{"id":"W2126578400","doi":"10.1016/j.jtbi.2008.04.004","title":"GC skew in protein-coding genes between the leading and lagging strands in bacterial genomes: New substitution models incorporating strand bias","year":2008,"lang":"en","type":"article","venue":"Journal of Theoretical Biology","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"CAS-SAFEA International Partnership Program for Creative Research Teams; Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia y Tecnología","keywords":"Skew; Substitution (logic); Gene; Lagging; Bacterial genome size; Genome; Biology; Genetics; Coding (social sciences); Computational biology; Computer science; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001112064,0.0001476029,0.0003229488,0.0001071715,0.0001005183,0.00002771997,0.0002000154,0.000223508,0.00001298779],"category_scores_gemma":[0.0001879275,0.00009860575,0.0000753338,0.00008979181,0.000399134,0.00001616216,0.00006105525,0.0002147549,6.675986e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002035598,"about_ca_system_score_gemma":0.0001257066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009335898,"about_ca_topic_score_gemma":0.00001314044,"domain_scores_codex":[0.9986328,0.0003293316,0.0005209186,0.0001891112,0.00008568322,0.0002421197],"domain_scores_gemma":[0.9994345,0.0001006582,0.0002291741,0.0001130133,0.00003859559,0.00008408574],"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":[0.0002885701,0.00001632309,0.006774578,0.00001089504,0.00002843723,0.0000192011,0.0001705716,0.00009687713,0.9479628,0.03562715,0.000002217974,0.009002389],"study_design_scores_gemma":[0.002318735,0.001008929,0.001827708,0.0002100097,0.00003736397,0.0003335384,0.0001275836,0.000284216,0.808112,0.1849801,0.0004175129,0.0003423684],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9780893,0.001282581,0.0199413,0.0003024738,0.00008096355,0.0001838601,0.000004034199,0.000002217552,0.0001132927],"genre_scores_gemma":[0.9964174,0.00056131,0.002492745,0.00003238784,0.0004669194,0.000003734448,0.000006343869,0.00001100329,0.000008173881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1493529,"threshold_uncertainty_score":0.4021026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03942514488241593,"score_gpt":0.2643855690242811,"score_spread":0.2249604241418652,"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."}}