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.
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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 | 0.075 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.023 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".