Ensuring High Quality in Anti-Doping Laboratories
Bibliographic record
Abstract
The worldwide network of World Anti-Doping Agency (WADA)-accredited anti-doping laboratories plays a fundamental role in supporting the global fight against doping in sport. This role is dependent on the ability to provide accurate, reliable and comparable data in identifying and measuring the presence of prohibited substances and methods. The accredited laboratories participate in WADA's External Quality Assessment Scheme (EQAS) program, which provides the structure to continuously assess and improve laboratory performance in compliance to the requirements of the International Standard for Laboratories and related Technical Documents. The WADA EQAS is comprised of various programs, including a blind EQAS, a double-blind EQAS and an educational EQAS, each with specific goals with regard to monitoring and improving laboratory competence. In this article, the anti-doping rules and processes that govern granting and maintenance of WADA laboratory accreditation, aimed at ensuring a high-quality of laboratory operations within the framework of the global fight against doping in sport, are reviewed.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".