A–Z of nutritional supplements: dietary supplements, sports nutrition foods and ergogenic aids for health and performance: Part 48
Bibliographic record
Abstract
As we end this series of reviews of supplements and sports foods, it is fitting that the last words should come from the World Anti-Doping Agency. After all, the contravention of anti-doping rules due to the ingestion of prohibited substances that are ingredients or contaminants of some supplements and sports foods is a key issue which must be taken into account whenever an athlete decides whether or not to use such products. ### A Vernec Athletes have a long history of using substances in an attempt to gain an advantage in sporting competitions. The ancient Greeks and Romans used herbs, fungi, poppy seeds and stimulants such as strychnine in order to boost performance.1 In the modern era, this practice continued mostly with the use of stimulants and narcotics. Sports federations took notice and in 1928 the International Association of Athletics Federations (IAAF) became the first federation to prohibit the use of performance-enhancing drugs (PEDs), although there would be no testing in sport for another 40 years.2 Amphetamine use was involved in the deaths of cyclists Knud Jensen and Tommy Simpson in the 1960 Olympic Games and the 1967 Tour de France respectively: this spurred the development of the International Olympic Commissions (IOC) Medical Commission, which published the first IOC Prohibited List in 1967. This became the de facto Prohibited List for Olympic Sport Federations. The ‘Festina affair’ (1998 Tour de France), where a team trainer's car was found to contain a panoply of PEDs, was the catalyst to create a new organisation to harmonise, coordinate and promote the fight against doping in sport in all its forms.3 The IOC convened the first World Conference in Doping in Sport in 1999, which resulted in the formation of the World Anti-Doping Agency (WADA). WADA is a unique, independent body representing equally sport and …
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.016 |
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".