Understanding Food Web Mercury Accumulation Through Trophic Transfer and Carbon Processing along a River Affected by Recent Run-of-river Dams
Bibliographic record
Abstract
Unlike large dams which favor methylation of Hg in flooded soils over long periods, run-of-river dams are designed to flood a limited area of soils and are therefore not expected to significantly affect mercury (Hg) cycling or carbon processing. We studied the Hg and carbon cycles within food webs from several sectors along the Saint-Maurice River, Quebec, Canada, that differ in how they are influenced by two run-of-river dams and other watershed disturbances. We observed peak Hg concentrations in fish five-year postimpoundment, but these levels were reduced three years after this peak. Methylmercury concentrations in low trophic level fish and invertebrates were related to their carbon source (δ 13 C) rather than their trophic positions (δ 15 N). Biomagnification, measured by trophic magnification slopes, was driven mainly by methylmercury concentrations in low-trophic level organisms and environmental factors related to organic matter degradation and Hg-methylation. River sectors, δ 13 C and δ 15 N, predicted up to 80% of the variability in food web methylmercury concentrations. The installation of run-of-river dams and the related pondages, in association with other watershed disturbances, altered carbon processing, promoted Hg-methylation and its accumulation at the base of the food web, and led to a temporary increase in Hg levels in fish.
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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 | 0.001 | 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".