New and Emerging Drug Threats in Canada: Fentanyl Precursors
Bibliographic record
Abstract
Introduction Over the past two decades, the illicit drug market in Canada has been marked by the rapid emergence and spread of synthetic substances, which have contributed significantly to drug-related deaths since 2017. The rise in availability of fentanyl and fentanyl analogues together with fentanyl precursors plays a critical role in this crisis. This communication presents the monitoring of these precursors, which provides insights into new patterns in illicit markets. Methods The Health Canada Drug Analysis Service (DAS) operates three laboratories across Canada that analyzes approximately 100,000 samples each year. Results of analyzed samples submitted by Canadian law enforcement and public health officials are reported in a centralized database. On average, the DAS supports the dismantling of 25 clandestine laboratories per year. Results In 2024, fentanyl analogues surpassed fentanyl in their proportion of the illicit opioid supply. Clandestine laboratories are evidence of fentanyl synthesis in Canada; while the increasing variety of fentanyl precursor chemicals in the Canadian illicit drug market, suggest both diversification in the means of fentanyl production and attempts by the illicit market to evade regulatory controls. Conclusions Evidence of illegal domestic production of fentanyl and its analogues in recent years, suggest a shift from the illegal importation of fentanyl and fentanyl analogues to the importation of fentanyl precursors into Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".