Complementary stable carbon isotope ratio and amount of substance measurements in sports anti‐doping
Bibliographic record
Abstract
The detection of steroids originating from synthetic precursors against a background of their chemically identical natural analogues has proven to be a significant challenge for doping control laboratories accredited by the World Anti-Doping Agency (WADA). The complementary application of gas chromatography-mass spectrometry (GC-MS) and gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS) has been demonstrated to provide specific detection of endogenous steroid misuse for improved anti-doping analysis. Markers of synthetically derived steroids are reviewed on the basis of abnormal urinary excretions and low (13)C content. A combinatorial approach is presented for the interpretation of GC-MS and GC-C-IRMS data in the anti-doping context. This methodology can allow all relevant information concerning an individual's metabolism to be assessed in order to make an informed decision with respect to a doping violation.
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How this classification was reachedexpand
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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".