Paleolimnological Assessment of Wildfire‐Derived Atmospheric Deposition of Trace Metal(loid)s and Major Ions to Subarctic Lakes (Northwest Territories, Canada)
Bibliographic record
Abstract
Abstract Wildfires release terrestrial elements to the atmosphere as aerosols, and these events are becoming more frequent and intense in the Arctic boreal forest as the climate is warming. We quantified the impact of atmospheric deposition of aerosols from local wildfires on metal(loid) fluxes using macroscopic charcoal accumulation rates, historical fire mapping, and element concentrations in 210 Pb‐dated lake sediment from five subarctic lakes with small catchments. Lake sediments showed small but significant increases in fluxes (median = 5–10%) for 22 trace metals, metalloids, or major ions following fire events. The impact of wildfire aerosols on element fluxes was mostly due to short‐term (≤2 years) increasing sedimentation rate (6 ± 41% increase), whereas sediment element concentrations were not strongly impacted. Wildfire‐associated deposition to lake sediments was mainly composed of Ca, Al, Fe, Mg, K, Mn, and Na, which are major constituents of ash from burned biomass, but changes in sediment flux were greatest for Sb, As, Ni, Ba, Mn, Mo, and Sr compared to pre‐disturbance conditions. Compared to anthropogenic sources of pollution, wildfire‐associated atmospheric fluxes of metal contaminants to the lakes (e.g., Hg, Pb, As, Sb, and Cd) were low. This study provides quantitative estimates of wildfire impacts on atmospheric geochemical fluxes to subarctic lakes, which can be used for modeling larger‐scale impacts under changing fire regimes.
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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 | 0.001 | 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".