A Taste for New Psychoactive Substances: Wastewater Analysis Study of 10 Countries
Bibliographic record
Abstract
New psychoactive substances (NPS) are compounds designed to mimic both licit and illicit drugs, and these substances are being discovered each year through forensic toxicology, drug enforcement agencies, and health authorities. However, there is limited information surrounding their international popularity. In this work, influent wastewater samples ( n = 144) were collected from 25 sites in 10 countries: Australia, Belgium, Canada, China, Fiji, Italy, New Zealand, Republic of Korea, Spain, and the United States over the 2020–2021 New Year period. All samples were extracted in the country of origin then shipped and analyzed centrally at the University of South Australia using validated liquid chromatography–mass spectrometry methods. This study focused on 28 NPS stimulants, with 11 detected. The emerging substances eutylone and 3-methylmethcathinone (3-MMC) were detected most frequently and with the highest mass loads, indicating international popularity. Interestingly, the “older” generation stimulants, para -methoxyamphetamine (PMA), methylone, and mephedrone, were also detected. From the sites monitored in this work, areas in New Zealand had the highest loads of NPS stimulant consumption. Results here show that wastewater analysis can elucidate the dynamic nature of the NPS market, providing near real-time information on changing consumption patterns whose information can be used to minimize public risk.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".