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Record W4415197852 · doi:10.1093/evolut/qpaf215

Can the form of sexual selection explain patterns of static weapon allometry expressed by alternative mating morphotypes?

2025· article· en· W4415197852 on OpenAlexafffund
Clint D. Kelly

Bibliographic record

VenueEvolution · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSexual selectionTraitAllometryMatingSelection (genetic algorithm)Context (archaeology)Sexual dimorphismMating system

Abstract

fetched live from OpenAlex

The regime of selection acting on a trait is expected to shape its static allometry. Few studies, however, have quantified the form of sexual selection acting on a trait in the wild to test whether the trait allometrically scales as predicted. Even fewer studies have tested these predictions using males expressing weapon polymorphism as part of their alternative mating strategies. Here, I use field data to test how sexual selection shapes scaling allometries of male weaponry in the Wellington tree wētā (H. crassidens), a male-trimorphic and harem-polygynous insect endemic to New Zealand. Contrary to the prediction that 10th instar males' large weaponry would scale hyperallometrically because it is under direct sexual selection, I found that 10th instar weaponry is not subject to direct sexual selection and scales hypoallometrically. Similarly, neither 8th nor 9th instar male weaponry experiences direct sexual selection, and their weaponry scales hyperallometrically and hypoallometrically, respectively. My study suggests that disentangling competing hypotheses for the evolution of scaling patterns of sexually selected traits must go beyond a simple viability-sexual selection dichotomy by also considering weapon function and the ecological context within which the weapon is used.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.217
Teacher spread0.198 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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