{"id":"W4289885599","doi":"10.1038/s41551-022-00934-x","title":"Publisher Correction: Efficient in vivo base editing via single adenoassociated viruses with size-optimized genomes encoding compact adenine base editors","year":2022,"lang":"en","type":"erratum","venue":"Nature Biomedical Engineering","topic":"Virus-based gene therapy research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Montreal Clinical Research Institute","funders":"National Institute of General Medical Sciences; National Human Genome Research Institute","keywords":"Base (topology); Encoding (memory); Genome; Genome editing; Computer science; In vivo; Computational biology; Biology; Genetics; Gene; Mathematics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.001015892,0.0007651742,0.0008159598,0.0006931837,0.0002025152,0.0001554707,0.0007700782,0.001984996,0.001112078],"category_scores_gemma":[0.001747829,0.0007175214,0.0002625431,0.001412913,0.0001564193,0.00001776833,0.0003176856,0.004522225,0.000003745932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007820571,"about_ca_system_score_gemma":0.000622391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005873101,"about_ca_topic_score_gemma":0.00006287083,"domain_scores_codex":[0.9952447,0.0002173074,0.0006511796,0.00113191,0.001638537,0.001116367],"domain_scores_gemma":[0.9981179,0.0002660608,0.0002928662,0.0006135187,0.000220296,0.0004893653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001021874,0.0003705476,0.00004343681,0.0001458189,0.000329214,0.0002297981,0.00006480461,0.02042145,0.1407544,2.891075e-7,0.8362843,0.0003341352],"study_design_scores_gemma":[0.003849142,0.003006344,0.00005786612,0.0005334927,0.0001060902,0.00009646729,0.0001600201,0.0231598,0.02312693,0.000001050344,0.9444787,0.0014241],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.09559404,0.06807267,0.004887378,0.005129641,0.8087303,0.00560869,0.001529803,0.001793336,0.00865412],"genre_scores_gemma":[0.6039589,0.002080224,0.007016824,0.004308459,0.2569003,0.001342616,0.02177735,0.003453395,0.09916188],"genre_candidate":"editorial","genre_consensus":null,"teacher_disagreement_score":0.55183,"threshold_uncertainty_score":0.999801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007204588174668301,"score_gpt":0.2439917303445079,"score_spread":0.2367871421698396,"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."}}