Methylmercury Transport and Fate Shows Strong Seasonal and Spatial Variability along a High Arctic Freshwater Hydrologic Continuum
Bibliographic record
Abstract
The presence of toxic methylmercury (MeHg) in Arctic freshwater ecosystems and foodwebs is a potential health concern for northern Indigenous people. Addressing this issue requires a better understanding of MeHg production, fate during transport, and uptake into foodwebs. We used methylation assays and spatiotemporal surveys of MeHg concentrations, during the ice-covered and open water seasons, across a hydrologic continuum (composed of thaw seeps, lake/ponds, and a wetland) to identify Hg methylation hotspots and seasonal differences in MeHg cycling unique to Arctic ecosystems. Ponds and saturated wetland soils support methylation hotspots during the open water season, but subsequent export of MeHg to downstream ecosystems is limited by particle settling, binding of MeHg on soil organic matter, and/or demethylation in drier wetland soils. During the ice-covered season, MeHg concentrations in lake waters were approximately ten-fold greater than in summer; however, zooplankton MeHg concentrations were paradoxically five times lower at this time. Despite limited evidence of snow-phase methylation, the snowpack is an important MeHg reservoir. Changes in ice-cover duration will alter MeHg production and bioaccumulation in lakes, while increased thaw and surface water flow will likely result in higher methylation rates at the aquatic-terrestrial interface and more efficient downstream transport of MeHg.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".