Mercury concentrations and associations with dissolved organic matter are modified by water residence time in eastern Canadian lakes along a 30° latitudinal gradient
Bibliographic record
Abstract
Abstract Surface water mercury (Hg) and dissolved organic carbon (DOC) concentrations and their ratios, which play a critical role in food chain bioaccumulation of Hg, were examined in lakes from southern boreal, sub‐Arctic taiga, Arctic tundra and polar desert landscapes of eastern and northern Canada. The study sites investigated span a 30° latitudinal gradient representing differences in climate, ecosystem productivity, and atmospheric mercury deposition. Lakes were selected to obtain a range of simple morphometrics such as area, depth, volume and catchment area, with corresponding differences in water residence times (WRT), ranging from 0.1 to 7.5 years. Total mercury (THg) and mono‐methylmercury (MMHg) concentrations correlated positively but weakly with DOC in lake surface waters along the climate gradient, consistent with lower ecosystem and organic matter productivity at higher latitudes. Specific UV absorbance, an indicator of terrestrial organic matter sources, was found to explain some residual variability in THg not explained by DOC. Concentrations of THg and MMHg and their ratios with DOC, particularly the MMHg : DOC ratio as well as %MMHg, were best explained by inverse associations with WRT. These relationships were apparent both within and between regions along the latitudinal gradient, suggesting a net‐negative effect of in‐lake processing on THg and MMHg concentrations associated with longer WRTs. Since the water MMHg : DOC ratio was previously shown to explain foodweb MMHg in the same study lakes, our results suggest that smaller lakes with shorter residence times are more susceptible to MMHg exposure even at low levels of inorganic Hg loading or MMHg production.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".