Influence of Forest Canopies on the Deposition of Methylmercury to Boreal Ecosystem Watersheds
Bibliographic record
Abstract
Although it has been previously shown that forest canopies significantly increase the total deposition of Hg to watersheds, sources and fates of atmospherically deposited MeHg in particular remain poorly understood. In this study, net loadings of MeHg to a watershed were quantified, and the retention and (photo)reduction of MeHg on foliage were measured using unique stable Hg isotope experiments. Annual loadings of MeHg in throughfall (0.34 ± 0.01 to 0.60 ± 0.16 mg ha⁻¹ yr⁻¹) and litterfall (0.77 ± 0.07 to 0.97 ± 0.34 mg ha⁻¹ yr⁻¹) were collectively 3-4 times higher under different forest canopies than loadings of MeHg in the open (0.41 mg ha⁻¹ yr⁻¹), suggesting dry deposition of MeHg to forest canopies. Using Me¹⁹⁹Hg, we found that a portion of MeHg wet deposited to forest canopies is retained on foliage over time, eventually contributing to MeHg in litterfall. Average half-lives (t½) of Me¹⁹⁹Hg on spruce, jack pine, and birch foliage were 204 ± 66, 187 ± 101, and 8 ± 3 days, respectively. We also found using Me¹⁹⁹Hg that following wet deposition, MeHg is rapidly (photo)reduced to ¹⁹⁹Hg(0) on canopy foliage, which then evades to the atmosphere. We were unable to quantify concentrations of particulate-bound MeHg (p-MeHg) in the air using vacuum pumps and quartz microfiber air sampling filters, despite the possibility that p-MeHg does exist in small quantities. As a result, the source of dry deposited MeHg remains partially elusive.
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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 | 0.000 |
| 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) | 0.000 | 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".