Bibliographic record
Abstract
Aging is associated with a progressive decline in skeletal muscle mass and function (sarocopenia). Despite several years of research, controversy exists regarding the manifestations and causes of sarcopenia. In the former respect, whereas a preferential loss of so-called "fast-twitch" muscle fibers occurs in rat models of aging, this appears unlikely in human skeletal muscle. In the latter respect, whereas a decline in physical activity with aging contributes to whole-muscle atrophy, it cannot explain the marked heterogeneity in muscle fiber size seen in aged muscles. Similarly, systemic alterations, such as reduced blood levels of anabolic hormones and nutritional deficits, although involved in modulating the degree of whole-muscle atrophy, cannot explain the observation that only some fibers atrophy and die while most appear unaffected. A further significant question remaining is that if death of some muscle fibers is normal and perhaps advantageous (that is, by removing malfunctioning cells), what is the capacity for muscle fiber regeneration in adult skeletal muscle and can this process be augmented in aging muscles?
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".