Assessing trace-metal exposure to American dippers in mountain streams of southwestern British Columbia, Canada
Bibliographic record
Abstract
To develop a suitable biomonitor of metal pollution in watersheds, we examined trends in exposure to nine trace elements in the diet (benthic invertebrates and fish), feathers (n = 104), and feces (n = 14) of an aquatic passerine, the American dipper (Cinclus mexicanus), from the Chilliwack watershed in British Columbia, Canada. We hypothesized that key differences may exist in exposure to metals for resident dippers that occupy the main river year-round and altitudinal migrants that breed on higher elevation tributaries because of differences in prey metal levels between locations or possible differences in diet composition. Metals most commonly detected in dipper feather samples in decreasing order were Zn > Cu > Hg > Se > Pb > Mn > Cd > Al > As. Resident dipper feathers contained significantly higher mean concentrations of mercury (0.64 microg/g dry wt), cadmium (0.19 microg/g dry wt), and copper (10.8 microg/g dry wt) relative to migrants. Mass balance models used to predict daily metal exposure for dippers with different diets and breeding locations within a watershed showed that variation in metal levels primarily was attributed to differences in the proportion of fish and invertebrates in the diet of residents and migrants. In comparing predicted metal exposure values to tolerable daily intakes (TDI), we found that most metals were below or within the range of TDI, except selenium, aluminum, and zinc. Other metals, such as cadmium, copper, and arsenic, were only of concern for dippers mainly feeding on insects; mercury was only of concern for dippers consuming high fish diets. The models were useful tools to demonstrate how shifts in diet and breeding location within a single watershed can result in changes in exposure that may be of toxicological significance.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".