Canadian sainfoin and fenugreek as forage and functional foods
Bibliographic record
Abstract
Abstract Sainfoin (Onobrychis viciifolia Scop.) and fenugreek (Trigonella foenum‐graecum L.) are two legumes that are being developed as forage crops in Canada with potential benefits for animal and human health. Sainfoin, a perennial crop containing condensed tannins (CTs), is gaining popularity in western Canada because of its benefits for cattle. Its CTs make the crop bloat‐free for grazing cattle while improving protein digestibility and reducing greenhouse gas emissions. The CT‐containing fenugreek is also considered as a bloat‐free annual forage legume that was developed to serve in short‐term crop rotations in western Canada. These crops are known to provide health and nutritional benefits to cattle with their high protein content and other beneficial nutraceuticals such as crude fiber, 4‐hydroxyisoleucine, steroid sapogenins, and galactomannans. Some of these nutraceuticals have the potential to benefit human health; however, such attributes have not been studied enough to harness the full potential of these legume crops in Canada. Recent research suggests that legumes are a healthy substitute for meat. However, metabolite analysis of sainfoin is mostly limited to proteins and CTs. The CTs reported in sainfoin are involved in reduction of blood pressure and detoxification and providing anticancer properties in humans. Recent studies on fenugreek have highlighted the beneficial nutraceuticals associated with human health but most of those claims are not backed by relevant clinical studies. In this article, we reviewed the nutritional quality attributes of sainfoin and fenugreek and assessed their potential as functional foods and nutraceuticals for animal and human health based on scientific evidence.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".