In vivo muscle force and muscle power during near-maximal frog jumps
Bibliographic record
Abstract
Frogs' outstanding jumping ability has been associated with a high power output from the leg extensor muscles. Two main theories have emerged to explain the high power output of the frog leg extensor muscles, either (i) the contractile conditions of all leg extensor muscles are optimized in terms of muscle length and speed of shortening, or (ii) maximal power is achieved through a dynamic catch mechanism that uncouples fibre shortening from the corresponding muscle-tendon unit shortening. As in vivo instantaneous power generation in frog hind limb muscles during jumping has never been measured directly, it is hard to distinguish between the two theories. In this study, we determined the instantaneous variable power output of the plantaris longus (PL) of Lithobates pipiens (also known as Rana pipiens), by directly measuring the in vivo force, length change, and speed of muscle and fibre shortening in near maximal jumps. Fifteen near maximal jumps (> 50cm in horizontal distance) were analyzed. High instantaneous peak power in PL (536 ± 47 W/kg) was achieved by optimizing the contractile conditions in terms of the force-length but not the force-velocity relationship, and by a dynamic catch mechanism that decouples fascicle shortening from muscle-tendon unit shortening. We also found that the extra-muscular free tendon likely amplifies the peak power output of the PL by modulating fascicle shortening length and shortening velocity for optimum power output, but not by releasing stored energy through recoiling as the tendon only started recoiling after peak PL power had been achieved.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".