Non-invasive methods in paediatric exercise physiology
Bibliographic record
Abstract
Oded Bar-Or's hypothesis that children may be "metabolic non-specialists", even when engaging in specialized sports, has stimulated the study of paediatric exercise metabolism since the publication of his classic text Pediatric sports medicine for the practitioner in 1983. Evidence drawn from several methodologies indicates an interplay of anaerobic and aerobic exercise metabolism in which children have a relatively higher metabolic contribution from oxidative energy pathways than adolescents or adults, whereas there is a progressive increase in glycolytic support of exercise with age, at least into adolescence and possibly into young adulthood. The picture is generally consistent but incomplete, as research with young people has been limited by both ethical and methodological constraints. The recent rigorous introduction of non-invasive techniques such as breath-by-breath respiratory gas analysis and magnetic resonance spectroscopy into paediatric exercise physiology promises to open up new avenues of research and generate unique insights into the metabolism of the exercising muscle during growth and maturation. It therefore appears that we might have available the tools necessary to answer some of the elegant questions raised by Professor Bar-Or over 25 years ago.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".