Canada's THC unit: Applications for the legal cannabis market
Bibliographic record
Abstract
The legalization of cannabis in Canada has accelerated the need for a standardized approach to measuring and communicating the amount of delta-9-tetrahydrocannabinol (THC) in cannabis products. This article offers an overview of the considerations associated with establishing and implementing a standard THC unit in the Canadian context. The article begins by discussing the applications of a standard THC unit, emphasizing its potential use in product labelling, consumer education, and product reporting and surveillance. The article then examines key considerations for identifying what a Canadian THC unit should be set at, specifically within the context of a country with a regulated commercial cannabis market. This is followed by a discussion of additional considerations related to the adoption of a Canadian THC unit, including its use across various product formats and modes of administration. A significant focus of this article is on prioritizing public health and safety and informed decision-making among adult consumers as the legal cannabis market evolves. Collaboration among various stakeholders, such as government agencies, industry, and public health professionals, is highlighted as crucial for a successful transition to the use of Canada's THC unit.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| 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".