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 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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".