Endangered, exploited glass eels (Anguilla anguilla) with critical levels of heavy metals and microplastics reveal both shipping and plastic spill threats
Bibliographic record
Abstract
In the life of the endangered but still fished Anguilla anguilla, glass eels are recruited through estuaries. These fragile ecosystems are among the most disturbed on the planet. Here, heavy metals and microplastics were measured in estuary water and European glass eels entering bay of Biscay rivers of different size and anthropogenic stress. Eels from all the estuaries exhibited cadmium exceeding legal European limits, as happened with lead in samples from the highly disturbed Avilés estuary. Several water samples from small rivers surpassed the estimated limit of microplastic for ecotoxicological safety. In multiple regression analysis, both eel lead and microplastic content were significantly explained from shipping activity in the estuaries. Eel cadmium content was not associated with estuary stressors, being probably acquired during the oceanic migration of eel larvae. The presence in eels of new white polyethylene particles that had not been found previously in the region could be explained from the marine plastic spill of "Toconao" cargo in December. The same spill could explain a significant increase of microplastic bioconcentration in the glass eels in comparison with previous surveys. These risks for critically endangered eels, and for the consumers, highlight the urgent need for reducing the impact of contaminants on both local and global scales.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".