{"id":"W2006616325","doi":"10.1016/j.cell.2008.03.010","title":"SnapShot: The Splicing Regulatory Machinery","year":2008,"lang":"en","type":"article","venue":"Cell","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Biology; Snapshot (computer storage); RNA splicing; Computational biology; Genetics; Cell biology; Gene; RNA; Database; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003211132,0.001304669,0.001007187,0.002913339,0.00148392,0.007485787,0.001305274,0.00419167,0.01395252],"category_scores_gemma":[0.003766342,0.0008284789,0.0006490088,0.00175177,0.002093254,0.01225898,0.00293519,0.009934589,0.006040504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002326331,"about_ca_system_score_gemma":0.003253634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002451326,"about_ca_topic_score_gemma":0.0088058,"domain_scores_codex":[0.9993542,0.0001195872,0.00004080429,0.0001168647,0.0002490225,0.0001194985],"domain_scores_gemma":[0.9958033,0.001166762,0.0001573824,0.0004272102,0.0009300917,0.001515239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003623773,0.00005287668,0.0005293402,0.001041501,0.00006906456,0.000320722,0.0002463854,0.0003831715,0.008321772,0.03727509,0.8009683,0.1504295],"study_design_scores_gemma":[0.00002574221,0.00004272686,0.0007214654,0.0002989433,0.00004175927,0.0003116325,0.0001919084,0.000176677,0.00207343,0.02606193,0.9700162,0.00003747685],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.006202081,0.5268545,0.02246463,0.2451589,0.1354205,0.00009272483,0.003952032,0.004081045,0.05577361],"genre_scores_gemma":[0.06815577,0.5435354,0.04150802,0.110802,0.08589331,0.0001230532,0.005641151,0.001400604,0.1429409],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01395252,"threshold_uncertainty_score":0.04667586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01202467684353311,"score_gpt":0.2374533073942728,"score_spread":0.2254286305507397,"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."}}