RETRACTED: A comprehensive review of AI-based collagen valorization: Recent trends, innovations in extraction, and applications
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
This article has been retracted at the request of the Editor-in-Chief. Please see Elsevier policy on article withdrawal ( https://www.elsevier.com/about/policies-and-standards/article-withdrawal ). The Editorial Team at Green Analytical Chemistry believes there is clear evidence that there are cases of non-existent and unreliable references which challenge the integrity of this review article, and that this dictates under the journal's policy that the article needs to be retracted. The reasons for the retraction include the following: References [68] and [71] cannot be found in scientific literature bases, nor was it possible to find the journals from which the references were claimed to have originated. In addition, the links attached with the references all direct to unrelated publications. These facts all cast doubt on the integrity of the references. The Editorial Team has contacted the Authors and no reasonable explanation has been offered for this issue. Therefore a retraction has been requested by both the Handling Editor and the Editor-in-Chief.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".