Is dietary mercury of neurotoxicological concern to wild polar bears (<i>Ursus maritimus</i>)?
Bibliographic record
Abstract
Polar bears (Ursus maritimus) are exposed to high concentrations of mercury because they are apex predators in the Arctic ecosystem. Although mercury is a potent neurotoxic heavy metal, it is not known whether current exposures are of neurotoxicological concern to polar bears. We tested the hypotheses that polar bears accumulate levels of mercury in their brains that exceed the estimated lowest observable adverse effect level (20 microg/g dry wt) for mammalian wildlife and that such exposures are associated with subtle neurological damage, as determined by measuring neurochemical biomarkers previously shown to be disrupted by mercury in other high-trophic wildlife. Brain stem (medulla oblongata) tissues from 82 polar bears subsistence hunted in East Greenland were studied. Despite surprisingly low levels of mercury in the brain stem region (total mercury = 0.36 +/- 0.12 microg/g dry wt), a significant negative correlation was measured between N-methyl-D-aspartate (NMDA) receptor levels and both total mercury (r = -0.34, p < 0.01) and methylmercury (r = -0.89, p < 0.05). No relationships were observed among mercury, selenium, and several other neurochemical biomarkers (dopamine-2, gamma-aminobutyric acid type A, muscarinic cholinergic, and nicotinic cholinergic receptors; cholinesterase and monoamine oxidase enzymes). These data show that East Greenland polar bears do not accumulate high levels of mercury in their brain stems. However, decreased levels of NMDA receptors could be one of the most sensitive indicators of mercury's subclinical and early effects.
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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) | 0.001 | 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".