Evidence of Smoke From Wildland Fire in Surface Water of an Unburned Watershed
Bibliographic record
Abstract
Abstract Large wildland fires generate smoke that can compromise air quality over a wide area. Limited studies have suggested that smoke constituents may enter natural water bodies. In an 18‐year water monitoring study, we examined whether smoke from distant wildland fires had a detectable effect on ion content in a mountain river in an unburned watershed. Significant local smoke occurred in six years as traced by MODIS satellite data of fires, regional and local atmospheric fine particulate matter (PM2.5), and the amount of potassium (K+) in PM2.5 as a marker of vegetation combustion. Rainwater had elevated K+ and calcium (Ca2+, also associated with wildland fire smoke) in high‐smoke years compared to low‐smoke years, and was the primary route of atmospheric deposition. Similarly, river water in high‐smoke years had elevated concentrations of K+ and Ca2+, with a higher ratio of K+ to Ca2+ compared to low‐smoke years. River concentrations were generally unrelated to river discharge and observed K+ concentrations in high‐smoke and low‐smoke years could be accounted for by atmospheric deposition. Our study provides early evidence that wildland fires affect water quality far beyond the watersheds where they occur. Wildland fires distribute vast quantities of smoke containing nutrients, toxins and microbes and are increasing in North America. Potassium is a routinely‐measured water quality parameter that can act as an indicator of biomass smoke inputs. Further work is needed on the patterns and processes by which wildfire smoke enters water as well as on the consequences for ecosystems and human health.
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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".