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Record W3046511888 · doi:10.1111/jzo.12810

Not playing by the rules: mixed support of ecogeographic rules in an arid‐adapted African ground squirrel

2020· article· en· W3046511888 on OpenAlexafffund
Lauren LaFlèche, Jane M. Waterman

Bibliographic record

VenueJournal of Zoology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSexual dimorphismBiologyResource (disambiguation)AridEcologyThermoregulationLimitingDegree (music)Zoology

Abstract

fetched live from OpenAlex

Abstract Body size impacts nearly all aspects of an animal's life and can be subject to considerable variation in response to differences in climate and resource availability. We studied the impact of temperature and resource availability on arid‐living Cape ground squirrels ( Xerus inauris ), relative to body size and degree of sexual dimorphism, over three locations differing in annual rainfall and temperature to test Bergmann's rule, the resource rule and Rensch's rule. We found that squirrels were the largest where resources were the highest and temperatures were the lowest but squirrels were similar in mass where resources were medium or low and temperatures medium and highest, partially supporting Bergmann's rule and the resource rule. Hind‐feet were the largest where resources were medium and temperatures were medium and similarly small in low and high resource and high‐ and low‐temperature areas. The degree of sexual dimorphism of hind‐foot and mass was constant no matter the resource abundance or temperature, suggesting no impact of resource availability or temperature on the degree of sexual dimorphism, contradicting Rensch's rule. Our findings suggest that, while resource availability and temperature may have an impact on body size, other mechanisms may be important in limiting both body size and degree of sexual dimorphism in this species.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.754

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)0.0010.000

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 head0.030
GPT teacher head0.255
Teacher spread0.225 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

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