So you think you can cook pot?
Bibliographic record
Abstract
Background: With regulations on additional cannabis products including edibles being in the works, Canada is faced with a new layer of food safety challenges as the public becomes increasingly curious about adding cannabis into their diets. Knowledge in both food safety and edible safety is essential to prevent health hazards associated with edible cannabis products. Methods: An online self-administered survey was conducted on a British Columbia population. In addition to demographic data which also included cannabis usage, participants answered two knowledge tests on food safety and edible safety, respectively. The surveys were analyzed for differences in test scores between demographic groups. Results: Users of cannabis edibles have significantly higher knowledge in edible safety than non-users. This was not affected by the purpose or frequency of edible use. A slight positive correlation (0.18) between food safety knowledge and edible safety knowledge suggested the two topic areas to be mutually beneficial. In contrary, knowledge in food safety was not significantly different across all demographic groups. Conclusions: Non-users of cannabis edibles are more at risk of health hazards related to ingestion of cannabis edibles due to lower knowledge in this subject matter and eagerness to experience cannabis products after their legalization. Therefore, there is a need for education programs to help familiarize the public with these products. It is also recommended for the public to strengthen general food safety knowledge because all of it also applies when making edibles.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.267 | 0.066 |
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".