Is there sufficient evidence to explain the cause of sexually dimorphic behaviour in force steadiness?
Bibliographic record
Abstract
Neuromuscular noise is a determining factor in the control of isometric force steadiness (FS), quantified as coefficient of variation (CV) of force around a preestablished target output. In this paper we examine sex-related differences of neural, muscular, and tendon influences on neuromuscular noise to understand FS in females and males. We use evidence from the literature to identify that CV of force is higher in females compared with males in the upper and lower body, with sex-related differences becoming less apparent with increasing age. Evaluation of sex-related physiology in tandem with results from FS studies indicate that differences in fibre type, contractile properties, and number of motor units (MUs) are unlikely contributors to differences in FS between females and males. MU type, behaviour of the population (inclusive of number of active MUs from the population), agonist-antagonist activity, maximal strength, and tendon mechanics are probable contributors to sexually dimorphic behaviour in FS. To clearly determine underlying causes of sex-related differences in FS, further study and reporting between females and males is required. Females and males are included in many studies; however, rich data on sexually dimorphic behaviour is lost when data are collapsed across sex or identified as nonsignificant without supporting values. This poses a challenge to identifying the underlying cause of females having higher CV of force than males. This review provides evidence of sexually dimorphic behaviour in FS and suggests that physiological differences between females and males effect neuromuscular noise, and in-turn contribute to sex-related differences in FS.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".