RETRACTED: Assessing Mercury Contamination Levels in the Sediments of Two Pyrenean Lakes
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Mercury, a trace metal, is a persistent environmental pollutant that can be detected even in remote regions, including high-mountain lakes. This study examined mercury concentrations in the sediment of two lakes in the French Pyrenees, the Legunabens and Labant lakes. Sediment samples were collected using a Hon-Kajak Sediment Corer, and mercury concentrations were measured following the EPA 7473 method with a direct mercury analyzer (DMA-80). Mercury levels reached up to 283 ng g−1 in the Legunabens lake and up to 110 ng g−1 in the Labant lake, possibly linked to the mining history of the Ariège department and atmospheric deposition from distant sources. These findings indicate significant contamination, ranging from an 8% to 42% probability of generating adverse biological effects according to Canadian standards, and approximately 90% higher concentrations compared to average mercury concentrations in other Pyrenean lakes. Such contamination poses potential risks to aquatic life and the environment due to mercury’s toxicity and bioaccumulation in microorganisms.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".