Methylmercury biogeochemistry: a review with special reference to Arctic aquatic ecosystems
Bibliographic record
Abstract
There has been increasing concern about mercury (Hg) levels in marine and freshwater organisms in the Arctic, due to the importance of traditional country foods such as fish and marine mammals to the diet of Northern Peoples. Due to its toxicity and ability to bioaccumulate and biomagnify in food webs, methylmercury (MeHg) is the form of Hg that is of greatest concern. The main sources of MeHg to Arctic aquatic ecosystems, the processes responsible for MeHg formation and degradation in the environment, MeHg bioaccumulation in Arctic biota and the human health implications for Northern Peoples are reviewed here. In Arctic marine ecosystems, Hg(II) methylation in the water column, rather than bottom sediments, is the primary source of MeHg, although a more quantitative understanding of the role of dimethylmercury (DMHg) as a MeHg source is needed. Because MeHg production in marine waters is limited by the availability of Hg(II), predicted increases in Hg(II) concentrations in oceans are likely to result in higher MeHg concentrations and increased exposure to Hg in humans and wildlife. In Arctic freshwaters, MeHg concentrations are a function of two antagonistic processes, net Hg(II) methylation in bottom sediments of ponds and lakes and MeHg photodemethylation in the water column. Hg(II) methylation is controlled by microbial activity and Hg(II) bioavailability, which in turn depend on interacting environmental factors (temperature, redox conditions, organic carbon, and sulfate) that induce nonlinear responses in MeHg production. Methylmercury bioaccumulation–biomagnification in Arctic aquatic food webs is a function of the MeHg reservoir in abiotic compartments, as well as ecological considerations such as food-chain length, growth rates, life-history characteristics, feeding behavior, and trophic interactions. Methylmercury concentrations in Arctic biota have increased significantly since the onset of the industrial age, and in some populations of fish, seabirds, and marine mammals toxicological thresholds are being exceeded. Due to the complex connection between Hg exposure and human health in Northern Peoples—arising from the dual role of country foods as both a potential Hg source and a nutritious, affordable food source with many physical and social health benefits—-reductions in anthropogenic Hg emissions are seen as the only viable long-term solution.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".