Impact of hindlimb length variation on jumping dynamics in the Longshanks mouse
Bibliographic record
Abstract
Distantly related mammals (e.g. jerboa, tarsiers, kangaroos) have convergently evolved elongated hindlimbs relative to body size. Limb elongation is hypothesized to make these species more effective jumpers by increasing their kinetic energy output (through greater forces or acceleration distances), thereby increasing take-off velocity and jump distance. This hypothesis, however, has rarely been tested at the population level, where natural selection operates. We examined the relationship between limb length, muscular traits and dynamics using Longshanks mice, which were selectively bred over 22 generations for longer tibiae. Longshanks mice have approximately 15% longer tibiae and 10% longer femora compared with random-bred Control mice from the same genetic background. We collected in vivo measures of locomotor kinematics and force production, in combination with behavioral data and muscle morphology, to examine how changes in bone and muscle structure observed in Longshanks mice affect their hindlimb dynamics during jumping and clambering. Longshanks mice achieved higher mean and maximum lunge-jump heights than Control mice. When jumping to a standardized height (14 cm), Longshanks mice had lower maximum ground reaction forces, prolonged contact times and greater impulses, without significant differences in average force, power or whole-body velocity. While Longshanks mice have longer plantarflexor muscle bodies and tendons than Control mice, there were no consistent differences in muscular cross-sectional area or overall muscle volume; improved lunge-jumping performance in Longshanks mice is not accomplished by simply possessing larger muscles. Independent of other morphological or behavioral changes, our results point to the benefit of longer hindlimbs for performing dynamic locomotion.
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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 | 0.001 | 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 | 0.001 |
| 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".