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
BACKGROUND: Fenetheylline, a psychostimulant drug, often branded as Captagon, is a combination of amphetamine and theophylline. Since the cessation of its legal production in 1986, counterfeited products have been produced illicitly in south-east Europe and far-east Asia. Its profitable trade has been linked to terrorist organizations, including Islamic State of Iraq and the Levant. This study aims to reach up-to-date data, concerning the Captagon e-commerce and use in the Middle East. METHODS: A multi-staged and multi-lingual literature search was carried out. A list of prespecified keywords was applied across medical and paramedical databases, web and Dark web, search engines, social communication media, electronic commerce websites, media networks, and the Global Public Health Intelligence Network database. RESULTS: The use of Captagon as a stimulant in terrorist settings has been marginally covered in the literature. Data can widely be retrieved from Google and AOL search engines, YouTube, and Amazon e-commerce websites, and to a lesser extent from Alibaba and eBay. On the contrary, Middle Eastern e-commerce websites yielded almost no results. Interestingly, the Dark web generated original data for Captagon e-commerce in the Middle East. CONCLUSION: Further investigations are needed on the role that psychoactive drugs play in terrorist attacks and civil war zones. Unless a comprehensive methodological strategy, inclusive of unconventional methods of research, is implemented, it will not be feasible to face such a threat to humanity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it