The importance of hydrogeomorphic setting for total mercury and methylmercury export from fen wetlands in western Canada
Bibliographic record
Abstract
The export of neurotoxic mercury (Hg) and its bioavailable form, methylmercury (MeHg), from wetland-dominated catchments is common throughout boreal regions. Wetlands vary significantly, however, in their degree of minerotrophy, which affects wetland Hg cycling and is related to their hydrogeomorphic setting. In this field study, we highlight how hydrogeomorphic setting, expressed as degree of minerotrophy, impacts streamwater total-Hg (THg) and MeHg export from Canadian Western Boreal Plain, fen-dominated catchments along a series of increasing minerotrophy (poor fen < moderate fen < channel fen < rich fen). Streamwater from the catchments dominated by less minerotrophic poor and moderate fens had the highest study period MeHg yields (13 and 19 mg km−2, respectively) and percent MeHg in exported streamwater, while THg yield was greatest from the rich fen headwaters (260 mg km−2). MeHg and THg yields decreased within the rich fen. Decreases in the rich fen MeHg concentrations and yields coincides with greater total manganese, suggesting manganese redox chemistry may be important in regulating MeHg cycling and/or mobility in more minerotrophic wetlands. We extend previous studies showing some swamps to be net MeHg importers to include rich fens as another wetland type that removes MeHg from streamwaters and provides another possible mechanism, manganese reduction, that influences MeHg cycling.
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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) | 0.000 | 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".