{"id":"W2054028885","doi":"10.1089/cmb.2006.0108","title":"Parsing Nucleic Acid Pseudoknotted Secondary Structure: Algorithm and Applications","year":2007,"lang":"en","type":"article","venue":"Journal of Computational Biology","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Algorithm; Parsing; Nucleic acid secondary structure; Pseudoknot; Protein secondary structure; Computer science; Generality; Dynamic programming; Nucleic acid structure; Energy (signal processing); Artificial intelligence; Mathematics; RNA; Biology; Statistics","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":[0.000702154,0.00081223,0.0007945963,0.0008944806,0.0006269965,0.001020374,0.001600708,0.001730036,0.003013478],"category_scores_gemma":[0.002866579,0.0004241251,0.0006671489,0.001950834,0.000827,0.001815683,0.0007534993,0.0009878771,0.001192046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009827698,"about_ca_system_score_gemma":0.001452122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002931844,"about_ca_topic_score_gemma":0.002870176,"domain_scores_codex":[0.9994881,0.0001134357,0.00004667943,0.0001554876,0.0001448342,0.00005136236],"domain_scores_gemma":[0.9987063,0.000830033,0.00007944903,0.0001451459,0.0002005663,0.00003850127],"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":[0.0002948806,0.0002558417,0.001884364,0.0003944964,0.00005261472,0.000328017,0.0002680451,0.3171293,0.0213424,0.05914112,0.009361813,0.589547],"study_design_scores_gemma":[0.00004539172,0.00003991802,0.0002403397,0.00001916402,0.0000125116,0.0001500404,0.00003564523,0.9489238,0.009994507,0.03742862,0.003085619,0.00002443423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008688298,0.0002263884,0.9871842,0.0002273178,0.00002780792,0.00005062897,0.00008238365,0.002586527,0.0009263717],"genre_scores_gemma":[0.0642271,0.0002539015,0.9334869,0.00009889757,0.00002393148,0.0001075831,0.0003483998,0.0002748903,0.001178458],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003013478,"threshold_uncertainty_score":0.01008111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006073057763412786,"score_gpt":0.2527020174113226,"score_spread":0.2466289596479098,"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."}}