An assessment of mercury and its dietary drivers in fur of Arctic wolves from Greenland and High Arctic Canada
Bibliographic record
Abstract
Mercury has become a ubiquitous hazardous element even ending up in pristine areas such as the Arctic, where it biomagnifies and leaves especially top predators vulnerable to potential health effects. Here we investigate total mercury (THg) concentrations and dietary proxies for trophic position and habitat foraging (δ15N and δ13C, respectively) in fur of 30 Arctic wolves collected during 1869–1998 in the Canadian High Arctic and Greenland. Fur THg concentrations (mean ± SD) of 1.46 ± 1.39 μg g −1 dry weight are within the range of earlier reported values for other Arctic terrestrial species. Based on putative thresholds for Hg-mediated toxic health effects, the studied Arctic wolves have most likely not been at compromised health. Dietary proxies show high dietary plasticity among Arctic wolves deriving nutrition from both marine and terrestrial food sources at various trophic positions. Variability in THg concentrations seem to be related to the wolves' trophic position rather than to different carbon sources or regional differences (East Greenland, the Foxe Basin and Baffin Bay area, respectively). Although the present study remains limited due to the scarce, yet unique historic study material and small sample size, it provides novel information on temporal and spatial variation in Hg pollution of remote Arctic species.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".