Fibre bundle element method of determining physiological cross-sectional area from three-dimensional computer muscle models created from digitised fibre bundle data
Bibliographic record
Abstract
Physiological cross-sectional area (PCSA) is used to compare force-producing capabilities of muscles. A limitation of PCSA is that it cannot be measured directly from a specimen, as there is usually no area within the muscle traversed by all fibres. Traditionally, a formula requiring averaged architectural parameters has been used. The purpose of this paper is to describe the development of a fibre bundle element (FBE) method to calculate PCSA from digitised fibre bundle data of five architecturally distinct muscles and compare the FBE and PCSA formula. An FBE method was developed that used a serially arranged set of cylinders as the volumetric representation of each fibre bundle, and PCSA was computed as the summation of the cross-sectional area of each FBE. Four of five muscles had significantly different PCSA between FBE and formula methods. The FBE method provides an approach that considers architectural variances while minimising the need for averaged architectural parameters.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".