{"id":"W2979751848","doi":"10.1093/nar/gkz884","title":"snoDB: an interactive database of human snoRNA sequences, abundance and interactions","year":2019,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":136,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Biology; Small nucleolar RNA; Abundance (ecology); Computational biology; Genetics; Evolutionary biology; RNA; Gene; Long non-coding RNA; Ecology","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":[0.0008212054,0.000155271,0.0001909998,0.0002447583,0.0001883876,0.00006984623,0.0005111014,0.0001454768,0.0005287576],"category_scores_gemma":[0.0001854405,0.0001575285,0.00006053473,0.0003262381,0.0003576967,0.00004003937,0.0006296351,0.0006816764,0.00007184767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008193476,"about_ca_system_score_gemma":0.0002125199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004717076,"about_ca_topic_score_gemma":0.0002654656,"domain_scores_codex":[0.9977041,0.0003553159,0.0002456497,0.0006445788,0.0005782575,0.0004721082],"domain_scores_gemma":[0.9983251,0.00004945835,0.00007196161,0.0008615361,0.0004851516,0.0002067814],"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":[0.000219077,0.000114975,0.0004157836,0.00006122191,0.0000591936,0.000008255536,0.0002054026,0.00001424451,0.9945804,0.000626426,0.0006776486,0.003017303],"study_design_scores_gemma":[0.0008933945,0.001781221,0.001043254,0.0001340142,0.000008961408,0.00004789002,0.00129205,0.0008041467,0.9773899,0.0003471074,0.01601699,0.0002410568],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915521,0.0003753113,0.0008372277,0.0001324648,0.0001177987,0.0005087618,0.00007495294,0.00001441056,0.006387045],"genre_scores_gemma":[0.9964912,0.0002673557,0.0006891319,0.00004754252,0.0001182346,0.00004080171,0.0001805311,0.00004455127,0.002120669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01719056,"threshold_uncertainty_score":0.6423825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0375488036975342,"score_gpt":0.3878909243684595,"score_spread":0.3503421206709252,"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."}}