Bibliographic record
Abstract
Psychoactive substances have a long history of use as doping substances in sport, usually constituting the second most used drug class behind anabolic steroids. In addition to the classic doping substances, a more challenging problem is presented by the increasing availability of new psychoactive substances (NPS) and other emerging drugs (ED). These news drugs need first to be found and identified, classified based on the little information available as doping substances and finally analytical methods have to be developed or implemented in WADA accredited laboratories to detect them. In an effort to anticipate doping trends, one of the methods WADA uses is an intelligence-based approach, where new or potential doping substances are identified and purchased to confirm or identify their chemical structures and to assess their quality and purity. In this study we set out to monitor both the Darkweb and the Clearweb for emerging drugs with potential doping benefits. A comparison between the prevalence of these substances from both sources will be presented as well, to establish whether athletes have access to more sophisticated substances in the Darkweb. The presence of these NPS/ED as components of multipart nutritional subjects will be reported as well. These results will serve to target efforts to discover new substances entering the sport world for doping purposes and develop prevention and rapid-response strategies.
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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) | 0.000 | 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".