{"id":"W584117045","doi":"10.71781/31901","title":"RNA recurrent motifs : identification and characterization","year":2010,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computational biology; Identification (biology); RNA; Characterization (materials science); Genetics; Biology; Computer science; Gene; Nanotechnology; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003636081,0.0004727844,0.0005313405,0.002489247,0.0002960271,0.0006303287,0.0005306837,0.0006894693,0.002157414],"category_scores_gemma":[0.001065998,0.0002558317,0.00057054,0.001485517,0.0002357911,0.0003824722,0.0002707413,0.0005555914,0.001658607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002355597,"about_ca_system_score_gemma":0.0003559206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009695608,"about_ca_topic_score_gemma":0.001701518,"domain_scores_codex":[0.9996512,0.00003320561,0.000036513,0.000120615,0.0001051776,0.00005327624],"domain_scores_gemma":[0.9994521,0.0001247256,0.000134777,0.00004516355,0.0001554606,0.00008787488],"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":[0.0002009751,0.00005749671,0.003698658,0.0002852809,0.00003539136,0.0005522309,0.0001046437,0.0004313658,0.9696213,0.0004200951,0.0004507244,0.02414186],"study_design_scores_gemma":[0.00005697512,0.0008009676,0.04502777,0.0001093655,0.0002140398,0.009590592,0.0003226722,0.03315815,0.8695189,0.001040153,0.04006511,0.00009525027],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9049716,0.00832139,0.07139871,0.0003111209,0.0001009749,0.0004606138,0.006653844,0.00109275,0.006688992],"genre_scores_gemma":[0.8284522,0.00370623,0.1425972,0.0001816699,0.00008660238,0.0003855913,0.01704892,0.0002696984,0.007271967],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002489247,"threshold_uncertainty_score":0.007217288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01862580469226275,"score_gpt":0.3284749079457405,"score_spread":0.3098491032534778,"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."}}