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Record W4309825784 · doi:10.1093/jmammal/gyac107

Temperature-associated morphological changes in an African arid-zone ground squirrel

2022· article· en· W4309825784 on OpenAlexafffund
Miyako H. Warrington, Jane M. Waterman

Bibliographic record

VenueJournal of Mammalogy · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of ManitobaNational Science Foundation
KeywordsBergmann's ruleAridThermoregulationEcologyClimate changeBiologyGeography

Abstract

fetched live from OpenAlex

Abstract The ecology, life histories, and physiology of many animals are changing in response to human-induced climate change. As the Earth warms, the ability of an animal to thermoregulate becomes ecologically and physiologically significant. Morphological adaptations to warmer temperatures include larger appendages and smaller bodies. We examined morphological features in a ground squirrel, Xerus inauris, living in the arid zones of South Africa, to examine whether squirrels have responded to increases in temperature and changes in seasonal rainfall with morphological modifications over the last 18 years. We found that over time, absolute hindfoot length and proportional hindfoot length increased, while spine length decreased. These changes are consistent with ecogeographical rules (Allen’s rule and Bergmann’s rule) and provide evidence in support of “shape-shifting” in response to climatic warming. Body mass also increased with time; however, these changes were not consistent with Bergmann’s rule, indicating that mass is influenced by other ecological factors (e.g., resource availability). Our study adds to the growing evidence that animal morphologies are changing in response to changing climatic conditions, although it remains to be seen whether these changes are adaptive.

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)0.001
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 score0.057
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.257
Teacher spread0.234 · 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

Citations13
Published2022
Admission routes2
Has abstractyes

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