Drivers of wildfire burn severity in the montane rainforests of northern Vietnam
Bibliographic record
Abstract
Background Fire impacts and drivers of wildfire burn severity remain poorly understood for tropical forests. Aims To assess variation and environmental drivers of burn severity for nine forest fires in northern Vietnam. Methods Burn severity was estimated from satellite image analyses, and associations with a remotely sensed index of annual fuel production, topographic factors (elevation, slope, aspect) and weather variables (temperature, rainfall, relative humidity, wind speed) were evaluated. Key results High severity burn areas were found to be fairly uncommon and were associated with steeper, south-west facing slopes, higher elevations and lower fuel abundance. There was a weak tendency for higher burn severity on days with lower relative humidity. Conclusions Conditions that increase fire intensity and the dryness and flammability of fuels are important contributors to high severity fires in wet tropical systems. However, the pattern of higher burn severity at high elevation, where forests tend to be denser and more humid, is counter to this interpretation and may be due to species compositional changes and greater vulnerability of high-elevation forests to fire impacts. Implications Better understanding of fire risk and where in the montane forests of northern Vietnam fires are most likely to burn at high severity will assist forest fire management and recovery strategies.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".