A Comparison of the Force‐Velocity Relationship of Bonobo and Human Muscle Fibers
Bibliographic record
Abstract
It has been reported that the muscles of chimpanzees and bonobos have "super strength" and it has been suggested that this is attributable to a larger specific tension and specific power of their muscles. To investigate this we compared the force-velocity relationship in 85 human and 49 bonobo (Pan paniscus) skinned fibers at 15°C. Fibers were classified as type I or II with gel electrophoresis. Type II fibers had a higher maximal shortening velocity (Vmax) and lower curvature of the force-velocity relationship (higher a/Po) than type I fibers in both species (p < 0.001). Although bonobo fibers of both types were larger and produced more force than human fibers, their specific tension and Vmax were lower (p < 0.001). The a/Po was higher in bonobo fibers (p < 0.001). Combined these differences in the parameters of the force-velocity relationship resulted in a similar specific power in bonobo and human fibers. The lesser curvature of the force-velocity relationship offsets the negative effects of a lower specific tension and Vmax on specific power of bonobo muscle fibers. The "super strength" of bonobos cannot be explained by differences in muscle fiber contractile properties but may reflect a higher proportion of type II fibers than in human muscle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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".