Key factors influencing Hg levels and trends in unperturbed oligotrophic temperate and boreal lakes
Bibliographic record
Abstract
Mercury (Hg) is a toxic metal that presents a major risk to ecosystems, biota, human health, and remains a priority concern. In temperate and boreal lakes Hg and methylmercury (MMHg) are expected to vary as a function of atmospheric Hg deposition, lake water chemistry, catchment characteristics and climate variables. The aim of this study was to quantify Hg and MMHg in unperturbed oligotrophic lakes and to identify the factors controlling their distribution. We first hypothesized that lake Hg (and MMHg to lesser extent) spatial variations are linked to atmospheric deposition, catchment characteristics, and terrestrial exportation of dissolved organic carbon (DOC). We secondly examined if lake Hg concentrations have followed the decrease in atmospheric Hg emission observed between the mid-1990s to the end-2010s. We found that overall, atmospheric Hg has little impact on lake Hg and MMHg concentrations, which are both primarily influenced by DOC input originating from the forest catchment. The relationship between DOC and Hg differed between the spring and the fall, with a Hg-to-DOC ratio twice as high in spring. This seems related to snowmelt input of Hg (with a relatively reduced input of DOC) or the internal lake build-up of Hg during the ice-covered period. Of the 10 lakes intensively visited over a 20-year period, only 3 showed significant lake Hg decreases despite significant negative trends in atmospheric Hg concentrations, suggesting a lag between atmospheric and surface water temporal trends. Overall, terrestrial catchments retain around 80% of atmospheric Hg implying that large Hg pools have been built up in soils in the last decades. As such, the reduction of atmospheric Hg alone will not necessarily result in Hg decreases in lakes, since the Hg concentrations may be modulated by DOC export trends and catchment characteristics. This stresses the need to improve our understanding of the processes governing Hg transfers from catchments into lakes.
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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 | 0.000 | 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".