CrossTalk proposal: Exercise training intensity is more important than volume to promote increases in human skeletal muscle mitochondrial content
Bibliographic record
Abstract
Mitochondria are vital organelles for health and performance that display remarkable plasticity, particularly in response to changes in contractile stimuli (Hood et al. 2019).Exercise training increases the abundance of skeletal muscle mitochondria (Holloszy, 1967;Morgan, 1971), as assessed by mitochondrial fractional area, respiration, enzyme activity and protein content, among other measures (Larsen et al. 2012).Given the associations between skeletal muscle mitochondrial content, exercise capacity and health (Holloszy, 1967;Hood et al. 2019), examining the responsiveness of mitochondria to various exercise stimuli is critical for understanding physiological regulation and providing evidence-based exercise prescription in athletic and clinical settings.While the responsiveness of skeletal muscle mitochondria to different exercise stimuli has been addressed in several systematic rodent studies (e.g.Fitts et al. 1975;Hickson, 1981;Dudley et al. 1982), the focus of thisThe authors are interested in understanding the mechanisms regulating human physiological responses to acute and chronic exercise and the factors mediating these responses, such as nutrition, sex and the environment.
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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.044 | 0.014 |
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".