Changes in methyl mercury concentrations in zooplankton from four experimental reservoirs with differing amounts of carbon in the flooded catchmentsThis paper is part of the series “Forty Years of Aquatic Research at the Experimental Lakes Area”.
Bibliographic record
Abstract
Methyl mercury (MeHg) concentrations in zooplankton were compared from four experimental reservoirs at the Experimental Lakes Area in northwestern Ontario, Canada, to test the hypothesis that increases in concentrations of MeHg in zooplankton would be proportional to C availability. The experimental reservoirs included three upland reservoirs flooded between 1999 and 2003 that differed in amounts of flooded organic terrestrial C (high, medium, and low C) and an experimental reservoir created over a wetland–peatland complex in 1993. After flooding, MeHg in zooplankton increased from <100 ng·g dry weight–1in inflow source waters to >500 ng·g dry weight–1in all reservoirs. In the first two years of flooding, MeHg in zooplankton was not correlated with amounts of flooded C, but the rates of decline in mean annual concentrations were negatively correlated with the amount of C stored in flooded catchments. Concentrations of MeHg in zooplankton were highly correlated with MeHg concentrations in unfiltered water, with reductions in bioaccumulation associated with increases in dissolved organic C and decreases in pH. Overall, our results suggest that reservoir designs that minimize the amount of flooded terrestrial C should result in shorter periods of elevated MeHg in the food web.
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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 | 0.001 |
| 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".