Effects of wildland fire smoke on a tree-roosting bat: integrating a plume model, field measurements, and mammalian dose–response relationships
Bibliographic record
Abstract
Faunal injury and mortality in wildland fires is a concern for wildlife and fire management although little work has been done on the mechanisms by which exposures cause their effects. In this paper, we use an integral plume model, field measurements, and models of carbon monoxide and heat effects to explore risk to tree-roosting bats during prescribed fires in mixed-oak forests of southeastern Ohio and eastern Kentucky. Tree-roosting bats are of interest primarily because of the need to mitigate risks for the endangered Indiana bat ( Myotis sodalis ), our focal species. Blood carboxyhemoglobin concentrations predicted from carbon monoxide data supplemented by model output only approached critical levels just above flames in the most intense fires. By contrast, an ear-heating model driven by plume model output suggested that injury to the bat’s thermally thin ears would occur up to heights similar to those of foliage necrosis, an effect for which predictive relationships exist. Risks of heat injury increase with fireline intensity and decrease with both roost height and ambient wind. Although more information is needed on bat arousal from torpor and behavior during fires, strategies for reducing the risk of heat injury emerge from consideration of the underlying causal processes.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".