Forest fire and its key drivers in the tropical forests of northern Vietnam
Bibliographic record
Abstract
Fire increasingly threatens tropical forests in northern Vietnam as climate changes and human population grows. Understanding fire occurrence patterns may support more effective forest management and reduce fire risk. We investigated spatiotemporal patterns and drivers of wildfire across three provinces in northern Vietnam and assessed the effectiveness of the Modified Nesterov index (MNI) fire danger rating system. We explored fire occurrence and size within and between years and forest types using descriptive analyses and developed spatiotemporal Maximum Entropy (Maxent) models incorporating variables representing potential drivers of fire, including weather, fuel, topography and human activity. Most fires occurred late in the dry season and fires were most common in natural forest. Maxent models successfully predicted fire occurrence (area under the receiver operating characteristic curve (AUC) values 0.67–0.79). While the contributions of drivers varied among provinces, MNI, temperature, elevation and distance to road were consistently important. The model for combined provinces showed that fire probability was greater under higher temperature and MNI, in areas with lower population, farther from roads, at higher elevations and in natural forests. This study suggests that an assessment integrating multiple drivers better predicts fire occurrence than a system based on weather alone and may support improved fire management and education in northern Vietnam.
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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 | 0.000 | 0.001 |
| 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".