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Record W3049221567 · doi:10.1111/jzo.12820

Do female frogs have higher resting metabolic rates than males? A case study with <i>Xenopus allofraseri</i>

2020· article· en· W3049221567 on OpenAlexaff
Valérie Ducret, Mathieu Videlier, C. Moureaux, Camille Bonneaud, Anthony Herrel

Bibliographic record

VenueJournal of Zoology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsUniversity of Ottawa
FundersAgence Nationale de la Recherche
KeywordsBiologyRespirometryBasal metabolic rateSexual dimorphismReproductionMatingEcologyZoologyInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Abstract The energetic costs of body maintenance can have a profound influence on the energy that an individual can allocate to other functions such as growth, locomotion, or reproduction. Therefore, resting metabolism can ultimately affect an individual's survival or reproductive success, especially when food is limited. Although males and females often differ in their body composition (e.g. sex organs and fat accumulation) and body size, the occurrence and direction of sexual dimorphism in resting metabolism remains poorly understood in anurans. In the present study, we investigated whether males and females of the false Fraser's clawed frog Xenopus allofraseri differ in their resting metabolic rates. We used an open‐flow push‐through respirometry system to measure the volume of carbon dioxide (VCO 2 ) produced by animals at rest. Variation in was explained by ambient temperature, body size, and sex. For a similar body size, our results revealed that females had about a 50% higher resting metabolic rate (RMR) than males. We suggest that the enhanced investment in gamete production in females compared to males may explain this difference. We further suggest that a lower RMR in males compared to females at similar body size could be selectively advantageous as unallocated energy may be devoted to costly mating, in support of the ‘compensation’ hypothesis.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score1.000

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)0.0010.000

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 head0.028
GPT teacher head0.265
Teacher spread0.237 · 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.

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

Citations7
Published2020
Admission routes1
Has abstractyes

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