HexFire: A Flexible and Accessible Wildfire Simulator
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As fire frequency and severity grow throughout the world, scientists working across a range of disciplines will increasingly need to incorporate wildfire models into their research. However, fire simulators tend to be highly complex, time-consuming to learn, and difficult to parameterize. As a result, embracing these models can prove impractical for scientists and practitioners who are not fire specialists. Here we introduce a parsimonious wildfire simulator named HexFire that has been designed for rapid uptake by investigators who do not specialize in the mechanics of fire spread. HexFire should be useful to such nonspecialists for representing the spread of fire, interactions with fuel breaks, and for integrating wildfire into other types of ecological models. We provide a detailed description of the HexFire simulator's design and mechanisms. Our heuristic fire spread examples highlight the flexibility inherent in the model system, demonstrate that HexFire can generate a wide range of emergent fire behaviors, and illustrate how HexFire might be coupled with other environmental models. We also describe ways that HexFire itself might be altered or augmented. HexFire can be used as a proxy for more detailed fire simulators and to assess the implications of wildfire for local ecological systems. HexFire can also simulate fire interactions with fuel breaks and active fire suppression.
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.
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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it