Bibliographic record
Abstract
Physiology plays a key role in understanding and optimizing human physical performance.For many athletes, the Olympic Games remain the pinnacle of achievement.Beyond public interest and return on (state) investment, studying the physiology of athletes provides a unique insight into the capabilities of the various systems (e.g., muscular, cardio-and cerebrovascular, respiratory) that can support basic and clinical research.This was the premise upon which a call was made for the current Special Issue on 'Physiology and the Olympics' , focused on advancing our physiological understanding of the science behind preparation for, competition in, and recovery from human performance.This call was answered comprehensively: (1) 18 papers in total, whose corresponding authors span nine countries (Belgium, Canada, France, Germany, Greece, Italy, Poland, UK and USA); (2) article categories including Myths and Methodologies (1), Short Communication (1), Case Reports (3), Reviews (4) and Research Articles (8); (3) subject areas that embrace Cardiovascular Control, Endocrinology and Metabolism, Environmental and Exercise, Muscle and Neuroscience; and (4) athlete (participant) categories that comprise cycling, running, jumping, throwing, swimming, football (soccer), sailing, alpine skiing, kayaking, weight-and power-lifting, CrossFit, esports, speed-skating, skeleton, biathlon, triathlon and decathlon.No matter your interest, this Special Issue has something to whet your physiological appetite! 2 AEROBIC CAPACITY AND PREDICTION OF PERFORMANCE A case report by Zubac et al. (2025) provided a retrospective analysis of world-class Laser Standard sailors through cardiopulmonary exercise testing.These sailors are renowned for their agile 'hiking' skills, whereby quasi-isometric bilateral and multi-joint movements account for up to 90% of the upwind time, strongly relate to neuromuscular fatigue and play a pivotal role in sailing performance.They report that well-balanced aerobic power is vital for superior sailing performance, whilst oxygen pulse measures over 6 years were noted not to change, indicative of compensatory cardiovascular mechanisms.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".