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Record W2137759134 · doi:10.1139/z08-146

Influence of weather on two insectivorous bats in a temperate Pacific Northwest rainforest

2009· article· en· W2137759134 on OpenAlexafffundvenueabout
D. W. Burles, R. Mark Brigham, Richard A. Ring, T. E. Reimchen

Bibliographic record

VenueCanadian Journal of Zoology · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsUniversity of ReginaUniversity of VictoriaParks Canada
FundersCanadian Forest Service
KeywordsInsectivoreBiologyEcologyPredationMyotis lucifugusReproductive successTemperate climateNocturnalPhenologyZoologyPopulationDemography

Abstract

fetched live from OpenAlex

Adverse weather conditions frequently have a significant negative influence on survival and reproductive success of insectivorous bats. Low ambient temperatures increase the energetic costs of maintaining euthermia and reduces insect activity, while precipitation likely adds “clutter” making prey more difficult to detect using echolocation. We studied two species of insectivorous bats, Myotis lucifugus (LeConte, 1831) and Myotis keenii (Merriam, 1895), in the Pacific Northwest of Canada, a region that experiences frequent cool, wet weather during spring and summer. Our study took place during the El Niño – La Niña cycle of 1998–1999, which resulted in contrasting years. The summer of 1998 was unusually warm and dry, while the summer of 1999 was unusually cool and wet. We predicted that both species would be adversely affected by the cool, wet conditions of 1999, resulting in prolonged gestation, late fledging of young, and lower reproductive success. However, this was not the case. Myotis lucifugus did experience delays in reproductive timing and lower reproductive success in 1999, as predicted, whereas M. keenii experienced much shorter gestations, earlier fledgings, and no difference in reproductive success between years. We hypothesize that the ability of M. keenii to glean prey enables it to better cope with cool, wet conditions.

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 score0.664
Threshold uncertainty score0.999

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 head0.010
GPT teacher head0.201
Teacher spread0.191 · 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

Citations82
Published2009
Admission routes4
Has abstractyes

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