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Record W1977530236 · doi:10.1163/000579511x588812

The calling songs of male spring field crickets (Gryllus veletis) change as males age

2011· article· en· W1977530236 on OpenAlexafffund
Susan M. Bertram, Lauren P. Fitzsimmons

Bibliographic record

VenueBehaviour · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsField cricketCricketLongevityAttractionCourtshipZoologyBiologyDemographyPsychology

Abstract

fetched live from OpenAlex

Abstract Sexual traits are typically thought to convey information about a male's quality or condition. Female preference for older males has been documented in many taxa, but the evidence that males signal their age is inconclusive. We investigated lifetime patterns of acoustic mate attraction signalling in a longitudinal study of the spring field cricket, Gryllus veletis. We recorded males continuously throughout their lives, such that every pulse of sound produced by every male was analyzed. Our study answers two main questions: (1) Do calls change as males age? Our results reveal that the calls of male spring field crickets change with age; the calls of older males were quieter, with more silent periods within and between chirps, and produced less often than those of younger males. As males aged most of the changes in call structure reflect decreased calling effort. (2) What is the relationship between calling effort and longevity? Lifetime calling effort was positively related to longevity, such that males that called the most over their life also lived longer than males that called less. Together, our findings provide the most thorough exploration of lifetime signalling patterns in crickets to date.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.100
GPT teacher head0.257
Teacher spread0.157 · 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

Citations21
Published2011
Admission routes2
Has abstractyes

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