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Record W4391897323 · doi:10.1093/jmammal/gyad128

Reproductive state alters vocal characteristics of female North American red squirrels (<i>Tamiasciurus hudsonicus</i>)

2024· article· en· W4391897323 on OpenAlexafffund
Alexander J. Hare, Andrew G. McAdam, Ben Dantzer, Jeffrey E. Lane, Stan Boutin, Amy E. M. Newman

Bibliographic record

VenueJournal of Mammalogy · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsUniversity of AlbertaUniversity of SaskatchewanUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsBiologyGeographyEcology

Abstract

fetched live from OpenAlex

Abstract Female advertisement of reproductive state and receptivity has the potential to play a large role in the mating systems of many taxa, but investigations of this phenomenon are underrepresented in the literature. North American red squirrels (Tamiasciurus hudsonicus) are highly territorial and engage in scramble competition mating, with males converging from spatially disparate territories to engage in mating chases. Given the narrow estrus window exhibited in this species, the ubiquitous use of vocalizations to advertise territory ownership, and the high synchronicity of males arriving from distant territories, we hypothesized that female vocalizations contain cues relating to their estrous state. To test this hypothesis, we examined the spectral and temporal properties of female territorial rattle vocalizations collected from females of known reproductive condition over 3 years. While we found no distinct changes associated with estrus specifically, we did identify significant changes in the spectral characteristics of rattles relating to both female body mass and reproductive state relative to parturition. To the best of our knowledge, this is the first evidence of changes in vocal characteristics associated with late pregnancy in a nonhuman mammal.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score0.460

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 head0.016
GPT teacher head0.285
Teacher spread0.268 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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