Bibliographic record
Abstract
In animals, muscles are the most common effectors that translate neuronal activity into behavior. Nowhere is behavior more restricted by the limits of muscle performance than at the upper range of high-frequency movements. Here, we see new and multiple designs to cope with the demands for speed. Extremely rapid oscillations in force are required to power cyclic activities such as flight in insects or to produce vibrations for sound. Such behaviors are seen in a variety of invertebrates and vertebrates, and are powered by both synchronous and asynchronous muscles. In synchronous muscles, each contraction/relaxation cycle is accompanied by membrane depolarization and subsequent repolarization, release of activator calcium, attachment of cross-bridges and muscle shortening, then removal of activator calcium and cross-bridge detachment. To enable all of these to occur at extremely high frequencies a suite of modifications are required, including precise neural control, hypertrophy of the calcium handling machinery, innovative mechanisms to bind calcium, and molecular modification of the cross-bridges and regulatory proteins. Side effects are low force and power output and low efficiency, but the benefit of direct, neural control is maintained. Asynchronous muscles, in which there is not a 1:1 correspondence between neural activation and contraction, are a radically different design. Rather than rapid calcium cycling, they rely on delayed activation and deactivation, and the resonant characteristics of the wings and exoskeleton to guide their extremely high-frequency contractions. They thus avoid many of the modifications and attendant trade-offs mentioned above, are more powerful and more efficient than high-frequency synchronous muscles, but are considerably more restricted in their application.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".