Not playing by the rules: mixed support of ecogeographic rules in an arid‐adapted African ground squirrel
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".