Control of Frontal Plane Motion of the Hindlimbs in the Unrestrained Walking Cat
Bibliographic record
Abstract
This study describes the patterns of activity of hip abductor and adductor muscles and relates their activity to the frontal plane motions of the hindlimbs during unrestrained walking in the cat to provide insight into the function of these muscles in maintaining stability during walking. Electromyographic activity was recorded from hindlimb muscles while cats walked across a walkway. Four video cameras were used to record the movement of the animal in three dimensions. To further delineate the role of the hip abductors and adductors in regulating frontal plane movements of the legs, medial-lateral translations of the walking surface were periodically introduced. During walking, the hip abducts throughout much of the stance phase and adducts during swing. Normally, the abductors and adductors are co-active during much of the stance phase and are quiescent during swing. Consequently, the adduction observed during swing is likely the result of passive events. It is argued that the activity of the hip abductors during stance phase plays a prominent role in regulating frontal plane motion of the legs during walking. However, when medial-lateral stability is disturbed, both the hip abductors and adductors respond to stabilize the frontal plane motion of the body mass while also adjusting the frontal plane swing trajectory and subsequent paw placement. The balance corrective reactions observed in the cat after medial-lateral perturbations of the support surface reasonably approximate the reactions observed previously in humans, indicating that the cat is a reasonable model to explore the neural mechanisms of lateral stability during walking.
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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".