Mercury and water level management in lakes of northern Minnesota
Bibliographic record
Abstract
Abstract Water level (WL) fluctuations substantially alter the fauna, flora, and microbial community of nearshore aquatic ecosystems. Water level management therefore has the potential to strongly influence a wide variety of ecosystem processes. Many northern temperate lake food webs experience substantial methylmercury contamination, which is partially mediated by the action of sulfate‐reducing bacteria occurring in sediments that are periodically inundated. For lakes with elevated methylmercury, WL management could be designed to reduce methylmercury contamination. At the lake scale, this concept is supported by studies that identified statistical associations between fish mercury content and water level (WL) fluctuations. Here, we compiled a long‐term dataset (1997–2015) of mercury content in young‐of‐year Yellow Perch ( Perca flavescens ) from six lakes on the border of the United States and Canada and examined whether mercury content was associated with WL fluctuation. Many WL metrics covary and appear to have strong associations with Yellow Perch mercury. However, these associations appear to vary by lake, and lake‐specific models are needed to identify relationships between WL fluctuation and Yellow Perch mercury content. We used partial least‐squares regression (PLSR) to identify the associations between Yellow Perch mercury content and WL metrics, temperature, and annual deposition data for lakes in northern Minnesota. These PLSR models not only showed some variation among lakes, but also supported strong associations between WL fluctuations and annual variation in Yellow Perch mercury content. The study lakes underwent a change in WL management in 2000, when winter WL minimums were increased by about 1 m in five of the six study lakes, which reduced annual WL fluctuation on those lakes. Using the PLSR models, we estimated how this change in WL management would have affected Yellow Perch mercury content. In four of the five study lakes in which annual WL fluctuation was reduced in 2000, the change in WL management likely reduced Yellow Perch mercury content, relative to the previous WL management regime.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.005 | 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 teacher head, 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".