Exposure of Arctic populations to methylmercury from consumption of marine food: an updated risk-benefit assessment
Bibliographic record
Abstract
Recent and powerful epidemiological studies have been used as a basis for revising international and domestic guidelines for human exposure to mercury. Long-range transport of mercury into the Arctic makes some Arctic peoples consuming traditional marine foods, especially newborns, children and pregnant women, very vulnerable to harmful exposures. The WHO, the USEPA and Health Canada have all recently revised their mercury intake guidelines as a result of neurological effects reported in children exposed in utero and adults. Guidance values are equivalent to 0.23 microg/kg-bw/d, 0.1 microg/kg-bw/d and 0.2 microg/kg-bw/d respectively. Differences between the numbers represent slight differences in the uncertainty factors applied, rather than in toxicological interpretation. More recent findings suggest that mercury may also be a factor in ischemic heart disease, which could lower guidance values in the future. Considering the benefits of marine fatty acids (n-3 fatty acids) and guidance that populations consume 300-400g fish/week, consumers face a reality that most open ocean and relatively 'unpolluted' fish species contain levels of mercury that would lead to exposures at current guidance levels. Clearly, there is no more room for further mercury pollution and there is an urgent need for international action to reduce mercury emissions. Concomitantly, while there may be a need for public health authorities to provide consumption advisories to some highly exposed populations, such as in the Arctic, there remains a need to better understand the interactions and benefits associated with marine foods that may reduce health risks associated with low-level mercury exposure.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".