Captagon, Octodrine, and NBOMe: An Integrative Analysis of Trends Databases, the Deep Web, and the Darknet
Bibliographic record
Abstract
BACKGROUND: Captagon, Octodrine, and NBOMe are unique substances; each represents a chemical category of its own pharmacodynamic and pharmacokinetic properties. Captagon is an amphetamine-type stimulant, while octodrine is a sympathomimetic agent, on the other hand, NBOMe is a hallucinogen (psychedelic substance). However, the mechanism of action for each is exerted via monoamine transporters.MATERIALS & METHODS: This study will explore these substances using an integrative approach via the analysis of the surface and deep web, and a trends database. The aims are; to visualise the extent of diffusion of each substance on the internet, conclude the geo-mapping for the diffusion, to see if the patterns are compatible on both divisions of the web, and to infer data on the basis of the power (authority) for e-vendors on the darknet e-marketplace. This study is a hybrid of cross-sectional and retrospective analyses.RESULTS: Google Trends analyses confirmed that the popularity of captagon is ahead over both NBOMe and octodrine; captagon popularity was correlated with terror attacks in the developed world, particularly in western European countries. The contribution of the developing countries to the diffusion of these substances, including the Middle East, was minimal.CONCLUSION: This study proposes a novel method to analyse the e-markets on the darknet via the use of; analysis of the basis of power, inferential statistics, geo-mapping in parallel with data from Google Trends database. Data from Google Trends can serve as a foundation for data mining techniques for an efficient warning system against an anticipated swarm of intoxications or an attack of terror.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".