Evaluating fire modelling systems in recent wildfires of the Golestan National Park, Iran
Bibliographic record
Abstract
This study analyzed differences between actual and modelled fire predictions for two recent fires that affected the Golestan National Park in northeastern Iran. FARSITE and FlamMap minimum travel time (MTT) fire modelling systems were used to compare spatial differences in burned area between observed and modelled fires. Then, the spatial variability in fire spread and behaviour related to differences in fuel types and topography was analyzed. Comparison between the observed and simulated fire perimeters showed a relatively good agreement. For both case studies, the simulations performed with the MTT algorithm presented slightly higher accuracy than the FARSITE ones. Although we found spatial differences in fire intensity and rate of spread modelling outputs, the average values in burned areas provided by FARSITE and FlamMap MTT simulations were very similar. The comparison of fire spread models provided a better understanding of their potential limits and differences in fire growth and behaviour predictions over heterogeneous-fuel landscapes, complex topography and changing weather conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| 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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".