Impacts of forest harvesting on mercury concentration, methylation and demethylation in soils and sediment in Canadian boreal forests 
Bibliographic record
Abstract
Methylmercury (MeHg) bioaccumulates through terrestrial and aquatic food webs and can irreversibly damage the central nervous system, particularly in frequent fish consumers. Forest harvesting can result in increased MeHg exports from watersheds. To date, little empirical research has been conducted to examine mercury (Hg) methylation and MeHg demethylation processes happening within soils and sediment of forest-harvested watersheds; and mechanistic details remain relatively scarce. A field investigation was therefore carried out in 5 forest-harvested and 2 unharvested boreal watersheds in northwestern Ontario Canada to better understand MeHg production and degradation in this ecosystem. Total Hg (THg) and MeHg concentrations as well as first-order potential rate constants for Hg methylation and MeHg demethylation potentials (Kmeth and Kdemeth) in soils and stream sediment were determined. Specifically, these values were compared between years before (2019) and after (2020) forest harvesting activities, as well as between harvested and unharvested watersheds in the same years. We found some increases in THg and MeHg concentrations in upland soils in harvested forests, but the overall concentrations were still relatively low. Concentrations of THg and MeHg in wetland/riparian soils remained relatively consistent and some even declined in the first year after harvesting. We found considerable increases in THg and MeHg concentrations, as well as Kmeth in stream sediment in two of the harvested watersheds, both which had significantly narrower than normal vegetated buffer zones. These results suggest that short-term (< 1 year) mercury-related impacts of harvesting activities are mostly constrained within the harvested upland zones but may stretch into downgradient stream sediment when machinery damage is very close to streams.
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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 | 0.002 | 0.001 |
| 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".