Snowmelt Sources of Methylmercury to High Arctic Ecosystems
Bibliographic record
Abstract
Mercury in humans and other top predators living in the Arctic is present at elevated levels. Since only methylmercury (MeHg) bioaccumulates in food chains, sources of MeHg need to be identified. Recently, wetlands in the High Arctic were found to produce MeHg, and this was confirmed in laboratory soil incubations. In the present study both wetlands and snowmelt water were evaluated as sources of MeHg to Arctic ecosystems in Nunavut. Three substudies took place on Cornwallis Island, and one took place on Ellesmere Island. First, the effect of wetland presence in lake watersheds was evaluated by comparing four lakes with wetlands present to four lakes without wetlands present. Next, two individual wetlands were spatially and temporally investigated. Finally, three basin tributaries were evaluated for snowmelt MeHg sources. Catchments on Cornwallis Island with wetlands did not have an observable effect on MeHg levels in downstream lake water, but the wetland on Ellesmere Island contributed significant MeHg. In contrast, calculated yields of MeHg in tributaries draining snowmelt on Cornwallis Island were higher (ca. 1.5 mg km(-2) day(-1)) than those measured in temperate catchments characterized by wetlands. Methylmercury and total Hg concentrations in lakes, wetlands, and basin tributaries showed a strong temporal trend that corresponded to inputs from snowmelt water in late spring. This study revealed that wetland export of MeHg to downstream Arctic lakes is site dependent, and snowmelt water was the most significant source of MeHg to Arctic ecosystems located on Cornwallis Island.
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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 | 0.000 |
| 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) | 0.001 | 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".