Influence of Local Vegetation on Fire Spread Mechanisms in Surface Fires
Bibliographic record
Abstract
The climate change in northern Europe is not only leading to more extreme weather events but also causing long dry summers. This affects the moisture content of the ground and the vegetation. Causing an increased number of wildfires within Germany posing a risk to the environment, society, and firefighters. Surface fire spread in wildfires can be influenced by several environmental conditions and parameters. As the extensive knowledge gained for the US, Australia, Canada, and southern Europe might be not fully applicable to northern Europe a parametric study is carried out based on a field experiment in Saxony-Anhalt Germany. The present work utilizes the particle model of the wildland-urban interface part implemented in the Fire Dynamics Simulator to analyse numerically the influence of the local vegetation on the surface fire spread. Therefore, a 9 m2 area of grassland is modelled and the influence of parameters such as packing ratio, moisture fraction, gras height, ambient temperature, and others are analysed. The vegetation is represented using particles. The combustion is based on a three-step pyrolysis model. The results show the importance of the energy released in ratio to the energy required for pyrolysis. This is observed in models with lower grass blades, a low packing ratio or high moisture contents as these are not leading to a sustained spread. Furthermore, vegetation-specific pyrolysis kinetic parameters are used. However, the significance of these cannot be identified for the chosen case. The chosen parameters in this work are only based on single measurements or assumptions. Therefore, future research should improve the parameters by analysing a larger number of samples. In addition, it should be investigated for other case studies whether individual pyrolysis kinetic parameters are required or if the default ones are sufficient as the deviations are neglectable compared to the uncertainty of the model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".