Correction: Therapeutic activity of green synthesized selenium nanoparticles from turmeric against cisplatin-induced oxido-inflammatory stress, and cell death in mice kidney
Bibliographic record
Abstract
The authors of the original article “Therapeutic activity of green synthesized selenium nanoparticles from turmeric against cisplatin-induced oxido-inflammatory stress and cell death in mice kidney” (10.1042/BSR20231130) would like to correct the affiliations of the author Bassel Dawod.The author was originally listed with the following affiliations: McMaster Children's Hospital, Faculty of Health Sciences, Hamilton, Ontario, CanadaDepartment of Biology, College of Science, Jouf University, Sakaka, Al-Jouf, Saudi ArabiaDepartment of Zoology, Faculty of Science, Fayoum University, Fayoum, EgyptThe correct affiliation is McMaster Children's Hospital, Faculty of Health Sciences, Hamilton, Ontario, Canada. The two additional affiliations have now been removed for this author.The authors would also like to correct the funding statement which was originally given as:“This work was funded by the Deanship of Scientific Research at Jouf University under grant No (DSR-2021-03-03112).”This should instead state:“This work was funded by the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number 223202.”
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 0.020 |
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".