Adaptive value of ambling gaits in primates and other mammals
Bibliographic record
Abstract
At speeds between the walk and the gallop, most mammals trot. Primates almost never trot, and it has been claimed that they transition directly from a walk to a gallop without any distinctive mid-speed running gait. If true, this would be another characteristic difference between the locomotion of primates and that of most other quadrupedal mammals. Presently, however, few data exist concerning the actual presence or absence of intermediate-speed gaits (i.e. gaits that are used between a walk and a gallop) in primates. Video records of running in twelve primate species reveal that, unlike most other mammals, all the primates studied almost exclusively adopt an 'amble'--an intermediate-speed running gait with no whole-body aerial phase--rather than trot. Ambling is also common in elephants and some horses, raising the question of why ambling is preferred over trotting in these diverse groups of animals. Mathematical analyses presented here show that ambling ensures continuous contact of the body with the substrate while dramatically reducing vertical oscillations of the center of mass. This may explain why ambling appears to be preferable to trotting for extremely large terrestrial mammals such as elephants and for arboreal mammals like primates that move on unstable branches. These findings allow us to better understand the mechanics of these unusual running gaits and shed new light on primate locomotor evolution.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".