A machine learning model to predict wildfire burn severity for pre-fire risk assessments, Utah, USA
Bibliographic record
Abstract
Abstract Background High-severity burned areas can have lasting impacts on vegetation regeneration, carbon dynamics, hydrology, and erosion. While landscape models can predict erosion from burned areas using the differenced normalized burn ratio (dNBR), post-fire erosion modeling has predominantly focused on areas that have recently burned. Here, we developed and validated a predictive burn severity model that produces continuous dNBR predictions for recently unburned forest land in Utah. Results Vegetation productivity, elevation, and canopy fuels were the most important predictor variables in the model, highlighting the strong control of fuels and vegetation on burn severity in Utah. Final model out-of-bag R 2 was 67.1%, residuals showed a correlation coefficient of 0.89 and classification accuracy into three classes was 85%. We demonstrated that dNBR can be empirically modeled relative to fuels and topography and found burn severity was highest in productive vegetation and at relatively cooler sites. Conclusions We found that prediction accuracy was higher when fuel moisture was lower, suggesting drier weather conditions drive more consistent and predictable burn severity patterns across a range of burn severity, vegetation types, and geographic locations. Moreover, burn severity predictions from this model can be used to inform hydro-erosion models and subsequent management actions aimed at reducing burn severity and post-wildfire erosion risks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".