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Record W6910691937 · doi:10.5061/dryad.12jm63xwg

Interspecific variation in evaporative water loss and temperature response, but not metabolic rate, among hibernating bats

2021· dataset· en· W6910691937 on OpenAlexaff

Bibliographic record

VenueDRYAD · 2021
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of ReginaWildlife Conservation Society CanadaUniversity of WinnipegUniversity of Waterloo
Fundersnot available
KeywordsHibernation (computing)Interspecific competitionRange (aeronautics)PopulationTorpor

Abstract

fetched live from OpenAlex

Hibernation is widespread among mammals in a variety of environmental contexts. However, few experimental studies consider interspecific comparisons, and for many unstudied (or understudied) species we must assume the underlying physiology of hibernation is comparable to the relatively few species that have been studied in detail. Studies of interspecific variation provide insight into general patterns of hibernation strategies. We studied 13 species of free-living bats, including populations spread over thousands of kilometers and diverse habitats. We measured torpid metabolic rate and evaporative water loss (two key parameters for understanding hibernation energetics) across a range of temperatures. Response to ambient temperature varied among species, but all species achieved similar minimum torpid metabolic rate. Conversely, evaporative water loss varied among species and our results suggest two general hibernation strategies in North American bats, representing high and low evaporative water loss groups. Notably, species that have suffered population declines due to white-nose syndrome fall in the high evaporative water loss group and less affected species in the low evaporative water loss group. Documenting general patterns of physiological diversity, and associated ecological implications, contributes to broader understanding of biodiversity, and may help predict which species are at greater risk of environmental and anthropogenic stressors.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.328
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.013
GPT teacher head0.262
Teacher spread0.248 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2021
Admission routes1
Has abstractyes

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