Correction to "Genetic and pharmacologic enhancement of SUMO<sub>2</sub> conjugation prevents and reverses cognitive impairment and synaptotoxicity in a preclinical model of Alzheimer's disease"
Bibliographic record
Abstract
Fioriti L, Wijesekara N, Argyrousi EK, et al. Genetic and pharmacologic enhancement of SUMO2 conjugation prevents and reverses cognitive impairment and synaptotoxicity in a preclinical model of Alzheimer's disease. Alzheimers Dement. 2025;21(3):e70030. doi:10.1002/alz.70030 The article originally listed an individual in the acknowledgements who requested to be removed. That name has been removed from the acknowledgements section of the published article. The Acknowledgement section now reads as follows: The authors thank the technical contributions by Ms. Kathy Ha, Ms. Ling Wu, and Sara Palacino for technical support. This work was supported by the Canadian Institutes of Health Research (PJT-173497 to P.E.F.), the Weston Brain Institute (TR192065 to P.E.F.), the Alzheimer's Association (AARG 17-505136 to L.F.), National Institutes of Health (NIH-NINDS R01NS110024 and R01NS049442 to O.A., NIH R01NS134902 to L.F.), the Japanese Society for the Promotion of Science (JSPS) for a Fund for the Promotion of Joint International Research (Fostering Joint International Research) (17KK0197 to S.M.), and Grant-in-Aid for Scientific Research (C) (21K06759 to S.M.).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.113 | 0.038 |
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".