Nutritive quality and protein production from grain legumes in a boreal climate
Bibliographic record
Abstract
BACKGROUND: Boreal cropping systems are heavily focused on the production of small-grain cereals; to improve their resilience to climate change and to achieve food and feed security, diversification is needed. This study investigated the potential of faba bean, narrow-leafed lupin and lentil as protein crops in southern Finland, where faba bean is traditional but the other two are novel. RESULTS: Early cultivars of narrow-leafed lupin and lentil matured adequately. Protein concentration in faba bean was, at 32%, higher than the world average of 29%, while those of narrow-leafed lupin and lentil were close to their world averages. Protein yields decreased in the order faba bean > narrow-leafed lupin > lentil. Lipid content of faba bean and lentil was about 1.2% and that of narrow-leafed lupin about 5.5%, and fatty acid composition was largely oleic and linoleic in all three species. CONCLUSION: Both lentil and narrow-leafed lupin can be added to the range of feed and food crops produced at high latitudes in Europe. While faba bean produces the greatest protein yield and lysine concentration, the higher sulfur amino acid concentration in lupin, its oil content and its adaptation to acid, sandy soils not suitable for faba bean make it an attractive alternative.
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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) | 0.000 | 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".