Influence of Wildfire on Downstream Transport of Dissolved Carbon, Nutrients, and Mercury in the Permafrost Zone of Boreal Western Canada
Bibliographic record
Abstract
Abstract Northern regions are undergoing rapid change with wildfires increasing in frequency and severity alongside thawing permafrost and altered water balance. These disturbances could cause significant change in the export of carbon, nutrients, and metals to aquatic systems, with implications for food webs and ecosystem processes. Here, we examine chemical data from a series of 52 streams and rivers that were sampled across a 250,000 km2 expanse of the Taiga Plains and Taiga Shield ecozones of the Northwest Territories, Canada. Samples were collected immediately after and for 3 years following a “megafire” that occurred in this region in 2014, and included wildfire‐affected and non‐affected catchments. While wildfire has been observed to cause significant impacts on water quality in other regions, we here report weak relationships with percent watershed burn with minor to moderate effect sizes, the greatest being a reduction in dissolved organic carbon (−32% concentration). Watershed‐specific properties were a strong driver of large spatial variability in stream water chemistry, which may overwhelm or obscure lesser wildfire effects. The watershed chemical yield‐specific response to wildfire was weaker than the response for concentrations, due to substantial variation and uncertainty in runoff among sites and years. This suggests that watershed chemical yields in this region are more sensitive to changes in water balance due to climate than to altered wildfire 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".