The NPS Phenomenon and the Deep Web: Trends Analyses and Internet Snapshots
Bibliographic record
Abstract
BACKGROUND: In relation to the phenomenon of novel psychoactive substances, activities on the surface web represent only the tip of the iceberg. The majority of the electronic commerce (e-commerce) activities exist on the deep web and the darknet. Observational analytic studies are failing to keep pace with these activities; these studies are either obsolete beyond the point in time of the taken internet snapshot or highly-consuming for resources including time, funding, and manpower.MATERIALS & METHODS: Cross-sectional and retrospective analyses via multiple Internet snapshots were carried out across Google Trends database and the e-markets on the darknet. Google Trends were scanned retrospectively (2012-2016) for keywords specific to the deep web in an aim to estimate and geo-map of the attentiveness (interest) of surface web users in the deep web and its illicit activities.RESULTS: The attentiveness of surface web users in the deep web was noticed to be incremented during 2013 and 2014; the top ten contributing countries were Norway, Germany, Denmark, Austria, Poland, Sweden, Slovenia, Switzerland, Finland, and Netherlands. Middle Eastern countries contributed minimally including; Syria, Iran, Israel, UAE, Morocco, Egypt, and Saudi Arabia. Power scoring of e-markets revealed that the top five markets were; AlphaBay, Agora, Nucleus, Abraxas, and Hansa. The most common categories of NPS on these markets were; cannabis and cannabimimetic (1st), stimulants (2nd), empathogens (3rd), and psychedelics (4th).CONCLUSION: The e-commerce activities on the deep web and the darknet e-marketplace represent an integral component of the NPS e-phenomenon. Unfortunately, recent attempts to examine and study those unlawful activities are outdated. Hence, to achieve real-time and reliable data, the inclusion of data mining tools and knowledge discovery in databases are critical to ensuring a future victory.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".