Tissue Distribution and Toxicological Risk Assessment of Mercury and Other Elements in Northern Populations of Wolverine (Gulo gulo)
Bibliographic record
Abstract
Wolverines are facultative scavengers that feed near the top of terrestrial food chains. We characterized concentrations of mercury and other trace elements in tissues of wolverine from a broad geographic area, representing much of their contemporary distribution in northwestern North America. We obtained tissues from 504 wolverines, from which mercury was measured on muscle (n = 448), kidney (n = 222), liver (n = 148), hair (n = 130), and brain (n = 52). In addition, methylmercury, seven trace elements (arsenic, cadmium, chromium, cobalt, lead, nickel, selenium), and arsenic compounds were measured on a subset of samples. Concentrations of mercury and other trace elements varied between tissues and were generally highest in kidney compared to brain, liver and muscle. Mercury was predominately as methylmercury in brain and muscle, but largely as inorganic mercury in liver and kidney. Mercury concentrations of hair were moderately correlated with those of internal tissues (Pearson r = 0.51-0.75, p ≤ 0.004), making hair a good non-lethal indicator of broad spatial or temporal differences in mercury exposure to wolverine. Arsenobetaine was the dominant arsenic compound identified in tissues, and arsenite, arsenocholine and dimethylarsinic acid were also detected. A preliminary risk assessment suggested the cadmium, lead, mercury, and selenium concentrations in our sample of wolverines were not likely to pose a risk of overt toxicological effects. This study generated a comprehensive dataset on mercury and other trace elements in wolverine, which will support future contaminants study of this northern terrestrial carnivore.
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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 | 0.000 |
| 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".