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Record W2154528772 · doi:10.1139/z08-002

Effects of food restriction on growth, energy allocation, and sexual size dimorphism in Yarrow’s Spiny Lizard, <i>Sceloporus jarrovii</i>

2008· article· en· W2154528772 on OpenAlexvenueno aff
Robert M. Cox, Michele Barrett, Henry B. John‐Alder

Bibliographic record

VenueCanadian Journal of Zoology · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsBiologySexual dimorphismSexual selectionLizardCricketZoologyEcology

Abstract

fetched live from OpenAlex

Evolutionary biologists often view sexual size dimorphism (SSD) as a fixed genetic consequence of sexually antagonistic selection, but the actual magnitude of SSD may often be strongly dependent upon proximate environmental factors. Sexual differences in growth rate lead to male-biased SSD in wild populations of Yarrow’s Spiny Lizard ( Sceloporus jarrovii Cope, 1875), yet both sexes grow at similar rates under controlled laboratory conditions. We hypothesized that male-biased SSD in S. jarrovii reflects an obligatory sexual difference in energy allocation to growth versus competing functions, but that an ad libitum diet provides an energy surplus which overwhelms this sex-specific energetic trade-off. To test this hypothesis, we reared juveniles under high (3 crickets/d) and low (1 cricket/d) food availabilities. Food restriction dramatically reduced growth in both sexes but did not differentially affect growth of females relative to males. Food consumption did not differ between sexes, but males grew slightly faster than females at both levels of food availability, indicating a greater fractional allocation of available energy to growth. By contrast, females had larger fat bodies than did males, particularly under food restriction. This sexual difference in energy allocation to storage could explain the slightly higher growth rate of males relative to females.

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 score0.522
Threshold uncertainty score0.996

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 studies0.0000.000
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 head0.012
GPT teacher head0.180
Teacher spread0.168 · 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

Citations32
Published2008
Admission routes1
Has abstractyes

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