A fireside chat: large wildfires are a looming threat to US lakes
Bibliographic record
Abstract
Wildfires are becoming larger and more frequent across much of the US due to a combination of climate change and land use activities. Increasing wildfires have begun to raise concerns about effects on fresh waters, including water quality and other ecosystem services. Despite this, previous research mostly consists of short-term case studies and focuses on streams and rivers rather than lakes and reservoirs (hereafter, lakes). Using the Monitoring Trends in Burn Severity (MTBS) database, we show that 4.5% of lakes ≥ 1 ha in the continental US experienced at least one watershed wildfire from 1984-2016. Interestingly, lake watershed fires are not restricted to the western US. Of all the lower 48 states, Florida, Texas and Kansas were the top 3 states with the most lakes experiencing wildfire, whereas Idaho, Arizona and Nevada were the top 3 states by percentage of lakes in respective states experiencing wildfire. Using the LAGOS-US database, we present new regional-scale findings demonstrating effects of large wildfires on lake water quality. For example, we found a negative correlation between post-fire lake water clarity and the proportion of a lake’s watershed burned in 11 Minnesota and Wisconsin lakes (r = -0.61). We highlight the urgent need for more broad-scale studies that encompass an ecologically diverse set of waterbodies, landscapes and fire regimes, particularly in landscapes in which humans depend on lakes for fresh water. Finally, we emphasize that growing data sources such as MTBS and continental-scale water quality databases (e.g., LAGOS-US) offer prime opportunities for research advances that can help scale up findings from local case studies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".