Limb anatomy influences swing duration and angular velocity: Implications for understanding primate locomotor adaptations
Bibliographic record
Abstract
To understand the functional correlates ofdiversity in limb length, mass, and weightdistribution among mammals, mostbiomechanical studies have focused on stancephase mechanics, while swing phase hasremained relatively understudied. Previousstudies have shown that as animals move fasterthe stance period shortens while the swing periodstays relatively constant. This suggests thatmechanical qualities, such as limb length andmass distribution, constrain swing timing andcan influence animal velocity and energeticcosts. Primates–with relatively more distalweight distribution associated with prehensilehands and feet–may experience longer swingperiods compared to other mammals. We testedthis hypothesis by calculating swing period fromvideorecords for a wide range of mammals,including humans, dogs, cats, kinkajous, coatis,lemurs, squirrel monkeys and callitrichids. Inevery species in our sample stance durationdecreases with increasing speed and swingduration remains nearly constant. When absoluteswing durations are compared, most species wereidentical, although dogs and marmosets showedsignificantly shorter absolute swing durationsthan other mammals. This similarity in swingperiod (in spite of differences in limb length)leads to differences in angular velocity, and thus,muscular effort needed to accelerate anddecelerate the limb. Although relatively longerlimbs and grasping cheiridia may providebenefits for increasing stride length and stability,such anatomy may also constrain speed,influence speeds at which gait transitions occur,and increase costs of locomotion. Understandingthe relative costs and benefits of different limbanatomies allows a better understanding ofselective pressures driving morphologicalevolution in primates.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".