Capturing sub-grid temperature and moisture variations for wildland fire modeling
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
Many wildfire behavior modeling studies have focused on fires during extreme conditions, where the dominant processes are resolved and smaller-scale variations have less influence on fire behavior. As such, wildfire behavior models typically perform well for these cases. However, they can struggle in marginal conditions (e.g. low-intensity fire) as small-scale variations significantly influence fire physics at scales below grid resolution. In an effort to generalize wildfire behavior models and improve their overall performance, we have developed a new set of equations for wet and dry fuel to capture the finer-scale sub-grid variations in temperature and moisture. We explore the behavior of these equations in simple scenarios ranging from high- to low-intensity fire. Furthermore, we evaluate the performance against observations of surface fire. In all cases the proposed model performs well after peak temperature is reached; however, the rise of fuel temperature at the onset of combustion is faster than expected.
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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 | 0.001 | 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) | 0.000 | 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