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Record W3204235183 · doi:10.3390/f12101347

Eastern Red Bat Responses to Fire during Winter Torpor

2021· article· en· W3204235183 on OpenAlexaff
Jason Thomas Layne, Dana Green, Anna A. Scesny, Lynn W. Robbins

Bibliographic record

VenueForests · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsTorporSmokeEnvironmental scienceEcologyPrescribed burnForagingHabitatGeographyBiologyMeteorologyThermoregulation

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.234
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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