An improved canopy wind model for predicting wind adjustment factors and wildland fire behavior
Bibliographic record
Abstract
The ability to rapidly estimate wind speed beneath a forest canopy or near the ground surface in any vegetation is critical to practical wildland fire behavior models. The common metric of this wind speed is the “mid-flame” wind speed, UMF. However, the existing approach for estimating UMF has some significant shortcomings. These include the assumptions that both the within-canopy wind speed and the canopy structure are uniform with depth (z) throughout the canopy and that the canopy roughness length (z0) and displacement height (d) are the same regardless of canopy structure and foliage density. The purpose of this study is to develop and assess a model of canopy wind and Reynolds stress that eliminates these shortcomings and thereby provide a more physically realistic method for calculating UMF. The present model can be used for canopies of arbitrary plant surface distribution and leaf area, and the single function that describes the within-canopy wind speed is shown to reproduce observed canopy wind speed profiles across a wide variety of canopies. An equally simple analytical expression for the within-canopy Reynolds stress, [Formula: see text], also provides a reasonable description of the observed vertical profiles of Reynolds stress. In turn, [Formula: see text] is used to calculate z0 and d. Tests of operational performance are also discussed.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".