Methylmercury in thawing peatlands on a trophic gradient in boreal Western Canada
Bibliographic record
Abstract
Ongoing permafrost thaw in the extensive peatlands of boreal western Canada may mobilize previously frozen mercury (Hg) and result in enhanced production of the neurotoxin methylmercury (MeHg). The often waterlogged conditions in thermokarst wetlands may represent ideal environments for Hg methylation to MeHg, but methylation potential could vary across distinct wetland types (i.e., nutrient-poor bogs and nutrient-rich fens) that emerge after the thawing of drier peat plateaus, depending on landscape position and groundwater connectivity. Here, we examined MeHg concentrations in twelve wetlands of varying nutrient richness in the Taiga Plains of western Canada across a 500 km permafrost gradient. We analyzed peat porewater chemistry (Hg, MeHg, dissolved organic matter composition), inferred the degree of groundwater connection (electrical conductivity, ions), and assessed the vegetation composition at each wetland. The key research objectives of this study were to 1) determine how methylmercury concentrations vary amongst wetland types in the Taiga Plains and amongst permafrost zones, and 2) understand how physicochemical characteristics and groundwater connectivity may influence methylation potential. Through this, we hope to understand the factors that lead to hotspots of MeHg production in the rapidly thawing peatlands of the Taiga Plains.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".