{"id":"W3017302601","doi":"10.1007/978-3-030-45257-5_12","title":"Stochastic Sampling of Structural Contexts Improves the Scalability and Accuracy of RNA 3D Module Identification","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; McGill University","funders":"","keywords":"Computer science; Scalability; Sequence (biology); Identification (biology); Scope (computer science); Nucleic acid secondary structure; Algorithm; Theoretical computer science; Data mining; RNA; Database","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":[0.001814124,0.001006126,0.001509503,0.0007969649,0.0006025855,0.001171434,0.002181305,0.001596184,0.003629576],"category_scores_gemma":[0.007372515,0.0007999467,0.001164597,0.001031341,0.000812978,0.001842018,0.00249337,0.001330808,0.00137988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007543209,"about_ca_system_score_gemma":0.00116984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005409383,"about_ca_topic_score_gemma":0.0106962,"domain_scores_codex":[0.99872,0.0004211329,0.00005914054,0.0003452124,0.0003416837,0.0001127254],"domain_scores_gemma":[0.9943816,0.003751228,0.0002084316,0.001089477,0.0003814225,0.0001879074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009902366,0.0002608091,0.005887199,0.0002007445,0.0002017556,0.0001787111,0.0001545079,0.6019092,0.04000736,0.01343094,0.006520129,0.3302585],"study_design_scores_gemma":[0.00001521549,0.00001969569,0.0001914347,0.000002531224,0.000006378784,0.00002442616,0.000008031535,0.9948341,0.001911035,0.002758976,0.0002226363,0.000005606514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1219392,0.0004948222,0.8668638,0.0003239023,0.00009838828,0.0000735025,0.0004931693,0.00686723,0.002846014],"genre_scores_gemma":[0.5233924,0.0002395824,0.4717186,0.0002694006,0.00009492154,0.0001194132,0.001438988,0.0008352466,0.001891427],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005409383,"threshold_uncertainty_score":0.01214212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01570331506254085,"score_gpt":0.2590219135206993,"score_spread":0.2433185984581585,"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."}}