A Taste for New Psychoactive Substances: Wastewater Analysis Study of 10 Countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it