Predicting mercury levels in yellow perch: use of water chemistry, trophic ecology, and spatial traits
Bibliographic record
Abstract
Recent research suggests that wetland abundance surrounding lakes, fish trophic position, and fish community composition may influence the bioavailability of mercury (Hg) to fish. To compare the importance of these spatial and biological factors to chemical factors known to influence bioavailability, we determined the relationship between 24 lake traits and Hg concentrations in yellow perch (Perca flavescens; whole fish samples) for 43 northern Wisconsin lakes. Independent variables included biological traits such as fish trophic position and body condition, spatial traits such as lake hydrologic position and surrounding wetland abundance, and chemical traits such as pH and water color. The strongest predictor of fish Hg levels was pH (R 2 = 0.42; p < 0.002). Of the biological traits measured, yellow perch body condition explained significant additional variation (final R 2 = 0.54; p = 0.024). Trophic position explained limited variability and population abundance of planktivores and piscivores were not correlated to perch Hg levels. Regression tree models indicated that small lakes with greater than 6% wetland in their watershed have moderately elevated fish Hg levels. Our results indicate that within-lake chemistry and fish growth patterns are stronger correlates of Hg levels in yellow perch than spatial traits, trophic position, or fish community attributes.
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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 | 0.001 |
| 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 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".