Ecotoxicity of a Representative Urban Mixture of Rare Earth Elements to Hydra vulgaris
Bibliographic record
Abstract
Rare earth elements (REEs) are considered as emerging contaminants due to their use in the fabrication process of current technologies. As such, their aquatic toxicity, especially as a mixture, is not well understood, as it has been scarcely investigated. The purpose of this study was to shed light on the sublethal and lethal toxicity of a realistic mixture of five REE in Hydra vulgaris. The REE mixture was composed of five elements (Gd, Ce, Nd, Y and Dy, with a total REE concentration of 0.137 µg/L = 1× concentration) that were found in six municipal effluents in Canada at the same concentration ratios. The organisms were exposed to increasing concentrations (0.5, 1, 5, 10, 25, 50 and 100×) of the mixture for 96 h at 20 °C. The lethal and sublethal toxicities were evaluated by morphological changes and the gene expression (mRNA) involved in oxidative stress, damaged protein salvaging (autophagy for the reabsorption of damaged proteins), regeneration, neural activity and DNA repair of oxidatively damaged DNA. The data revealed that the total REE concentration of the environmental mixture was well below the lethal concentrations of the individual REEs, which occur generally at concentrations > 200 µg/L. This study proposes a novel gene transcription set to investigate the mode of action where gene expression changes occurred at concentrations below those reported in municipal effluents, suggesting long-term toxic effects in hydras close to municipal effluent discharges. This suggests that the release of REEs by municipal/hospital (for Gd) effluents should be more closely monitored.
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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) | 0.000 | 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".