Landscape characteristics govern the impacts of beaver ponds on surface water methylmercury concentrations in boreal watersheds
Bibliographic record
Abstract
Abstract Studies in boreal regions concerning the bioaccumulative neurotoxin methylmercury (MeHg) in natural wetlands and experimental reservoirs have shown that these waterbodies contribute to high MeHg levels in underlying sediments, inundated vegetation, and aquatic organisms. Beaver ponds are natural reservoirs that are ubiquitous in the Canadian boreal region and have been reported to increase downstream MeHg concentrations. However, the reported impacts of beaver ponds on stream MeHg vary widely across a limited number of studies, and factors influencing this variation are not well understood. To quantify the effect of beaver ponds on stream mercury concentrations, water samples were taken upstream and downstream of 10 in-channel beaver impoundments in northwestern Ontario, Canada. The downstream:upstream MeHg concentration ratios were related to pond and landscape characteristics to examine potential factors that play a role in determining the effect of beaver ponds on stream MeHg concentrations. Overall, MeHg concentrations were 1.6 times greater downstream of the beaver ponds, though this increase was not consistent; downstream concentrations up to 12 times greater and up to 5 times less were also observed. Landscape characteristics that can be readily obtained from existing spatial datasets or quantified using remote sensing techniques emerged as better predictors of downstream:upstream MeHg concentrations than site-specific stream chemistry parameters or pond characteristics that are more difficult to ascertain, with drier landscapes indicative of lower background MeHg export being more likely to exhibit greater increases in MeHg downstream of a beaver pond. These results suggest that the effects of beaver ponds on surface water MeHg concentrations are generally small but highly variable, and that the magnitude of the pond’s influence on stream MeHg are lessened in landscapes already conducive to higher MeHg concentrations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".