Effects of Mercury on Neurochemical Receptors in Wild River Otters (<i>Lontra canadensis</i>)
Bibliographic record
Abstract
Fish-eating wildlife, such as river otters (Lontra canadensis), accumulate mercury (Hg) at concentrations known to impair animal behavior, but few studies have explored the underlying biochemical changes that precede clinical neurotoxicity. The objective of this study was to determine if Hg exposure can be related to concentrations of neurochemical receptors in river otters. River otter carcasses (n = 66) were collected in Ontario and Nova Scotia (Canada) by local trappers in 2002-2004. Concentrations of Hg (total and organic) were measured in the cerebral cortex and cerebellum. Saturation binding curves for the cholinergic muscarinic acetylcholine (mACh) receptor and dopamine-2 (D2) receptor were completed for each animal to calculate receptor density (Bmax) and ligand affinity (Kd). Negative correlations were found between concentrations of Hg and mACh receptor Bmax (r(total) Hg = -0.458, r(inorganic) Hg = -0.454, r(organic) Hg = -0.443) in the cerebral cortex. A negative correlation was also found between concentrations of total Hg and D2 receptor Bmax (r = -0.292) in the cerebral cortex. These results suggest that neurochemical receptors may prove useful as novel biomarkers of Hg exposure and neurotoxic effects in wildlife. Given the importance of cholinergic and dopaminergic systems in animal physiology, the ecological implications of these changes need to be investigated.
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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".