The response of two remote, temperate lakes to changes in atmospheric mercury deposition, sulfate, and the water cycle
Bibliographic record
Abstract
Atmospheric deposition is the ultimate source of Hg to many remote lakes, but the response to depositional change remains uncertain because of potential buffering by historical pools of Hg in sediments and soils. We investigated the responsiveness of two lakes over time scales ranging from weeks to decades. For waterborne Hg, results indicated that a precipitation-dominated seepage lake and a wetland-dominated drainage lake responded similarly to depositional change, despite high loadings of terrigeneous matter to the drainage lake. For methylmercury (meHg), the response was more complicated, reflecting the influence of multiple factors on in-lake methylation. In the seepage lake, waterborne meHg was correlated with the atmospheric deposition of Hg(II) and SO4, and there was an indirect dependence on the water cycle. In the drainage lake, although wetland export was always the dominant external source of meHg, mass balance indicated that net in-lake methylation was four- to seven-fold greater than loading from the wetland. However, in-lake meHg production was related to the export of Hg(II), SO4, dissolved organic carbon, and P from the wetland to the lake. The results suggest that atmospheric Hg deposition, weather, and microbial activity interactively effect the aquatic mercury cycle in ways that can be independent of historical pools of Hg in catchments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".