How reliably do keratinous tissues predict total mercury concentration in internal tissues of a highly mobile carnivore with a terrestrial diet?
Bibliographic record
Abstract
Abstract Hair offers a non‐invasive way to assess mercury exposure in diverse species, but variable and poorly understood molt patterns in wildlife may hamper the interpretation of tracer levels in hair. The reliability of using hair to precisely assess internal mercury burden, thus, varies across species, but few studies have assessed the reliability of using claw. Claws grow continuously, and growth rate may be easier to monitor than molt patterns. We quantified total mercury concentration (THg) in internal and keratinous tissues of 55 red foxes ( Vulpes vulpes ) legally harvested near Arctic tree line in Canada in winter (i.e., when mobility and opportunistic foraging are maximal) to compare the performance of hair and claw in predicting internal mercury burden, and examine the overall capacity of THg in keratinous tissues to predict internal THg at low exposure. Red foxes are widespread, occur at high densities and occupy a high trophic position, and thus could be a good sentinel species for monitoring mercury in the less‐studied terrestrial food webs. We found generally low levels of THg in all fox tissues (range: 0.06 mg kg −1 in brain to 1.13 mg kg −1 in hair) indicating overall low contamination of this low‐Arctic terrestrial food web. Relationships between THg of diverse internal organs were strong, but THg in both claws and hair were poor predictors of THg in internal organs. We thus advise caution when interpreting mercury levels in keratinous tissues of highly mobile species, as they may over‐ or underestimate the overall levels of exposure of a population because of non‐continuous or seasonal growth. Adding a time component using segmental analysis of continuously growing keratinous tissues, or associating multiple tissues reflecting diverse timeframes and physiological processes, may offer a more accurate and complete understanding of toxicological risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".