Safety and efficacy of hemp-derived products in animal feeds—a narrative review
Bibliographic record
Abstract
The current interest in hemp is not limited to the medicinal use for humans, it is also gaining attention as a valuable feed ingredient for animals. For instance, the processing of whole hemp seed (HS) for the extraction of the oil can result in various feed materials for animals, including HS hulls, dehulled HS, HS oil, and HS cake/meal. However, currently, HS-derived products are not approved as feed ingredients in Canada, in part, because the safety and efficacy of these derivatives, like other jurisdictions, require certification by regulatory bodies for their use in animal feeds. In this context, this document reviews previous studies testing the application of hemp-derived products in various animal feeds, including poultry, swine, ruminants, as well as in aquaculture production to support their safety and efficacy. Furthermore, the document attempts to review available data on the transfer of delta-9-tetrahydrocannabinol (THC; the psychoactive component) into animal products. Hence, the aim of this work is to support future policy directions and guidelines for the application of various hemp-derived products in animal feeds.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".