Temperature and the Sulfur Cycle Control Monomethylmercury Cycling in High Arctic Coastal Marine Sediments from Allen Bay, Nunavut, Canada
Bibliographic record
Abstract
Monomethylmercury (MMHg) is a neurotoxin of concern in the Canadian Arctic due to its tendency to bioaccumulate and the importance of fish and wildlife in the Inuit diet. In lakes and wetlands, microbial sediment communities are integral to the cycling of MMHg; however, the role of Arctic marine sediments is poorly understood. With projected warming, the effect of temperature on the production and degradation of MMHg in Arctic environments also remains unclear. We examined MMHg dynamics across a temperature gradient (4, 12, 24 °C) in marine sediments collected in Allen Bay, Nunavut. Slurries were spiked with stable mercury isotopes and amended with specific microbial stimulants and inhibitors, and subsampled over 12 days. Maximal methylation and demethylation potentials were low, ranging from below detection to 1.13 pmol g(-1) h(-1) and 0.02 pmol g(-1) h(-1), respectively, suggesting that sediments are likely not an important source of MMHg to overlying water. Our results suggest that warming may result in an increase in Hg methylation - controlled by temperature-dependent sulfate reduction, without a compensatory increase in demethylation. This study highlights the need for further research into the role of high Arctic marine sediments and climate on the Arctic marine MMHg budget.
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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) | 0.001 | 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".