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Increase in song frequency decreases spermatophore size: correlative evidence of a macroevolutionary trade‐off in katydids (Orthoptera: Tettigoniidae)

2007· article· en· W2067915795 on OpenAlexafffund
Raúl Cueva del Castillo, Darryl Gwynne

Bibliographic record

VenueJournal of Evolutionary Biology · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsToronto ZooUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsTettigoniidaeSpermatophoreBiologyOrthopteraSexual selectionSexual dimorphismZoologyEvolutionary biologyEcologyMating

Abstract

fetched live from OpenAlex

In many katydids, the male feeds his mate with a large gelatinous spermatophore. Males of most species also produce elaborate calling songs. We predicted a negative relationship between spermatophore size and call frequency because of trade-offs between these two costly traits. Our comparative analysis controlling phylogeny and body size supported this prediction. Although call frequency is expected to decrease with increasing body size, after controlling for phylogeny, both variables were not related. Finally, given that song frequency and spermatophore size are likely targets of sexual selection, we examined the relationship between these variables and sexual size dimorphism (SSD) which can be influenced by sexual selection on body size. We found that only female body size was positively related to SSD, suggesting that natural and/or sexual selection on female body size may be stronger than sexual selection on male and spermatophore size.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.019
GPT teacher head0.270
Teacher spread0.251 · 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

Citations39
Published2007
Admission routes2
Has abstractyes

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