Ensuring High Quality in Anti-Doping Laboratories
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| 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) | 0.001 | 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