Premature deactivation of soleus during the propulsive phase of cat jumping
Bibliographic record
Abstract
It has been shown that cat soleus (SOL) forces remain nearly constant despite increases in electromyography (EMG) activity for increasing speeds of locomotion, while medial gastrocnemius (MG) forces and EMG activity increase in parallel. Furthermore, during jumping, average cat SOL forces decrease, while average EMG activity increases dramatically compared with walking conditions. Finally, during rapid paw-shake movements, SOL forces and EMG activities are nearly zero. Based on these results, we hypothesized that the SOL is deactivated, despite ankle extensor requirements, if the contractile conditions limit SOL force potential severely. The purposes of this study were to (i) investigate SOL EMG activity and force as a function of its contractile conditions during jumping, (ii) test whether SOL EMG activity is associated with SOL contractile conditions, and (iii) determine the functional implications of SOL EMG activity during jumping. It was found that the SOL was prematurely deactivated in two distinct phases during the propulsive phase of jumping, in which shortening speeds approached or even exceeded the maximal speed of muscle shortening. We concluded that the SOL was prematurely deactivated to save energy because its mechanical work output approached zero, and speculated that the first phase of deactivation might be caused by a decrease in group Ia firing associated with active shortening and the second by a pre-programmed response inherent to the central pattern generator.
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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".