Bibliographic record
Abstract
The modelling of skeletal muscle raises a number of philosophical questions, particularly in the realm of the relationship between different possible levels of representation and explanation. After a brief incursion into this area, a list of desiderata is proposed as a guiding principle for the construction of a viable model, including: comprehensiveness, soundness, experimental consistency, predictive ability and refinability. Each of these principles is illustrated by means of simple examples. The presence of internal constraints, such as incompressibility, may lead to counterintuitive results. A one-panel example is exploited to advocate the use of the principle of virtual work as the ideal tool to deal with these situations. The question of stability in the descending limb of the force-length relation is addressed and a purely mechanical analogue is suggested. New experimental results confirm the assumption that fibre stiffness is positive even in the descending limb. The indeterminacy of the force-sharing problem is traditionally resolved by optimizing a, presumably, physically meaningful target function. After presenting some new results in this area, based on a separation theorem, it is suggested that a more fundamental approach to the problem is the abandoning of optimization criteria in favour of an explicit implementation of activation criteria.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".