Bibliographic record
Abstract
In elite sport, where opponents are evenly matched, small factors can determine the outcome of sporting contests. Not all athletes know the value of making wise nutrition choices, but anything that might give a competitive edge, including dietary supplements, can seem attractive. Between 40% and 100% of athletes typically use supplements, depending on the type of sport, level of competition, and the definition of supplements. However, unless the athlete has a nutrient deficiency, supplementation may not improve performance and may have a detrimental effect on both performance and health. Dietary supplements are classified as a subcategory of food, so manufacturers are not required to provide evidence of product safety and efficacy, nor obtain approval from regulatory bodies before marketing supplements. This creates the potential for health risks, and serious adverse effects have been reported from the use of some dietary supplements. Athletes who compete in sports under an anti-doping code must also realize that supplement use exposes them to a risk of ingesting banned substances or precursors of prohibited substances. Government systems of regulations do not include specific laboratory testing for banned substances according to the WADA list, so a separate regulatory framework to evaluate supplements for their risk of provoking a failed doping test is needed. In the high-performance culture typical of elite sport, athletes may use supplements regardless of possible risks. A discussion around medical, physiological, cultural, and ethical questions may be warranted to ensure that the athlete has the information needed to make an informed choice.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".