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Record W1975782471 · doi:10.1086/505161

Gaping Displays Reveal and Amplify a Mechanically Based Index of Weapon Performance

2006· article· en· W1975782471 on OpenAlexaff
A. Kristopher Lappin, Yoni Brandt, Jerry F. Husak, Joseph M. Macedonia, Darrell J. Kemp

Bibliographic record

VenueThe American Naturalist · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsUniversity of Toronto
FundersU.S. Fish and Wildlife Service
KeywordsBite force quotientBiologyDominance (genetics)Adductor musclesIndex (typography)ZoologyAnatomyEcologyComputer science

Abstract

fetched live from OpenAlex

Physical prowess, a key determinant of fight outcomes, is contingent on whole-organism performance traits. The advertisement of performance, via display, is poorly understood because it is unclear how information about performance is encoded into display characteristics. Previous studies have shown that weapon performance (i.e., bite force) predicts dominance and reproductive success in male lizards. We tested the hypothesis that gaping displays by adult male collared lizards (Crotaphytus) can provide an index of weapon performance by exposing the major jaw-adductor muscle complex and that white patches at the mouth corners amplify this index. For territorial adult males, the breadth of the muscle complex, which is not correlated with body size, was a strong predictor of bite force. For nonterritorial yearling males and females, however, measures of body and head size predicted bite force. The patches are highly conspicuous, exhibit UV-reflecting properties within the visual range of lizards, and provide size-independent information about bite force only in adult males. We conclude that exposure of the muscle complex during gaping displays can provide rival males with a reliable, body-size independent, biomechanically based index of weapon performance, an index that the mouth-corner patches amplify. Indexes that transmit information through mechanistic links to performance are expected to be widespread among animals.

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 score0.516
Threshold uncertainty score0.398

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 studies0.0000.001
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 head0.005
GPT teacher head0.209
Teacher spread0.204 · 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

Citations102
Published2006
Admission routes1
Has abstractyes

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