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Record W2531003434 · doi:10.1139/cjz-2016-0055

Environmental correlates and energetics of winter flight by bats in southern Alberta, Canada

2016· article· en· W2531003434 on OpenAlexafffundvenueabout
Brandon J. Klüg-Baerwald, L.E. Gower, Cori L. Lausen, R. Mark Brigham

Bibliographic record

VenueCanadian Journal of Zoology · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsWildlife Conservation Society CanadaUniversity of Regina
FundersAlberta Environment and Parks
KeywordsEptesicus fuscusMyotis lucifugusHuman echolocationBiologyEnergeticsForagingEcologyZoology

Abstract

fetched live from OpenAlex

Winter activity of bats is common, yet poorly understood. Other studies suggest a relationship between winter activity and ambient temperature, particularly temperature at sunset. We recorded echolocation calls to determine correlates of hourly bat activity in Dinosaur Provincial Park, Alberta, Canada. We documented bat activity in temperatures as low as −10.4 °C. We observed big brown bats (Eptesicus fuscus (Palisot de Beauvois, 1796)) flying at colder temperatures than species of Myotis bats (genus Myotis Kaup, 1829). We show that temperature and wind are important predictors of winter activity by E. fuscus and Myotis, and that Myotis may also use changes in barometric pressure to cue activity. In the absence of foraging opportunity, we suggest these environmental factors relate to heat loss and thus the energetic cost of flight. To understand the energetic consequences of bat flight in cold temperatures, we estimated energy expenditure during winter flights of E. fuscus and little brown myotis (Myotis lucifugus (Le Conte, 1831)) using species-specific parameters. We estimated that winter flight uses considerable fat stores and that flight thermogenesis could mitigate energetic costs by 20% or more. We also show that temperature-dependent interspecific differences in winter activity likely stem from differences between species in heat loss and potential for activity–thermoregulatory heat substitution.

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 score0.547
Threshold uncertainty score0.603

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 head0.004
GPT teacher head0.142
Teacher spread0.138 · 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

Citations45
Published2016
Admission routes4
Has abstractyes

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