Bibliographic record
Abstract
In 1998, industrial hemp became a legal crop in Canada, promising environmentally-sound farming and processing of fibre for paper, textiles, and building products. In addition, hemp seed is among the world's most nutritious foods, and its oil is an exceptional bodycare emollient. In 1999, Health Canada issued a draft report entitled Industrial Hemp Risk Assessment. The report dealt only with hemp foods and cosmetics (bodycare products) and focused on tetrahydrocannabinol (THC), the psychoactive ingredient in cannabis hemp. By law, hemp foods and cosmetics must contain less than 10 parts per million THC. Health Canada concluded that, even with THC content limited to 10 ppm, "inadequate margins of safety exist between potential exposure and adverse effect levels for cannabinoids in cosmetics, food, and nutraceutical products made from industrial hemp. " Health Canada, therefore, is considering a ban on hemp foods and cosmetics. The purpose of the Ad Hoc Committee on Hemp Risks is to respond scientifically to the Health Canada risk assessment. We focused on four allegations by Health
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.328 | 0.246 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".