Limb anatomy influences swing duration and angular velocity: Implications for understanding primate locomotor adaptations
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 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) | 0.000 | 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 it