The role of prokaryotic mercury methylators and demethylators in Canadian Arctic thermokarst lakes
Bibliographic record
Abstract
Permafrost soils are critical reservoirs for mercury (Hg), with the thawing process leading to the release of this element into the environment, posing significant environmental risks. Of particular concern is the methylated form of mercury, monomethylmercury (MMHg), known for its adverse effects on Human health. Microbial communities play a pivotal role in the formation of MMHg by facilitating Hg methylation and in the demethylation of MMHg, slowing the crossing of toxic threshold concentration in the environment. However, the specific microbes involved still need to be understood. This study aimed to identify the microbial drivers behind changes in Hg speciation (MMHg and Hg) in permafrost thaw lakes and assess the significance of the biotic component in Hg biogeochemistry. Sediment samples from two thermokarst lakes in the Canadian sub-Arctic were collected during the winter and summer of 2022. Gene-centric metagenomics using whole-genome sequencing (WGS) was employed to identify key genes involved in mercury methylation (hgcA and hgcB) and demethylation (merA and merB), supported by qPCR analyses. A seasonal decline in microbial diversity, involved in the Hg methylation, and hgcA gene coverage was observed from winter to summer, mirroring patterns in mercury methylation rates. Notably, hgcA sequences were significantly more abundant than merAB sequences, with contrasting seasonal trends. These results indicate a seasonal shift in the microbial community, transitioning from a dominance of mercury methylation in winter to a predominance of mercury demethylation in summer. Environmental drivers of these dynamics were integrated into a conceptual model. This study provide new insights on the microbial processes influencing the Hg cycle in Arctic permafrost undergoing degradation.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".