Fenugreek: an “old world” crop for the “new world”
Bibliographic record
Abstract
Fenugreek is an annual legume crop that is new to North America. This crop has the potential to have positive impacts on commercial, agricultural and environmental aspects of agriculture on this continent. In addition to increasing crop diversity this crop will enrich soil by fixing atmospheric nitrogen and would be easy to incorporate into short term crop rotations to help soil conservation and reduce the impact of soil borne pathogens. Fenugreek leaves and seed have been used extensively for medicinal purposes. It is effective as an anti-diabetic agent and in the treatment of hypocholesterolemia. Fenugreek cultivars are being developed for use as a forage crop in Canada. Its high quality and dryland adaptation makes it attractive as a forage crop for our large beef cattle industry. This crop is expected to reduce feed requirements through increased feed efficiency and lower water consumption during crop production. Fenugreek contains animal growth promoting substances not present in other forage legumes and so has the potential to reduce use of artificial growth promoters. This and other medicinal properties of Fenugreek will help reduce dependence on synthetic drugs that are considered serious contaminants of water resources. Cultivars with improved seed yield and enhanced levels of chemical constituents can be developed for improving efficiency of its use both for cattle and humans.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".