Bibliographic record
Abstract
Sport plays a major role in the lives of many people, both for active participation and as entertainment. Sport is now a huge nationally and internationally based industry. The desire to win has led some athletes to resort to the use of performance enhancing drugs. With huge financial rewards now available in some sports the pressure to excel has grown. Some have argued that drug use should be given free rein, however most people are of the view that it is athletic prowess that should be applauded not the efficacy of various performance enhancing drugs. Apart from the obvious aspects of equality and fair play, the use of drugs is associated with significant health risks. In the 1960's the use of stimulants in sports such as cycling led to the death of at least one cyclist. Since 1968 the International Olympic Committee (IOC) has required all Olympic Games' host cities to provide laboratory facilities for the analysis and detection of performance enhancing drugs. There are now 29 IOC accredited laboratories throughout the world that routinely test samples from athletes for the presence of such drugs. The purpose of this tutorial review is to give an overview of drug testing procedures, including those that were used at the last summer Olympic Games in Sydney 2000, and the incorporation of the latest developments in analytical chemistry technology in the drug testing process. More recently, developments in biotechnology mean that the use of whole new classes of drugs are banned in sport, often requiring new methodologies and techniques for their analysis. The contest between those who wish to cheat and those who wish to maintain fair play in sport is an ongoing one.
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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".