New Insights into Traditional Health Risk Assessments of Mercury Exposure: Implications of Selenium
Bibliographic record
Abstract
There is increasing evidence that selenium (Se) has a significant effect on mercury (Hg) toxicology; however, Hg exposure risk assessments usually consider only the amount of Hg present in the environment or in food. On the basis of the present understanding of mechanisms of interaction between Se and Hg, the physiology/toxicology of Se, and the toxicology of Hg, we propose a new criterion for Se/Hg exposure assessment. This criterion, which is based on Se-Hg interactions, considers not only the toxicological consequences of Hg exposure but also the benefits and/or adverse effects of Se intake, especially the adverse effects related to a Se deficiency/excess. According to an illustrative assessment based on the new criterion and nine existing criteria, large knowledge gaps in the traditional assessments of exposure to Hg and/or Se were found, including those that assessed the interactions between Hg and Se. These results suggest that future assessments of Hg exposure (or Se intake) should include both Se and Hg.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".