Assessing the utility of sulfur isotope values for understanding mercury concentrations in water and biota from high Arctic lakes
Bibliographic record
Abstract
Methylmercury (MeHg) biomagnifies through aquatic food webs resulting in elevated concentrations in fish globally. Stable carbon and nitrogen isotopes are frequently used to determine dietary sources of MeHg and to model its biomagnification. However, given the strong links between MeHg and sulfur cycling, we investigated whether sulfur isotopes (δ 34 S) would improve our understanding of MeHg concentrations ([MeHg]) in Arctic lacustrine food webs. Delta 34 S values and total mercury (THg) or MeHg were measured in water, sediments, and biota from six lakes near Resolute Bay, NU, Canada. In two lakes impacted by historical eutrophication, aqueous sulfate δ 34 S was ∼8‰ more positive than sedimentary δ 34 S, suggestive of bacterial sulfate reduction in the sediment. In addition, aqueous δ 34 S showed a significant positive relationship with aqueous [MeHg] across lakes. Within taxa across lakes, [THg] in Arctic char muscle and [MeHg] in their main prey, chironomids, were positively related to their δ 34 S values across lakes, but inconsistent relationships were found across entire food webs among lakes. Across lakes, nitrogen isotopes were better predictors of biotic [THg] and [MeHg] than δ 34 S within this dataset. Our results suggest some linkages between Hg and S biogeochemistry in high Arctic lakes, which is an important consideration given anticipated climate-mediated changes in nutrient cycling.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".