Dietary and Waterborne Mercury Accumulation by Yellow Perch: A Field Experiment
Bibliographic record
Abstract
It is well accepted that the majority of monomethylmercury (MMHg) in fish originates in their food; however, the additional contribution of water as a source to fish MMHg levels remains unclear. We used isotope enriched mercury (Hg) in a controlled field experiment to quantify the uptake of Hg from ingested and aqueous sources by young-of-year yellow perch (Perca flavescens). Water and zooplankton from a lake that had received (202)Hg-enriched additions (called spike Hg) for 7 y during a whole-ecosystem loading study (METAALICUS) provided natural, low-level Hg exposure. We achieved separation of exposure pathways by housing perch in one of four treatments: clean water + clean food; clean water + Hg spiked food; Hg spiked water + clean food; Hg spiked water + Hg spiked food. Fish accumulated MMHg directly from water, and this source accounted for at least 10% of MMHg in fish during the 27-d trial. Accumulation of spike Hg from water and food was additive, with food providing the majority of spike MMHg taken in by fish. Predictions from a bioenergetics model that excludes water as a source underestimated Hg in perch by 11%. This study illustrates the importance of acknowledging both food and water as sources of Hg to fish and suggests that aqueous Hg should be included as a source of contamination in bioaccumulation models and experiments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".