{"id":"W2003843657","doi":"10.1089/cmb.2013.0085","title":"Using Structural and Evolutionary Information to Detect and Correct Pyrosequencing Errors in Noncoding RNAs","year":2013,"lang":"en","type":"article","venue":"Journal of Computational Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Agence Nationale de la Recherche","keywords":"Sequence (biology); Pointwise; Algorithm; Computer science; Computational biology; Complement (music); Pipeline (software); Theoretical computer science; Biology; Genetics; Mathematics; Gene","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":[0.002019217,0.0006784966,0.0005836602,0.001029424,0.0004340364,0.0007522244,0.0007004317,0.0009113121,0.0005948273],"category_scores_gemma":[0.0113426,0.0005471504,0.0005784241,0.0008805391,0.0004002198,0.001128477,0.000748586,0.001012561,0.0002807121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003852026,"about_ca_system_score_gemma":0.0007409129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008851578,"about_ca_topic_score_gemma":0.002231388,"domain_scores_codex":[0.9992222,0.0001939031,0.00005815562,0.0001954494,0.0002889807,0.00004139142],"domain_scores_gemma":[0.9980015,0.001095179,0.0003501145,0.0002527722,0.0002516665,0.00004880146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005129243,0.0001744206,0.03416504,0.0004896465,0.0001935221,0.0003999147,0.0004297764,0.3502521,0.1400403,0.0116548,0.0009174432,0.4607701],"study_design_scores_gemma":[0.00001992858,0.0001007426,0.006450873,0.00003385678,0.00005196125,0.0002367244,0.00005861352,0.9247173,0.05589787,0.01105228,0.001345391,0.00003437742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1652157,0.0002294894,0.8312818,0.0001070229,0.0000482862,0.00004678551,0.000226005,0.002148339,0.0006965767],"genre_scores_gemma":[0.4762394,0.0002150361,0.5219581,0.00006656757,0.00002481572,0.00004859841,0.0005440737,0.0003336497,0.0005697506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002019217,"threshold_uncertainty_score":0.01067877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01517735947202547,"score_gpt":0.2679005415797938,"score_spread":0.2527231821077683,"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."}}