A model of gas mixing into single-entrance tree cavities during wildland fires
Bibliographic record
Abstract
The level of protection to fauna provided by tree cavities during wildland fires is not well understood. Here we present a model for estimating the transport of combustion gases into cylindrical, single-entrance cavities during exposures caused by different wildland fire scenarios. In these shelters, the entrance occurs near the top of the cavity. This empirical model was developed from a suite of numerical experiments using the National Institute of Standards and Technology’s Fire Dynamics Simulator, which spanned a range of entrance diameters, wind speeds, gas temperatures, and vertical angles of incidence. To evaluate the model’s predictions, it was used to replicate, with great accuracy, a time series of carbon monoxide (CO) concentrations in a controlled experiment where a fabricated cylindrical cavity was exposed to combustion products. The time constant for cavity filling is proportional to the ratio of cavity volume to entrance area. Hot gases lead to significant stratification within the cavity during exposures. To demonstrate the model’s potential use in predicting faunal exposures in the context of land management, we show that the model can be used to estimate dosage within red-cockaded woodpecker (Picoides borealis Vieillot, 1809) cavities without requiring temporally detailed, local measurements of wind speed and combustion product concentrations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".