A risk assessment review of mercury exposure in Arctic marine and terrestrial mammals
Bibliographic record
Abstract
There has been a considerable number of reports on Hg concentrations in Arctic mammals since the last Arctic Monitoring and Assessment Programme (AMAP) effort to review biological effects of the exposure to mercury (Hg) in Arctic biota in 2010 and 2018. Here, we provide an update on the state of the knowledge of health risk associated with Hg concentrations in Arctic marine and terrestrial mammal species. Using available population-specific data post-2000, our ultimate goal is to provide an updated evidence-based estimate of the risk for adverse health effects from Hg exposure in Arctic mammal species at the individual and population level. Tissue residues of Hg in 13 species across the Arctic were classified into five risk categories (from No risk to Severe risk) based on critical tissue concentrations derived from experimental studies on harp seals and mink. Exposure to Hg lead to low or no risk for health effects in most populations of marine and terrestrial mammals, however, subpopulations of polar bears, pilot whales, narwhals, beluga and hooded seals are highly exposed in geographic hotspots raising concern for Hg-induced toxicological effects. About 6% of a total of 3500 individuals, across different marine mammal species, age groups and regions, are at high or severe risk of health effects from Hg exposure. The corresponding figure for the 12 terrestrial species, regions and age groups was as low as 0.3% of a total of 731 individuals analyzed for their Hg loads. Temporal analyses indicated that the proportion of polar bears at low or moderate risk has increased in East/West Greenland and Western Hudson Bay, respectively. However, there remain numerous knowledge gaps to improve risk assessments of Hg exposure in Arctic mammalian species, including the establishment of improved concentration thresholds and upscaling to the assessment of population-level effects.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".