Trapped river otters (<i>Lontra canadensis</i>) from central Saskatchewan differ in total and organic mercury concentrations by sex and geographic location
Bibliographic record
Abstract
Mercury (Hg) in wildlife remains of great concern, especially for apex piscivores. Despite this, exposure information from many species in many areas is lacking, so that management decisions are hampered. Here we examine Hg concentrations in fur, liver, and kidney tissues from river otters ( Lontra canadensis (Schreber, 1777)) ( n = 203) to quantify existing Hg concentrations over a broad geographic area in Saskatchewan. Mean fur total Hg (THg) (9.68 ± 7.52 mg/kg fresh weight (f.w.)) was significantly correlated with THg and organic Hg (OHg) in liver and kidney tissue, showcasing the potential for using fur as a noninvasive method of monitoring Hg in top-level mammals. Livers of males had higher mean OHg concentrations than livers of females (males: 2.71 mg/kg d.w., females: 1.87 mg/kg d.w.), but not significantly so. No sex-related differences were observed in kidney OHg concentrations. THg concentrations in otter fur collected in the Boreal Shield ecozone (Churchill River Upland) were significantly higher (mean = 16.1 mg/kg f.w.) than in otter fur collected from the Boreal Plain ecozone (mean = 8.59 mg/kg f.w.). Fur from otters ( n = 20; trapping block N66) trapped near a decommissioned smelter contained the highest concentrations of THg in the study (mean = 18.4 mg/kg f.w.).
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".