<i>Cannabis sativa</i> (Hemp) Seeds, Δ <sup>9</sup> -Tetrahydrocannabinol, and Potential Overdose
Bibliographic record
Abstract
Abstract Introduction: Cannabis sativa (hemp) seeds are popular for their high nutrient content, and strict regulations are in place to limit the amount of potentially harmful phytocannabinoids, especially Δ 9 -tetrahydrocannabinol (Δ 9 -THC). In Canada, this limit is 10 μg of Δ 9 -THC per gram of hemp seeds (10 ppm), and other jurisdictions in the world follow similar guidelines. Materials and Methods: We investigated three different brands of consumer-grade hemp seeds using four different procedures to extract phytocannabinoids, and quantified total Δ 9 -THC and cannabidiol (CBD). Discussion: We discovered that Δ 9 -THC concentrations in these hemp seeds could be as high as 1250% of the legal limit, and the amount of phytocannabinoids depended on the extraction procedure employed, Soxhlet extraction being the most efficient across all three brands of seeds. Δ 9 -THC and CBD exhibited significant variations in their estimated concentrations even from the same brand, reflecting the inhomogeneous nature of seeds and variability due to the extraction method, but almost in all cases, Δ 9 -THC concentrations were higher than the legal limit. These quantities of total Δ 9 -THC may reach as high as 3.8 mg per gram of hemp seeds, if one were consuming a 30-g daily recommended amount of hemp seeds, and is a cause for concern for potential toxicity. It is not clear if these high quantities of Δ 9 -THC are due to contamination of the seeds, or any other reason. Conclusion: Careful consideration of the extraction method is very important for the measurement of cannabinoids in hemp seeds.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads 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".