Drug adulteration and substitution within Australian cryptomarkets: An analysis of Test4Pay
Bibliographic record
Abstract
Prohibited drugs in unregulated markets are often adulterated, resulting in increased risks for consumers. This study investigated levels of drug adulteration and substitution in drugs purchased by Australians from cryptomarkets.Methods.Data were collected from the Dread subforum /d/Test4Pay from 1/9/2022 to 23/8/2023. Posts were included if they reported the results of drug samples submitted by post to the Vancouver-based Get Your Drugs Tested service, which uses Fourier-transform infrared spectroscopy (FTIR) with immunoassay strip tests (fentanyl and benzodiazepines).Results.Out of 103 samples, 65% contained only the advertised substance, 14% contained the advertised substance in combination with other psychoactive and/or potentially harmful substances, and for 21%, the advertised substance was absent. MDMA, methamphetamine and heroin were consistently found to contain only the advertised substance, while 2C-B, alprazolam and ketamine were the most likely to be completely substituted. Only one-fifth (21%) of cocaine samples contained solely the advertised substance, with 68% of the samples containing cocaine with adulterants like lidocaine, creatine, levamisole, and boric acid. No fentanyl contamination was detected. Different novel dissociatives and novel benzodiazepines were detected, as well as a nitazene compound.Discussion and Conclusions.Drug markets under prohibition continue to contain numerous unexpected substances, some of which can elevate risk of harm dramatically for consumers. Cryptomarkets are not immune to this problem, despite review systems which should in theory make vendors more accountable for the quality of their stock. An expansion of local drug checking services is urgently needed in the Australian context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".