Grasping performance in primates does not align with preferred substrate use
Bibliographic record
Abstract
Arboreal locomotion presents considerable mechanical challenges, requiring animals to maintain stability on narrow supports. While some species rely on gait adjustments, others use grasping autopodia to counteract toppling torques. We investigated how substrate size affects grasping force in strepsirrhine primates—a lineage regarded as a model for early primates and known for fine-branch arboreal locomotion. Using a custom apparatus, we measured in vivo grip strength across three substrate diameters (small, medium and large) in 11 species. In both hands and feet, grip strength peaked on medium-sized substrates—those allowing optimal digital wrapping—and declined on small and large diameters. These patterns remained significant after controlling for phylogeny, body size, sex and age. Despite weaker performance on small substrates, strepsirrhines commonly navigate thin terminal branches in nature, suggesting an ecological mismatch between peak grasping performance and substrate use. This implies that powerful digital grasping may be less critical for arboreal stability than often assumed. Instead, whole-body mechanics and precise limb placement likely compensate when grip is reduced. Rather than maximizing force, the primate hand appears adapted for versatility—supporting the broader principle that evolutionary success often reflects functional adequacy and adaptability over specialization for force production.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".