Assumptions about the cross-sectional shape of skinned muscle fibers can distort the relationship between muscle force and cross-sectional area
Bibliographic record
Abstract
Comparisons of muscle force output are often performed after normalization to muscle physiological cross-sectional area (CSA). Differences in force per CSA (i.e., specific force) suggest the presence of physiological differences in contractile function. Permeabilized mammalian skeletal muscle fibers frequently exhibit substantial declines in specific force with increasing CSA, suggesting that smaller fibers are intrinsically stronger than larger fibers of the same group. However, the potential for CSA assessment error to account for CSA-dependent differences in specific force has not received adequate attention. Assessment of fiber CSA typically involves measurement of fiber width and perhaps also height, and CSA is calculated by assuming the cross sections are either circular or elliptical with major and minor axes aligned with the optical measurement system. Differences between the assumed and real cross-sectional shapes would cause variability in the ratio of assessed CSA (aCSA) to real CSA (rCSA). This variability can insidiously bias aCSA such that large aCSAs typically overstate rCSAs of the fibers they represent, and small aCSAs typically understate the rCSAs of the fibers they represent. As aCSA is the denominator for the specific force calculation, scatterplots of specific force versus aCSA would be expected to show declines in specific force as aCSA increases without a corresponding effect in a scatterplot of specific force versus rCSA. When comparing active and passive muscle forces between data subsets defined by aCSA, the impact of CSA assessment error should be considered before exploring other physiological mechanisms.
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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 | 0.020 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".