Current Implementation And Future of The Athlete Biological Passport
Bibliographic record
Abstract
During the last four decades, the main instrument at the disposal of anti-doping authorities has been the detection of prohibited substances in biological samples collected from athletes. However, the availability of substances identical to those produced by the human body, such as EPO, testosterone and GH, necessitated a new drug-testing paradigm. From the early 2000's, the Athlete Biological Passport (ABP) was proposed as an alternative means to drug testing. Doping leaves a characteristic fingerprint on the biology of the athlete and the ABP is used to prove the act of doping from the detection of that fingerprint. Once a biomarker of doping is implemented in the ABP, it will continue to remain valid and should be able to detect the physiological changes brought on by performance-enhancing drugs that have not yet been invented. However, the sensitivity of the ABP to detect doping is limited if the physiological result of a low level of doping remains within the individual's own reference range. Recent advances in proteomics and metabolomics show the huge potential of the ABP.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".