Ratio of Methylmercury to Dissolved Organic Carbon in Water Explains Methylmercury Bioaccumulation Across a Latitudinal Gradient from North-Temperate to Arctic Lakes
Bibliographic record
Abstract
We investigated monomethylmercury (MMHg) bioaccumulation in lakes across a 30° latitudinal gradient in eastern Canada to test the hypothesis that climate-related environmental conditions affect the sensitivity of Arctic lakes to atmospheric mercury contamination. Aquatic invertebrates (chironomid larvae, zooplankton) provided indicators of MMHg bioaccumulation near the base of benthic and planktonic food chains. In step with published data showing latitudinal declines in atmospheric mercury deposition in Canada, we observed lower total mercury concentrations in water and sediment of higher latitude lakes. Despite latitudinal declines of inorganic mercury exposure, MMHg bioaccumulation in aquatic invertebrates did not concomitantly decline. Arctic lakes with greater MMHg in aquatic invertebrates either had (1) higher water MMHg concentrations (reflecting ecosystem MMHg production) or (2) low water concentrations of MMHg, dissolved organic carbon (DOC), chlorophyll, and total nitrogen (reflecting lake sensitivity). The MMHg:DOC ratio of surface water was a strong predictor of lake sensitivity to mercury contamination. Bioaccumulation factors for biofilms and seston in Arctic lakes showed more efficient uptake of MMHg in low DOC systems. Environmental conditions associated with low biological production in Arctic lakes and their watersheds increased the sensitivity of lakes to MMHg.
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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 | 0.001 | 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".