Influence of topography and fuels on fire refugia probability under varying fire weather conditions in forests of the Pacific Northwest, USA
Bibliographic record
Abstract
Fire refugia — locations that burn less severely or less frequently than surrounding areas — support late-successional and old-growth forest structure and function. This study investigates the influence of topography and fuels on the probability of forest fire refugia under varying fire weather conditions. We focused on recent large fires in Oregon and Washington, United States (n = 39 fires > 400 ha, 2004–2014). Our objectives were to (1) map fire refugia as a component of the burn severity gradient, (2) quantify the predictability of fire refugia as a function of prefire fuels and topography under moderate and high fire weather conditions, and (3) map the conditional probability of fire refugia to illustrate their spatial patterns in old-growth forests. Fire refugia exhibited higher predictability under relatively moderate fire weather conditions. Prefire live fuels were strong predictors of fire refugia, with higher refugia probability in forests with higher prefire biomass. In addition, fire refugia probability was higher in topographic settings with relatively northern aspects, steep catchment slopes, and concave topographic positions. Conditional probability maps revealed consistently higher fire refugia probability under moderate versus high fire weather scenarios. Results from this study inform conservation planning by determining late-successional forests most likely to persist as fire refugia despite increasing regional fire activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".