Challenges And Perspectives in Anti-Doping Testing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In less than 10 years after the implementation of the World Anti-Doping Code and of the International Standard for Laboratories and its related Technical Documents, the analysis of human samples for the purpose of anti-doping testing has undergone a noticeable evolution. The research programs developed by the anti-doping organizations, and in particular the World Anti-Doping Agency (WADA), have created an unprecedented momentum in anti-doping science to strengthen the existing analytical methods, as well as to support the development and implementation of new and more sophisticated methodologies by the WADA-accredited laboratories. The integration of technical novelties into the analytical menus has been stimulated by the never-ending challenges posed by the adoption of more complex doping regimens by some athletes and their entourage. This increased sophistication of doping practices has also been reflected in the addition of new doping substances or methods on the WADA Prohibited Substances and Methods List. The integration of new anti-doping scientific paradigms with the development of the Athlete Biological Passport or the foreseen implementation of genomic- and proteomic-based tests constantly reshapes the environment of anti-doping analysis. This article provides a multiangle perspective on some of the key analytical challenges that anti-doping analytical science will face in 2012 and beyond.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it