Extreme Colorado 2020 fires: remotely sensed burn severity influenced by treatments, forest types, and days of burning
Bibliographic record
Abstract
Forest managers are faced with escalating size, severity, and cost of wildfires. To mitigate this, U.S. federal land management agencies are increasing forest treatments such as mechanical thinning and prescribed fire. While there is a growing body of work on treatment–wildfire interactions, treatment impacts in increasingly extreme wildfire situations remain unknown. Here we examined how treatments and previous wildfires influenced remotely sensed burn severity across four 2020 wildfires in Colorado that burned over 238 000 ha, 10 000 ha of which were treated or experienced previous wildfires. Our analyses show lower observed burn severity in treated areas across forest types and day of burning conditions. Treatments were associated with the lowest severity on days with lower wind speeds and days of limited fire growth, conditions indicative of less extreme fire weather. Additionally, treatments correlated with the lowest burn severity in lower-elevation, fire-resistant dominated forest types. This indicates both a difference in the predominant treatment types and extent of treatments in these forests and latent forest structure results in variable burn severity. This study demonstrates the importance of acting strategically about where and what types of treatments, as well as past wildfires, will mitigate future ecological impacts of wildfires 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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".