Spatial Characteristics of Net Methylmercury Production Hot Spots in Peatlands
Bibliographic record
Abstract
Many wetlands are sources of methylmercury (MeHg) to surface waters, yet little information exists about the distribution of MeHg within wetlands. Total mercury (THg) and MeHg in peat pore waters were studied in four peatlands in spring, summer, and fall 2005. Marked spatial variability in the distribution of MeHg, and %MeHg as a proxy for net MeHg production, was observed, with highest values occurring in discrete zones. We denote these zones "MeHg hot spots", defined as an area where the pore water %MeHg exceeded the 90th percentile of the data set (n=463) or >22% of THg as MeHg. MeHg hot spots occurred near the interface between peatland and the upland watershed with few exceptions. The %MeHg in pore water was significantly less in peatland interiors compared to upland-peatland interface zones, with the significance of these differences related to the delineation of the boundary between the two areas. Although further research is necessary, our data suggest that the occurrence of MeHg hot spots is related to the transport of solutes in upland runoff to the peatland perimeter and not to the accumulation of MeHg in this zone as a result of transport from either the peatland interior or the surrounding upland watershed. These findings augment the understanding of peatland MeHg production in upland-peatland watersheds, provide guidance for more accurate quantification of MeHg pool sizes in the landscape, and a spatial framework forthe further study of mercury methylation processes in peatlands.
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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".