Production of Methylmercury in Peatlands Following Permafrost Thaw Increases along a Trophic Gradient
Bibliographic record
Abstract
Permafrost thaw in peatlands risks increasing the production and mobilization of methylmercury (MeHg), a bioaccumulative neurotoxin that poses a health hazard to humans. We studied 12 peatlands on a trophic gradient in northwestern Canada, including permafrost peat plateaus and thawed bogs and fens, to determine the effects of thaw on MeHg production from measures of soil and porewater MeHg and in situ methylation assays. The production of MeHg was greater in thawed peatlands, especially rich fens, as indicated by higher potential rates of microbial methylation of inorganic mercury (Hg) to MeHg and higher soil %MeHg (MeHg:total Hg). Soil %MeHg was 0.1% in permafrost peat plateaus, 0.7% in bogs, and 2.0% in fens. Microbial analysis indicated three putative methylators (two methanogens and one novel bacteria) as most influential to the community composition, although their abundances were not consistently highest in fens. Fens had a greater range of porewater MeHg concentrations than bogs, potentially due to hydrological flushing, controls on MeHg solubility, or redox disequilibria in fens. MeHg in porewater was strongly associated with dissolved organic matter that had a low aromaticity and a low oxygen-to-carbon ratio. Regionally upscaling our results suggested that the expansion of bogs and fens due to thawing may increase the landscape-scale potential for MeHg production by 65% by 2100, representing a substantial risk to downstream aquatic ecosystems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".