Selenium–mercury interactions and relationship to aquatic toxicity: a review
Bibliographic record
Abstract
A review of the literature pertaining to selenium-mercury (Se/Hg) interactions in aquatic species was performed to provide insight into the mechanisms allowing for the reported changes in bioaccumulation and toxicity that have been observed when the two elements occur at elevated concentrations. Selenium (Se) has been shown to protect against mercury (Hg) toxicity in all animal models evaluated (fish, birds, mammals, and plants). To explore the interaction between the two elements, data are presented on concentrations of both elements in wild-caught fish at numerous locations. The data show that most fish have Se/Hg ratios >1.0. The importance of this ratio has been reported, with suggestions that the protective interaction is due in large part to the formation of HgSe. Data show that when the Se/Hg molar ratio is <1.0 in the diet of fish and animals, Hg toxicity will be expressed, provided that the Hg concentration is sufficiently high. This toxicity is likely the result of Se deficiency leading to an excess of reactive oxygen species. Laboratory fish toxicity studies reviewed show that Se toxicity can be reduced or eliminated when Hg is added to the diet in moderate amounts. Field studies have shown reduced accumulation of Hg when Se concentrations are increased. When Hg in the diet is significantly elevated (usually >10 µg/g), toxicity is expressed regardless of the Se present. Likewise, amelioration of Se toxicity by Hg occurs over a limited range. Tissue thresholds for Se toxicity have been derived primarily from studies where fish eggs were extracted from wild fish and embryo deformities were observed; however, the amount of Hg in the fish or ovaries was not considered, which could lead to uncertainty in the toxicity threshold. It is recommended that both elements be measured and evaluated when performing risk assessments and setting water quality criteria.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".