Public health considerations about tetrahydrocannabinol‐infused beverages
Bibliographic record
Abstract
The US cannabis market has evolved to include cannabis-infused beverages; 20% of cannabis consumers in recreational cannabis states have consumed a delta-9-tetrahydrocannabinol (THC)-infused beverage [1].US sales of THC-infused beverages represented 6% of all edible sales [1].THC beverages are being sold throughout the United States, regardless of the state cannabis laws, due to the 2018 Farm Bill, which defined hemp as cannabis products with < 0.3% delta-9 THC by dry weight.Due to the heavy weight of liquid, these products can contain high levels of THC and still be under the threshold.Changing the federal definition of hemp could make THC-infused beverages federally illegal.THC beverages are also available in other locations across the globe, including Canada, Germany, Thailand and Laos.However, their legal status and level of regulations differ depending on local laws.For example, while cannabis is illegal in Germany and Laos, THC beverages are still available.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 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".