The Most Popular Chemical Categories of NPS in Four Leading Countries of the Developed World: An Integrative Analysis of Trends Databases, Surface Web, and the Deep Web
Bibliographic record
Abstract
BACKGROUND: New psychoactive substances are very diverse; hundreds exist today. Several schemes exist to categorise them; NPS can be classified into Cannabinoids and Cannabimimetics (1), Phenethylamines (2), Cathinones (3), Tryptamines (4), Piperazines (5), Pipradrol derivatives (6), and miscellaneous substances (7)MATERIALS & METHODS: Observational analyses via multiple internet snapshots will be carried out on the surface web and the deep web. The analyses will be hierarchical and integrative to infer the most popular categories of NPS based on the attentiveness (interest) of web users.RESULTS: Analysis of Google Trends from 2012 to the end of 2016, shows that interest in cannabinoids was the highest (98%), while all other chemical categories of NPS summed up to a tiny fragment (2%). The trends were highly oscillating over the years and shooting up during holiday seasons. Geo-mapping and localisation of the Middle East were not possible (not allowed) via Google Trends, while trends were attributed to four major leading countries of the developed world; US (35%), UK (17%), Canada (26%), and Australia (22%). Cannabinoids and stimulants were also found to be the most popular on the darknet.CONCLUSION: A novel method is proposed in this study; it has been carried out to provide an updated extrapolation on the most favoured chemical categories of NPS. This method is based on a combinatory examination at multiple levels of the surface web and the deep web. Furthermore, this method when potentially combined with data mining tools should provide unprecedented real-time analyses of high quality.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".