Chemical Characterization of Hemp (<i>Cannabis sativa</i> L.)-Derived Products and Potential for Animal Feed
Bibliographic record
Abstract
Hemp ( Cannabis sativa L.)-derived products are not approved as feed ingredients in Canada. This study aims to provide detailed chemical characterization of hemp seed (HS)-processing products to support regulatory approval of their potential use as animal feed. Eight cold-pressed derivatives of HS including HS hulls (HH), dehulled HS, HS oil (HO), and HS cake/meal (HC/HM) were analyzed for nutritional, antinutritional, and cannabinoid contents. Although crude protein (CP) was lower ( P < 0.0001) in HH (15.7 ± 2.96%) compared to other proteinous derivatives (>20%), all fractions were rich in amino acids. Neutral detergent fiber was highest ( P < 0.0001) in HH (58.8 ± 4.77%) and lowest in dehulled HS (2.99 ± 4.77%). However, their energy values in poultry and swine were comparable to HC/HM and coarse HS protein. All fractions, except HO, are rich in macro- and microminerals. Antinutritional factors including heavy metals, nitrate, and Δ9-tetrahydrocannabinol were below the maximum allowable residual levels in food/feed [ CFIA Canadian Food Inspection Agency. Rg-8 Regulatory Guidance: Contaminants in Feed (Formerly Rg-1, Chapter 7), 2017 . https://inspection.canada.ca/animal-health/livestock-feeds/regulatory-guidance/rg-8/eng/1347383943203/1347384015909?chap=0 (Accessed October 20, 2023), Commission Regulation EC Setting Maximum Levels for Certain Contaminants in Foodstuffs, 2006, https://extwprlegs1.fao.org/docs/pdf/eur68134.pdf (Accessed October 11, 2020), and EFSA EFSA J. 2015, 13 (6), 4141 ]. In summary, all HS-derived fractions are nutritionally favorable to serve as potential animal feed ingredients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".