Sources of Methylmercury to a Wetland-Dominated Lake in Northern Wisconsin
Bibliographic record
Abstract
Several lines of evidence suggest that wetlands may be a major source of methylmercury (MeHg) to receiving waters, perhaps explaining the strong correlation between concentrations of waterborne MeHg and dissolved organic carbon (DOC) in regions such as northern Wisconsin. We evaluated the relative importance of wetland export in the MeHg budget of a wetland-dominated lake in northern Wisconsin using mass balance. Channelized runoff from a large headwater wetland was the major source of water and total mercury (HgT) to the lake during the study period. The wetland also exported MeHg in high concentrations (0.2-0.8 ng L(-1)), resulting in an export rate similar to those reported for other northern wetlands (ca. 0.3 microg MeHg m(-2) y(-1)). Yet, based on intensive sampling during 2002, the mass of MeHg that accumulated in the lake during summer was an order of magnitude greater than the export of MeHg from the wetland to the lake. Hence, a large in-lake source of MeHg is inferred from the mass balance. Most of the accumulated MeHg built-up in anoxic hypolimnetic waters; and the build-up was roughly balanced by losses of inorganic Hg (Hg(II)) implying a chemical transformation within the anoxic water column. An abundance of sulfate-reducing bacteria (SRB) in hypolimnetic waters, established by DNA analysis of the pelagic microbial community, along with a previous report documenting high methylation rates in the hypolimnion of this lake (ca. 10% d(-1)), suggest that this transformation was microbially mediated. These findings indicate that the direct effect of wetland runoff may be outweighed by indirect effects on the lacustrine MeHg cycle, enhancing the load of Hg(II), the activity of SRB, and the retention of MeHg, especially in northern lakes with flushing times longer than six months.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".