Bibliographic record
Abstract
There are many instances in daily life and sport in which force must be exerted when an individual performing the task is in an unstable condition. Instability can decrease the externally-measured force output of a muscle while maintaining high muscle activation. The high muscle activation of limbs and trunk when unstable can be attributed to the increased stabilization functions. The increased stress associated with instability has been postulated to promote greater neuromuscular adaptations, such as decreased co-contractions, improved coordination, and confidence in performing a skill. In addition, high muscle activation with less stress on joints and muscles could also be beneficial for general musculoskeletal health and rehabilitation. However, the lower force output may be detrimental to absolute strength gains when resistance training. Furthermore, other studies have reported increased co-contractions with unstable training. The positive effects of instability resistance training on sports performance have yet to be quantified. The examination of the literature suggests that when implementing a resistance training program for musculoskeletal health or rehabilitation, both stable and unstable exercises should be included to ensure an emphasis on both higher force (stable) and balance (unstable) stressors to the neuromuscular system.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".