Substantial Mercury Releases and Local Deposition from Permafrost Peatland Wildfires in Southwestern Alaska
Bibliographic record
Abstract
Increasing wildfire activity at high northern latitudes has the potential to mobilize large amounts of terrestrial mercury (Hg). However, understanding implications for Hg cycling and ecosystems is hindered by sparse research on peatland wildfire Hg emissions. In this study, we used measurements of soil organic carbon (SOC) and Hg, burn depth, and environmental indices derived from satellite remote sensing to develop machine learning models for predicting Hg emissions from major wildfires in the permafrost peatland of the Yukon–Kuskokwim Delta (YKD) in southwestern Alaska. Wildfire Hg emissions during summer 2015─estimated as the product of Hg:SOC (0.38 ± 0.17 ng Hg g C 1– ), predicted SOC stores (mean [5th–95th] = 9.1 [5.3–11.2] kg C m –2 ), and burn depth (11.3 [8.2–13.9] cm)─were 556 [164–1138] kg Hg or approximately 6% of Hg emissions from wildfire activity >60°N. Modeling estimates suggest that wildfire nearly doubled summertime Hg deposition within 10 km, despite advection of more than 75% of total emissions beyond Alaska. YKD areal emissions combined with remote sensing estimates of burned area suggest that wildfire Hg emissions from northern peatlands (25.4 [14.9–33.6] Mg y –1 ) are an important component of the northern Hg budget. Additional research is needed to refine these estimates and understand the implications for Arctic and global Hg cycling.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".