{"id":"W1986902688","doi":"10.1016/j.cell.2011.09.002","title":"SnapShot: High-Throughput Sequencing Applications","year":2011,"lang":"en","type":"article","venue":"Cell","topic":"RNA modifications and cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Biology; Snapshot (computer storage); Computational biology; Throughput; DNA sequencing; Genetics; Gene; Computer science; Database; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003966132,0.00006232972,0.00004489517,0.00001261079,0.00006151968,0.000006991967,0.0001266718,0.00005963826,0.0002293826],"category_scores_gemma":[0.000002095572,0.00006079849,0.00003240813,0.00004996248,0.00003632389,0.000001852682,0.00003071066,0.00003495032,0.00007384682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001330946,"about_ca_system_score_gemma":0.00005005434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005016471,"about_ca_topic_score_gemma":0.00000785104,"domain_scores_codex":[0.9995645,0.000009507216,0.00008522319,0.0001925766,0.00003730061,0.0001109293],"domain_scores_gemma":[0.9995372,0.000001437794,0.00003467067,0.0003485335,0.00003800213,0.00004020642],"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":[0.000005980449,0.00004181997,0.0002212483,0.000009438007,0.000009254095,5.232781e-7,0.00008971677,0.00001294339,0.9921632,0.00173872,0.004467351,0.001239844],"study_design_scores_gemma":[0.000134639,0.00003840514,0.0008346093,0.000001490918,0.000009689178,0.000002330132,0.0000811787,0.000009162493,0.8549103,0.0005982304,0.1432688,0.0001111884],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7260413,0.001580367,0.05409948,0.00006328567,0.0001720994,0.0003375424,0.00002705874,0.00002878,0.2176501],"genre_scores_gemma":[0.9935284,0.00014114,0.003813318,0.0001764378,0.0001501908,0.0001077757,0.0001002671,0.00001135952,0.001971098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2674871,"threshold_uncertainty_score":0.2511579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02599777782921255,"score_gpt":0.23165893105008,"score_spread":0.2056611532208675,"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."}}