Eastern Red Bat Responses to Fire during Winter Torpor
Bibliographic record
Abstract
Prescribed fires are a forest management tool used to improve natural areas for a variety of benefits including increased plant diversity, reduced competition for desired species, decreased fuel loads, and improved wildlife habitat. The post-fire results in landscapes have shown positive benefits for bat populations. However, prescribed fires set in the winter may cause direct mortality of eastern red bat (Lasiurus borealis) populations that use leaf litter for roosting during periods of colder (<10 °C) temperatures. Therefore, we used controlled laboratory techniques to explore if eastern red bats arouse from torpor when exposed to cues associated with fire (i.e., smoke and the sound of fire). Through subsequent field trials, we confirmed latencies of first response (i.e., movement or increased respiration), arousal, and flight behaviors to the stimuli of fire. We provide evidence of smoke influencing eastern red bat first response and arousal through laboratory and field trial results. Latencies of all behaviors were negatively correlated with temperatures and wind speeds prior to and during field trials. We recommend prescribing winter fires on days when temperatures are >10 °C to provide eastern red bats with a better chance to passively rewarm and react to an approaching fire.
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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 | 0.000 |
| 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 | 0.000 |
| 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".