Human genetic variation: New challenges and opportunities for doping control
Bibliographic record
Abstract
Sport celebrates differences in competitors that lead to the often razor-thin margins between victory and defeat. The source of this variation is the interaction between the environment in which the athletes develop and compete and their genetic make-up. However, a darker side of sports may also be genetically influenced: some anti-doping tests are affected by the athlete's genotype. Genetic variation is an issue that anti-doping authorities must address as more is learned about the interaction between genotype and the responses to prohibited practices. To differentiate between naturally occurring deviations in indirect blood and urine markers from those potentially caused by doping, the "biological-passport" program uses intra-individual variability rather than population values to establish an athlete's expected physiological range. The next step in "personalized" doping control may be the inclusion of genetic data, both for the purposes of documenting an athlete's responses to doping agents and doping-control assays as well facilitating athlete and sample identification. Such applications could benefit "clean" athletes but will come at the expense of risks to privacy. This article reviews the instances where genetics has intersected with doping control, and briefly discusses the potential role, and ethical implications, of genotyping in the struggle to eliminate illicit ergogenic practices.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".