Controls on methylmercury concentrations in lakes and streams of peatland‐rich catchments along a 1700 km permafrost gradient
Bibliographic record
Abstract
Abstract Permafrost thaw may increase the production of neurotoxic methylmercury (MeHg) in northern peatlands, but the downstream delivery of MeHg is uncertain. We quantified total mercury (THg) and MeHg concentrations in lakes and streams along a 1700 km permafrost transect in boreal western Canada to determine the influence of regional permafrost extent compared to local lake and catchment characteristics. In lakes, we assessed sediment microbial communities and modeled potential rates of water column photodemethylation (PD). Regardless of permafrost conditions, peatlands were the primary sources of MeHg across the transect as MeHg concentrations in streams increased with aromatic dissolved organic carbon (DOC), iron, and lower pH. Higher DOC and greater catchment peatland extent were further associated with higher stream %MeHg (MeHg/THg). Peatland lakes were potential MeHg sinks, with lower MeHg concentrations than streams (mean ± 1SD: 0.19 ± 0.23 and 0.47 ± 0.77 ng MeHg L−1, respectively), and larger stream catchments had lower %MeHg where PD may occur in abundant small lakes. Microbial communities in lake sediments showed that abundance of Hg reducing genes (merA) predominated over Hg methylating (hgcA) and MeHg demethylating (merB) genes. The effects of permafrost extent on MeHg processes in lakes were secondary to the influence of local catchment characteristics, but lakes in regions with less permafrost had higher DOC concentrations, higher %MeHg, and lower potential rates of PD. Our study highlights a need to understand the impacts of climate change on MeHg source and sink processes, particularly as mediated through changes to peatland DOC, to improve projections of future MeHg concentrations in northern catchments.
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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".