Impacts of water level fluctuations on mercury concentrations in hydropower reservoirs: A microcosm experiment
Bibliographic record
Abstract
Hydropower generation, a renewable source of electricity, has been linked to elevated methylmercury (MeHg) concentrations in impoundments and aquatic biota. This study investigates the impact of water level fluctuations (WLF) on MeHg concentrations in water, sediment, and fish. Using a set of controlled microcosm experiments emulating the drawdown/refill dynamics and subsequent sediment exposure to air experienced in reservoirs, we demonstrate that less frequent WLFs, and/or increased exposure of sediment to air, can lead to elevated MeHg concentrations in sediment, and total mercury (THg) and MeHg concentrations in water. In examining the effects of WLF frequency (two-day, weekly, and monthly), the monthly treatment displayed the highest THg and MeHg water levels, while the weekly treatment was characterized by the highest MeHg levels in the sediment. Our work supports emerging evidence that longer duration between WLF creates a larger surface area of sediment exposed to air leading to conditions conducive to higher MeHg concentrations in sediments and water. In contrast, THg, MeHg, and fatty acid trends in fish were largely inconclusive characterized by similar among-treatment effects and minimal temporal variability over the course of our experiment. This result could partly be attributed to overall low mercury levels and simple "worm-forage fish" food web in our experiment. To elucidate the broader impacts of water fluctuations on aquatic chemistry and biota, other factors (e.g., longer WLF cycles, dissolved organic matter, temperature, more complex food webs) which modulate both methylation rates and food web dynamics must be considered.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".