Human Health Risks of Selenium-Contaminated Fish: A Case Study for Risk Assessment of Essential Elements
Bibliographic record
Abstract
A screening level human health risk assessment (HHRA) was applied to evaluate the human health implications of consuming selenium found in fish tissues collected downstream of coal mines in southeastern British Columbia, Canada. The study evaluated the potential for adverse human health effects associated with selenium, and considered known and potential benefits of selenium and fish ingestion. The results indicated that risks of selenosis due to consumption of selenium-contaminated fish in the region are negligible. Conclusions were strengthened by consideration of the potential benefits of selenium to human health, including: selenium essentiality for maintenance of good health; potential cancer prevention properties due to its role as an antioxidant; potential benefits for cardiovascular health; and other positive health benefits. The findings indicated that some aspects of the traditional framework for HHRA (e.g., application of safety factors to “err on the side of safety”) are inappropriate for the assessment of selenium-contaminated fish. Due to both deficiency and toxicity in the selenium dose-response relationship, application of compounding conservatism in risk assessment may lead to recommended intakes of fish that are contrary to the public health interest. The need for balancing risk types, for incorporating positive responses in risk assessments, and the linkage to the precautionary principle, are discussed.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".