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Record W2108911491 · doi:10.1098/rspb.2010.0467

Oestrous red deer hinds prefer male roars with higher fundamental frequencies

2010· article· en· W2108911491 on OpenAlexaff
David Reby, Benjamin D. Charlton, Yann Locatelli, Karen McComb

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsEspace pour la vie
Fundersnot available
KeywordsBiologySexual selectionMatingEstrous cycleAdult maleOffspringDemographyZoologyAnimal scienceEndocrinologyGenetics

Abstract

fetched live from OpenAlex

Across vertebrates, the observation that lower-pitched vocalizations are typically associated with larger and/or higher quality males has lead to the widespread belief that inter- and intra-sexual selection will produce male calls with low fundamental frequencies (F0). Here we investigated the response of oestrous red deer hinds to playback of re-synthesized male roars characterized by either higher than average or lower than average F0. We found that hinds prefer higher rather than lower 'pitched' roars, providing, to our knowledge, the first evidence of such a bias in nonhuman mammals. Our findings can be interpreted in relation to previous observations that the minimum F0 of roars is positively correlated with male reproductive success in free-ranging red deer stags, and that across Cervids the F0 of male mating calls shows extreme variability. Females showing preferences for higher-pitched roars might derive genetic benefits through more competitive male offspring. Our results emphasize the need for further investigations of female preferences in mammals in order to better understand the extreme variation of F0 values observed in male sexual calls.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 head0.031
GPT teacher head0.259
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), 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

Citations62
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Royal Society B Biological SciencesSame topicAnimal Vocal Communication and BehaviorFrench-language works237,207