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Record W2501045576 · doi:10.1093/jmammal/gyw119

Sex differences in spring migration timing and body composition of silver-haired bats<i>Lasionycteris noctivagans</i>

2016· article· en· W2501045576 on OpenAlexafffund
Kristin A. Jonasson, Christopher G. Guglielmo

Bibliographic record

VenueJournal of Mammalogy · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationMinistry of Natural ResourcesBat Conservation International
KeywordsBiologyPhenologyPopulationMatingReproductionEcologyDemographyReproductive successZoology

Abstract

fetched live from OpenAlex

Although the phenology of bat migration has been investigated at the population level, the timing and energy management of individual bats is poorly understood. Early arrival on the summering grounds with ample energy stores may give a fitness advantage to females preparing to raise pups. In contrast, there is no such fitness gain for males because they invest in mating during autumn. We use 3 years of capture data to investigate sex differences in spring migration passage date and body composition of Lasionycteris noctivagans. We predicted that females would arrive earlier in the spring and maintain greater fat stores than males. Females passed through the study site earlier and had more fat than males in 2 of 3 study years. Cold weather appeared to delay female migration and to deplete fat stores but did not appear to affect the passage date or fat stores of males. Our findings indicate that sex differences occur in the timing and energy management decisions of bats during spring migration. We postulate this difference in migration strategy is related to the increased demands of reproduction once females arrive at their summering grounds. Our results also suggest that females’ fuel migration with energy acquired en route to a greater extent than males.

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 score0.481
Threshold uncertainty score0.091

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)0.0000.000

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 head0.022
GPT teacher head0.211
Teacher spread0.189 · 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

Citations29
Published2016
Admission routes2
Has abstractyes

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