Editorial: Rising stars in exercise physiology
Bibliographic record
Abstract
Physical exercise has been recognized as essential for human health and evolution for thousands of 30 years, beginning with ancient cultures. Hippocrates, Plato, Aristotle, and the Roman physician Galen 31 were the earliest-recorded and most well-known promoters of the beneficial effects of physical 32 exercise. Since these times, several dedicated laboratories worldwide have been established, with many 33 researchers conducting numerous investigations related to exercise physiology; nevertheless, a 34 cornerstone of all laboratories is the development of new and novel researchers. These talented and 35 emerging researchers are necessary for our understanding of exercise physiology to reach where we 36 are today (and where we will be in the future). Given the evolution of exercise physiology, the field 37 has incorporated a range of basic to applied scientific investigations, as well as a range of end-users 38 (e.g., researchers, athletes, coaches, physiologists, and clinical/public health professionals) who will 39 benefit from these new advances in exercise physiology. 40 Accordingly, we were delighted to develop a special issue called "Rising Stars in Exercise Physiology" 41 to showcase the work of these emerging researchers. The current special issue has highlighted the work of "rising stars" within the exercise physiology field 99 whom their peers nominated. The continued support of these, and other emerging researchers, will 100 ensure that the future of exercise physiology is advanced for improved performance and health. We 101 look forward to the future advancement of the exercise physiology field. 102
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.023 | 0.020 |
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".