867,"domain_scores_codex":[0.9990857,0.0001325032,0.00005966324,0.0003761191,0.000238599,0.0001074242],"domain_scores_gemma":[0.9990259,0.0003121424,0.0001386223,0.0001322021,0.000308703,0.00008243758],"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":[0.0016171,0.0001614831,0.08286677,0.004082789,0.0006980317,0.0005681011,0.001691005,0.006833373,0.7154775,0.002364672,0.04653397,0.1371052],"study_design_scores_gemma":[0.0002721183,0.0004510725,0.527593,0.000803289,0.0009282031,0.0009801423,0.001059583,0.0365846,0.1014891,0.00252416,0.3271355,0.0001793011],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3906975,0.004089296,0.0820585,0.0003783495,0.0002511081,0.0001743563,0.5031904,0.009899567,0.009260852],"genre_scores_gemma":[0.1334919,0.00101985,0.09345866,0.0002656993,0.00007747843,0.0002894023,0.7665924,0.001670255,0.003134443],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008233305,"threshold_uncertainty_score":0.0200119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04032370523054855,"score_gpt":0.2485711275180897,"score_spread":0.2082474222875412,"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."}}