Publisher Correction: Efficient in vivo base editing via single adenoassociated viruses with size-optimized genomes encoding compact adenine base editors
Bibliographic record
Abstract
Authors and Affiliations Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, Cambridge, MA, USA Jessie R. Davis, Isaac P. Witte, Tony P. Huang, Jonathan M. Levy, Aditya Raguram, Samagya Banskota & David R. Liu Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA Jessie R. Davis, Isaac P. Witte, Tony P. Huang, Jonathan M. Levy, Aditya Raguram, Samagya Banskota & David R. Liu Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA Jessie R. Davis, Isaac P. Witte, Tony P. Huang, Jonathan M. Levy, Aditya Raguram, Samagya Banskota & David R. Liu Cardiovascular Institute, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA Xiao Wang & Kiran Musunuru Division of Cardiovascular Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA Xiao Wang & Kiran Musunuru Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA Xiao Wang & Kiran Musunuru Laboratory of Biochemical Neuroendocrinology, Montreal Clinical Research Institute (IRCM), University of Montreal, Montreal, Quebec, Canada Nabil G. Seidah Authors Jessie R. Davis View author publications You can also search for this author in PubMed Google Scholar Xiao Wang View author publications You can also search for this author in PubMed Google Scholar Isaac P. Witte View author publications You can also search for this author in PubMed Google Scholar Tony P. Huang View author publications You can also search for this author in PubMed Google Scholar Jonathan M. Levy View author publications You can also search for this author in PubMed Google Scholar Aditya Raguram View author publications You can also search for this author in PubMed Google Scholar Samagya Banskota View author publications You can also search for this author in PubMed Google Scholar Nabil G. Seidah View author publications You can also search for this author in PubMed Google Scholar Kiran Musunuru View author publications You can also search for this author in PubMed Google Scholar David R. Liu View author publications You can also search for this author in PubMed Google Scholar Corresponding author Correspondence to David R. Liu .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.042 | 0.026 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".