Comparing effects of fire modeling methods on simulated fire patterns and succession: a case study in the Missouri Ozarks
Bibliographic record
Abstract
We compared four fire spread simulation methods (completely random, dynamic percolation, size-based minimum travel time algorithm, and duration-based minimum travel time algorithm) and two fire occurrence simulation methods (Poisson fire frequency model and hierarchical fire frequency model) using a two-way factorial design. We examined these treatment effects on simulated forest succession dynamics and fire patterns including fire frequency, size, burned area, and shape complexity of burned patches. The comparison was carried out using a forest landscape model (LANDIS) for a surface fire regime in the Missouri Ozark Highlands. Results showed that incorporation of fuel into fire occurrence modeling significantly changed simulated dynamics of fire frequency and area burned. The duration-based minimum travel time algorithm produced the highest variability in fire size, and the dynamic percolation method produced the most irregular burned patch shapes. We also found that various fire modeling methods greatly affected temporal fire patterns in the short term, but such effects were less prominent in the long term. The simulated temporal changes in landscape-level species abundances were similar for different fire modeling methods, suggesting that a complex fire modeling method may not be necessary for examining coarse-scale vegetation dynamics.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".