{"id":"W2137166663","doi":"10.1093/molbev/msl012","title":"Cytosine Usage Modulates the Correlation between CDS Length and CG Content in Prokaryotic Genomes","year":2006,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Biology; Genome; Negative selection; Cytosine; Selection (genetic algorithm); Correlation; GC-content; Positive selection; Negative correlation; Genetics; Evolutionary biology; Computational biology; Gene; Computer science; Machine learning","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":[0.0003337882,0.0001575059,0.000271674,0.0006050288,0.0001992367,0.0005150862,0.0001620055,0.0003095864,0.0006232193],"category_scores_gemma":[0.002762733,0.0002081449,0.0001217639,0.0005066627,0.0003485024,0.0003507934,0.0003660568,0.0003145621,0.0001691236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002221241,"about_ca_system_score_gemma":0.0001601255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007368315,"about_ca_topic_score_gemma":0.0009950342,"domain_scores_codex":[0.9997221,0.00006976494,0.00002367452,0.00009043568,0.00005251275,0.00004141672],"domain_scores_gemma":[0.997666,0.001220258,0.0005546779,0.0001395981,0.0002340783,0.0001854499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004995334,0.00004291676,0.3033529,0.0001293422,0.0001016288,0.0002031709,0.0002790191,0.001784009,0.6844112,0.0004487452,0.0001183969,0.008629106],"study_design_scores_gemma":[0.00001305285,0.0001617839,0.9379709,0.00001222057,0.00005137833,0.0003322967,0.0001333425,0.005797648,0.05410825,0.000854644,0.000532723,0.00003170457],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987912,0.0003014897,0.0004990684,0.00002182175,0.000002457753,0.000001844527,0.00005614975,0.00002037181,0.0003056872],"genre_scores_gemma":[0.9994037,0.00006889222,0.0003563379,0.0000175778,0.000003496107,0.000001634923,0.0000705242,0.000006346664,0.00007148132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007368315,"threshold_uncertainty_score":0.002084911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009924754739168061,"score_gpt":0.2241051432522668,"score_spread":0.2141803885130987,"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."}}