RETRACTED: Amalgamation of electrocoagulation-flotation and membrane technology: Rapid and efficient microalgal biomass recovery and fouling mitigation
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This article has been retracted: please see Elsevier policy on article withdrawal ( https://www.elsevier.com/about/policies-and-standards/article-withdrawal ). Post-publication, an investigation conducted by the editors identified concerns regarding Figure 7f, which could not be resolved with the available data. Furthermore, the affiliation of Dr Mohsen Taghavijeloudar with the Department of Civil and Environmental Engineering, Seoul National University (SNU), was questioned and the justification supplied was not accepted by the journal. The current head of the Department of Civil and Environmental Engineering at SNU was consulted on the matter, and the documentation subsequently provided by the author was reviewed but did not meet the publisher's evidentiary threshold for confirming eligibility for the listed affiliation. The investigation concluded that the affiliation of Mohsen Taghavijeloudar was not proven as the Department of Civil and Environmental Engineering, Seoul National University. Apologies are offered to readers for any confusion or inconvenience caused.
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 | 0.010 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.158 | 0.105 |
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".