Arctic methylmercury cycling
Bibliographic record
Abstract
Anthropogenic mercury (Hg) undergoes long-range transport to the Arctic where some of it is transformed into methylmercury (MeHg), potentially leading to high exposure in some Arctic inhabitants and wildlife. The environmental exposure of Hg is determined not just by the amount of Hg entering the Arctic, but also by biogeochemical and ecological processes occurring in the Arctic. These processes affect MeHg uptake in biota by regulating the bioavailability, methylation and demethylation, bioaccumulation and biomagnification of MeHg in Arctic ecosystems. Here, we present a new budget for pools and fluxes of MeHg in the Arctic and review the scientific advances made in the last decade on processes leading to environmental exposure to Hg. Methylation and demethylation are key processes controlling the pool of MeHg available for bioaccumulation. Methylation of Hg occurs in diverse Arctic environments including permafrost, sediments and the ocean water column, and is primarily a process carried out by microorganisms. While microorganisms carrying the hgcAB gene pair (responsible for Hg methylation) have been identified in Arctic soils and thawing permafrost, the formation pathway of MeHg in oxic marine waters remains less clear. Hotspots for methylation of Hg in terrestrial environments include thermokarst wetlands, ponds and lakes. The shallow sub-surface enrichment of MeHg in the Arctic Ocean, in comparison to other marine systems, is a possible explanation for high MeHg concentrations in some Arctic biota. Bioconcentration of aqueous MeHg in bacteria and algae is a critical step in the transfer of Hg to top predators, which may be dampened or enhanced by the presence of organic matter. Variable trophic position has an important influence on MeHg concentrations among populations of top predator species such as ringed seal and polar bears distributed across the circumpolar Arctic. These scientific advances highlight key processes that affect the fate of anthropogenic Hg deposited to Arctic environments.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".