{"id":"W2042479565","doi":"10.1109/cec.2009.4982958","title":"RNA pseudoknot prediction via an evolutionary algorithm","year":2009,"lang":"en","type":"article","venue":"","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Pseudoknot; RNA; Algorithm; Nucleic acid secondary structure; Computer science; Function (biology); Sensitivity (control systems); Evolutionary algorithm; Nucleic acid structure; Computational biology; Biological system; Biology; Artificial intelligence; Genetics; Engineering; 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":[0.001153936,0.0005844284,0.001008565,0.001078325,0.0007174785,0.0007678582,0.001173348,0.001176616,0.001655966],"category_scores_gemma":[0.003544325,0.0004227196,0.0006413577,0.0007039721,0.0004470409,0.0008515729,0.0006300251,0.0007106991,0.0003809139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005264156,"about_ca_system_score_gemma":0.00108894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002176107,"about_ca_topic_score_gemma":0.002508828,"domain_scores_codex":[0.9993995,0.0002206683,0.00003001279,0.0001294382,0.0001712899,0.00004904645],"domain_scores_gemma":[0.9985625,0.0007907928,0.00009314364,0.0001204503,0.0003743175,0.00005877073],"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":[0.0002553127,0.0001297072,0.002221459,0.00005782646,0.00005643598,0.0001194857,0.00003900562,0.8413751,0.005610974,0.005790684,0.001408551,0.1429355],"study_design_scores_gemma":[0.000007269822,0.00001337519,0.00009297903,0.000001526114,0.00000231175,0.00001243653,0.000002046431,0.9984943,0.0005009224,0.0007351821,0.0001355165,0.000002147839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1659752,0.0003161,0.8282552,0.0001898609,0.00006776855,0.0001388649,0.0002022287,0.002316745,0.002537972],"genre_scores_gemma":[0.4350487,0.0001067122,0.5620992,0.0001048634,0.00003454792,0.0001371194,0.0005201634,0.0001851054,0.001763554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002176107,"threshold_uncertainty_score":0.006102622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007541450117873835,"score_gpt":0.2278102336068383,"score_spread":0.2202687834889645,"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."}}