RETRACTED ARTICLE: Green synthesis of calcium nanoferrites using leaf extract of <i>Brassica oleracea</i> for photocatalysis of malachite green dye
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
We, the Editor and Publisher of International Journal of Phytoremediation have retracted the following article:Raji, P., & Kumar, K. B. (2024). Green synthesis of calcium nanoferrites using leaf extract of Brassica oleracea for photocatalysis of malachite green dye. International Journal of Phytoremediation, 26(14), 2356–2365. https://doi.org/10.1080/15226514.2024.2390188Following publication, the publisher became aware that the article contained non-standard and unscientific language. When contacted for an explanation, the authors stated that they had used automated paraphrasing software to create parts of the text. The use of paraphrasing software to obscure the origin of text is against our editorial policies. Therefore, the editor and publisher have lost confidence in the article and are retracting it. The authors disagree with the retraction.We have been informed in our decision-making by our editorial policies and the COPE guidelines.The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”.
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 | 0.007 | 0.031 |
| 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 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.017 |
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".