Mercury concentration and isotopic compositions in lake waters and lake sediment from Canada and China
Bibliographic record
Abstract
Lakes are important ecosystems where mercury (Hg) may undergo extensive methylation and bioaccumulation in the aquatic food web. It is thus crucial to understand the sources of Hg in lake systems. Mercury isotope composition is a powerful tracer of its sources in the environment. However, previous studies on Hg isotope compositions in lake systems mainly focus on the aquatic organism and sediments, whereas lake water that provides the nutrients for the bottom of aquatic food chain has received much less attention. Here we report Hg isotope compositions in surface water and sediments of 8 freshwater lakes in Ontario, Canada. We determined significantly positive Delta199Hg and Delta200Hg values in surface water of all lakes, which are similar to previously reported isotope signature of local atmospheric precipitation. In contrast, the isotope compositions of lake sediments show predominantly negative Delta199Hg and negligible delta200Hg values that are similar to atmospheric elemental Hg(0) and terrestrial soils and plants. We conclude that the source of Hg in lake surface water is likely dominated by direct atmospheric precipitation, while the Hg in lake sediments is primarily derived from watershed inputs of terrestrial particles. The concurrent odd Hg isotope anomalies in surface waters suggest that methylmercury (MeHg) from the watershed wetland, together with Hg(II) derived from the catchment weathering is likely another Hg source to lake waters besides atmospheric deposition. We suggest that the different Hg sources and isotope compositions between lake water and sediments would result in different isotope signatures for MeHg that is produced in water column and in sediments. Therefore, our study not only elucidates the sources of Hg to lake systems, but also has implications in distinguishing between different MeHg sources and in tracing the bioaccumulation of MeHg in aquatic organism.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".