Bibliographic record
Abstract
Various aspects of performance (e.g., sprint speed, grip strength) are thought to be important determinants of the success of animals in natural activities such as foraging, mating, and escaping from predators. However, it is generally known that morphological properties enhancing one type of performance (e.g., speed) can lead to a reduction in another (e.g., strength). Such performance trade-offs have been quantified at the inter-specific level, but evidence at the inter-individual level remains equivocal. To test for the presence of a performance trade-off, I initiated a study on wild white-footed mice (Peromyscus leucopus). In summer 2016, I captured a total of 186 individuals, 87 of which were repeatedly phenotyped for grip strength and sprint speed. A significant positive relationship was found between body mass and grip strength (but not for sprint speed). Individual differences in performance were repeatable through time for both grip strength and sprint speed. Using a bivariate mixed model, I detected a significant negative correlation between grip strength and sprint speed at the among-individual level. By contrast, the within-individual correlation between grip strength and sprint speed tended to be positive, suggesting that some unquantified aspects of the mouse phenotype (e.g., body condition, age) may have a positive effect on both performance traits. Given the relatively low repeatability of grip strength and sprint speed, a failure to properly partition the correlation at the among- and within-individual level generates a counter-intuitive, positive correlation. This study is one the first to detect a performance trade-off at the among-individual level in a wild animal population.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".