{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001440925,0.001380979,0.001262546,0.001411169,0.0007681718,0.001627963,0.001300253,0.00107036,0.01213446],"category_scores_gemma":[0.002456285,0.001225745,0.0006423444,0.001464638,0.0002427156,0.0009716692,0.001001597,0.001392781,0.005275853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004100124,"about_ca_system_score_gemma":0.0005928936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009402901,"about_ca_topic_score_gemma":0.002622721,"domain_scores_codex":[0.999215,0.0001720955,0.00006453237,0.0002459263,0.0002442669,0.00005804062],"domain_scores_gemma":[0.9986028,0.0006374032,0.00009361823,0.000265136,0.0002722933,0.0001288807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002866352,0.0002384955,0.008939605,0.001646386,0.0008788449,0.0004333892,0.0005532104,0.006330073,0.4939976,0.009605102,0.1481713,0.3263396],"study_design_scores_gemma":[0.0002573334,0.0003826571,0.01080329,0.0001865431,0.0003942483,0.001216557,0.0002168762,0.1609359,0.6349031,0.01936311,0.1709442,0.000396284],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03622541,0.002730871,0.7985796,0.0006231457,0.001071543,0.0002922205,0.02992744,0.1214934,0.009056352],"genre_scores_gemma":[0.1174043,0.001763111,0.8285113,0.001011753,0.000337535,0.0009855317,0.02900599,0.008583236,0.01239723],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01213446,"threshold_uncertainty_score":0.0405938,"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."}}