Bibliographic record
Abstract
Since the first police bust of fentanyl in Canada in 2013, fentanyl may have become a more powerful and deadly drug than ever before (Howlett & Woo, n.d.). The Canadian healthcare system is likely impacted, with the likelihood of the situation worsening because of COVID-19. One of the possible reasons for this could be Canada’s role as a consumer in the supply chain of fentanyl. Accessibility to fentanyl is becoming easier with online purchases and delivery services, such as Canada Post (Brownell, 2019; Howlett & Woo, n.d.). This threat may continue as long as there is financial motivation for drug cartels, such as the Sinaloa cartel to transport fentanyl across borders with the assistance of China. Additionally, the success could potentially incite the production of more man-made synthetic drugs. Hence, in order to minimize this potential risk to Canadian communities, the direct impact Canada is facing must be addressed first. APA Citation Tung, G. (2020). The potential threat of the Sinaloa cartel to Canada: production and transportation of fentanyl. The Journal of Intelligence, Conflict, and Warfare, 3(2), 46-53. https://journals.lib.sfu.ca/index.php/jicw/article/view/2373/1811.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".