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 (δ34S) would improve our understanding of MeHg concentrations ([MeHg]) in Arctic lacustrine food webs. Delta34S 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 δ34S was ∼8‰ more positive than sedimentary δ34S, suggestive of bacterial sulfate reduction in the sediment. In addition, aqueous δ34S 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 δ34S 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 δ34S 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".