Forest terrains influence walking kinematics among indigenous Tsimane of the Bolivian Amazon
Bibliographic record
Abstract
Laboratory-based studies indicate that a major evolutionary advantage of bipedalism is enabling humans to walk with relatively low energy expenditure. However, such studies typically record subjects walking on even surfaces or treadmills that do not represent the irregular terrains our species encounters in natural environments. To date, few studies have quantified walking kinematics on natural terrains. Here we used high-speed video to record marker-based kinematics of 21 individuals from a Tsimane forager-horticulturalist community in the Bolivian Amazon walking on three different terrains: a dirt field, a forest trail and an unbroken forest transect. Compared with the field, in the unbroken forest participants contacted the ground with more protracted legs and flatter foot postures, had more inclined trunks, more flexed hips and knees, and raised their feet higher during leg swing. In contrast, kinematics were generally similar between trail and field walking. These results provide preliminary support for the idea that irregular natural surfaces like those in forests cause humans to alter their walking kinematics, such that travel in these environments could be more energetically expensive than would be assumed from laboratory-based data. These findings have important implications for the evolutionary energetics of human foraging in environments with challenging terrains.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".