Seasonal patterns of mercury dynamics in thermokarst lakes from sporadic permafrost
Bibliographic record
Abstract
Mercury (Hg) is a natural occurring element but is often emitted from anthropogenic sources and reaches the Arctic via long-range atmospheric transport. Organic matter (OM)-rich thermokarst lakes are characteristic features of the permafrost landscape in this region, where monomethylmercury (MMHg) production can be enhanced, as this process is mainly carried out by prokaryotes. To better understand the complex Hg biogeochemical cycle, two distinct thermokarst lakes (SAS 1A and SAS 2A) in sporadic permafrost in the Sasapimakwananistikw (SAS) River Valley, Canadian Subarctic, were sampled during winter and summer of 2022. Water column analysis showed no seasonal variation in total Hg (THg) and MMHg concentrations in SAS 2A but significantly higher THg and MMHg in winter in SAS 1A. Biogeochemical parameters affecting the activity of known methylating communities drive both inter-lake and seasonal variations in the water column. Strong correlations between MMHg and dissolved organic carbon (DOC) were found, with SAS 1A showing almost seven times more MMHg variability with DOC than SAS 2A. This difference is potentially linked to variations in OM composition between the sites. The lakes showed high THg seasonality in sediments, with higher concentrations in winter. In contrast, no seasonal variation was observed in MMHg concentrations with SAS 1A exhibiting higher values. Different divalent mercury (Hg(II)) bioavailability might explain these differences. By conducting incubation experiments with isotope-enriched Hg in the sediment, the important role of sulfate-reducing bacteria in the methylation process was revealed. This study highlights the complexity of thermokarst lakes which are increasing in Northern landscapes and might be hotspots for MMHg formation.
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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 | 0.000 |
| 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".