Lentils (Lens culinaris Medikus Subspecies culinaris): A Whole Food for Increased Iron and Zinc Intake
Bibliographic record
Abstract
Micronutrient malnutrition, the hidden hunger, affects more than 40% of the world's population, and a majority of them are in South and South East Asia and Africa. This study was carried out to determine the potential for iron (Fe) and zinc (Zn) biofortification of lentils ( Lens culinaris Medikus subsp. culinaris ) to improve human nutrition. Lentils are a common and quick-cooking nutritious staple pulse in many developing countries. We analyzed the total Fe and Zn concentrations of 19 lentil genotypes grown at eight locations for 2 years in Saskatchewan, Canada. It was observed that some genetic variation exists for Fe and Zn concentrations among the lentil lines tested. The total Fe and Zn concentrations ranged from 73 to 90 mg of Fe kg(-1) and from 44 to 54 mg of Zn kg(-1). The calculated percentages of the recommended daily allowance (RDA) for Fe and Zn were within the RDA ranges from a 100 g serving of dry lentils. Broad-sense heritability estimates for Fe and Zn concentrations in lentil seed were 64 and 68%, respectively. It was concluded that lentils have great potential as a whole food source of Fe and Zn for people affected by these nutrient deficiencies. This is the first report on the genetic basis for Fe and Zn micronutrient content in lentils. These results provide some understanding of the genetic basis of Fe and Zn concentrations and will allow for the development of potential strategies for genetic biofortification.
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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".