{"id":"W3009349626","doi":"10.1101/2020.03.06.980250","title":"Metabolic labeling of RNA using multiple ribonucleoside analogs enables simultaneous evaluation of transcription and degradation rates","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; Japan Society for the Promotion of Science; Research Organization of Information and Systems","keywords":"Transcription (linguistics); Degradation (telecommunications); RNA; Ribonucleoside; Gene; Cell biology; Biology; Gene expression; Transcription factor; Chemistry; Biochemistry; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007306287,0.0004683385,0.0004010691,0.0003151937,0.0002204358,0.0006760348,0.0002946471,0.0004280524,0.0008639807],"category_scores_gemma":[0.0004978974,0.0003180014,0.0003526171,0.0002837881,0.0004032197,0.0003316907,0.0002966758,0.0006696631,0.0007037689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000529639,"about_ca_system_score_gemma":0.0003050329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005990064,"about_ca_topic_score_gemma":0.001015423,"domain_scores_codex":[0.9995599,0.0000841099,0.00002734023,0.0001647398,0.000120718,0.00004318168],"domain_scores_gemma":[0.9996092,0.0001247065,0.00009006975,0.00007516579,0.00007473928,0.00002619273],"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":[0.0000385822,0.000004063487,0.0002050883,0.00003185691,0.000004834811,0.00001529926,0.000007959768,0.0001781807,0.9979377,0.0001664342,0.00003992331,0.00137],"study_design_scores_gemma":[0.000001664276,0.00001763971,0.0004575157,0.000002457436,0.000004878375,0.00002497189,0.000005211658,0.001234473,0.9970664,0.00004888447,0.001132229,0.000003597843],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7673608,0.002775436,0.2210377,0.0003537841,0.0001662055,0.00008652949,0.001923639,0.001086104,0.005209887],"genre_scores_gemma":[0.8308718,0.001974615,0.1575894,0.000213999,0.00004668339,0.0001994728,0.00253565,0.0004562296,0.006112099],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008639807,"threshold_uncertainty_score":0.00386399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03657171920695002,"score_gpt":0.2777744750385994,"score_spread":0.2412027558316494,"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."}}