Linking burn severity to pre-fire forest structure and weather on the Dixie fire offers potential to map prospective burn severity
Bibliographic record
Abstract
Seeking to understand controllable stand structure drivers of high burn severity, contingent on weather, 35 candidate predictors were derived from Forest Inventory and Analysis observations and ancillary data on managerially uncontrollable factors—fire history, climate, weather, and topography—within California’s massive 2021 Dixie fire. Logistic regression models were fitted to evaluate the effects of these predictors, with remotely sensed burn severity as the response. Using multiple model selection strategies to choose from among parsimonious predictor subsets that we structured to control for collinearity, high burn severity was consistently predicted (area under the receiver operating curve = 0.72–0.73) from just six variables: three stand characteristics—basal area of standing dead trees, basal area of mid-size or all live trees, and ladder fuel abundance—and three uncontrollable predictors—30-year mean maximum annual temperature and maximum wind gust speed and precipitation for the hour at which fire arrived at the stand. To demonstrate how these models might inform landscape treatment priority, we applied the models to publicly available, imputed rasters of the stand characteristics and to the uncontrollable factors listed above, evaluating map accuracy against Relative differenced Normalized Burn Ratio derived severity. Disappointing accuracy of the resultant maps might be improved as forest attribute imputation products begin to incorporate fine scale (e.g., Light Detection and Ranging) information capable of representing ladder fuels and as pixel scale models of weather at time of fire arrival become available.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".