Human <i>in vivo</i> medial gastrocnemius gear during active and passive muscle lengthening: effect of inconsistent methods and nomenclature on data interpretation
Bibliographic record
Abstract
'Muscle gear' is calculated as the ratio of fascicle-to-muscle length change, strain, or velocity. Inconsistencies in nomenclature and definitions of gear exist across disciplines partly due to differences in fascicle [curved (Lf) versus linear (Lf,straight)] and muscle [whole-muscle belly (Lb) versus belly segment (Lb,segment)] length calculation methods. We tested whether these differences affect gear magnitude during passive and active muscle lengthening of human medial gastrocnemius of young men (n=13, 26.3±5.0 years) using an isokinetic dynamometer. Lb, Lb,segment, Lf and Lf,straight were measured from motion analysis and ultrasound imaging data. Downshifts in belly gear but not belly segment gear occurred with muscle lengthening only during active lengthening. Muscle gear was unaffected by fascicle length measurement method (P=0.18) but differed when calculated as changes in Lb or Lb,segment (P<0.01) in a length-dependent manner. Caution is therefore advised for the use and interpretation of different muscle gear calculation methods and nomenclatures in animal and human comparative physiology.
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 | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".