NBOMe Compounds: Systematic Review and Data Crunching of the Surface Web
Bibliographic record
Abstract
BACKGROUND: NBOMe compounds, some of which commercially known as “N-Bomb” or “Smiles” signifying their potency, represent a uniquely potent group of phenethylamine derivatives. These have been recently used in the past decade for their powerful hallucinogenic properties to induce a “psychedelic trip”.METHODS: This study is an analytics of the surface web incorporating data from; the published literature, grey literature, drug fora, and trends’ databases. The study aims to review the pharmacodynamic effects of three most popular N-Bombs (25b, 25c, and 25i), analyse reported cases of intoxications and fatalities, and correlate these incidents with data retrieved from Google Trends.RESULTS: The potency and popularity of NBOMe compounds are tallied worldwide, 25b-NBOMe (least potent and least popular), 25i-NBOMe (most potent and most popular), while the 25c-NBOMe is in the middle. The popularity of each has been on the rise since 2011-2012, these compounds are most popular in the United States and the United Kingdom, while data from the developing world and the densely-populated India and China are either lacking or inadequate. The reported cases of intoxications and deaths were statistically proven to be correlated with the trends’ dataCONCLUSION: Inferential statistical information has associated cases of NBOMe(s)’ morbidities-mortalities with the public interest of surface web users in these hallucinogens. This study can serve a blueprint for an early warning system to be activated based on changes in trends’ data.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.029 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".