Accumulation of dietary and waterborne mercury by fish - experimental and whole-ecosystem approaches using enriched stable isotopes
Bibliographic record
Abstract
To improve our understanding of how environmental mercury (Hg) concentrations influence Hg in fish, I conducted a field experiment to quantify the relative contributions of dietary and aqueous exposure to Hg levels in fish. To further assess the importance of water as a source of Hg to fish, a long-term dataset from the Mercury Experiment to Assess Atmospheric Loading in Canada and the United States was used as input data for two Hg bioaccumulation models: OneFish (accumulation from food and water) and Wisconsin (accumulation from food). Both approaches used enriched stable isotopes of Hg. Yellow perch accumulated 10-21% of their Hg directly from water. Wisconsin model predictions were significantly lower than observed fish Hg concentrations and OneFish predictions. These results suggest that waterborne Hg is an important contributor to Hg in fish and that the exclusion of water in bioaccumulation models may produce underestimates of fish Hg concentrations.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".