Hempseed Products Fed to Hens Effectively Increased n‐3 Polyunsaturated Fatty Acids in Total Lipids, Triacylglycerol and Phospholipid of Egg Yolk
Bibliographic record
Abstract
Hempseed products represent potential alternative feed ingredients for poultry. However, their usage is not currently approved due to a lack of data to support their safety and efficacy. In this regard, the current study was conducted to assess the impact of dietary concentration of hempseed (HS) products and duration of their feeding to hens on the polyunsaturated fatty acid (PUFA) composition of egg yolk lipids. In the current study, 48 Lohmann LSL-Classic hens were individually housed in metabolism cages, in a completely randomized design, and provided one of six diets (wheat-barley-soybean-based) containing either HS (10, 20 and 30 %), hempseed oil (HO; 4.5 and 9.0 %) or no hempseed product (control) over 12 weeks. Increasing alpha-linolenic acid (ALA) intake via increasing dietary hempseed product inclusion, significantly (p < 0.0001) increased the n-3 PUFA contents of yolk total lipid. The values of ALA increased by 12-fold (152 ± 3.56 and 156 ± 2.42 mg/yolk) and docosahexaenoic acid (DHA) by twofold to threefold (41.3 ± 1.57 and 43.6 ± 1.61 mg/yolk) over the control, for the highest levels of HS and HO inclusion, respectively. Increasing levels of hemp products in laying hen diets proved effective in manipulating the fatty acid profile of the total lipid, triacylglycerol (TAG) and total phospholipid (PL) fractions of yolks, enhancing the n-3 fatty acids and reducing the n-6/n-3 ratio. The latter benefit was achieved within 4 weeks of feeding hens either HS- or HO-containing diets.
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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 | 0.000 | 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".