Effects of body size and mass on running speed of male yellow-pine chipmunks (<i>Tamias amoenus</i>)
Bibliographic record
Abstract
Male-biased sexual size dimorphism in mammals is usually attributed to the success of large males in intrasexual combat for mates. However, mating success may be determined by contests that are not combative. In the mating chase of the yellow-pine chipmunk (Tamias amoenus), a mammalian species with female-biased sexual size dimorphism, fast males may have an advantage in acquiring matings with estrous females. However, the effects of intraspecific variation in body size on running speed are not obvious; heavy individuals may run more slowly than light individuals because excess mass can be a hindrance to locomotion, but individuals that are structurally large may run faster than small individuals because of longer stride length. We examined the effects of both body mass and structural size on running speed in male yellow-pine chipmunks using manipulated runs in which male chipmunks were chased over a known distance. Structurally large male chipmunks had faster running speeds than small males, potentially giving large males an advantage when chasing estrous females. However, small male chipmunks are known to be aggressively dominant over large males. This leads to a potential trade-off in male body size between two behavioural components of mating success (running speed and dominance) that may constrain the evolution of male body size, ultimately leading to female-biased sexual size dimorphism.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".