High Potential for Selenium Biofortification of Lentils (Lens culinaris L.)
Bibliographic record
Abstract
Beneficial forms of selenium (Se) and their impact on human health are a global topic of interest in public health. We are studying the genetic potential for Se biofortification of pulse crops to improve human nutrition. Lentils ( Lens culinaris L.) are an important protein and carbohydrate food and are a valuable source of essential dietary components and trace elements. We analyzed the total Se concentration of 19 lentil genotypes grown at eight locations for two years in Saskatchewan, Canada. We observed significant genotypic and environmental variation in total Se concentration in lentils and that total Se concentration in lentils ranged between 425 and 673 microg kg(-1), providing 77-122% of the recommended daily intake in 100 g of dry lentils. Over 70% of the Se was present as selenomethionine (SeMet) with a smaller fraction (<20%) as inorganic Se and very small amounts as selenocysteine (SeCys). We found that soils from the locations where the lentils were grown were rich in Se (37-301 microg kg(-1)) and that lentils grown in Saskatchewan have the potential to provide an excellent natural source of this essential element. Our analyses gave us a preliminary understanding of the genetic basis of Se uptake in lentil and indicated that any potential strategy for micronutrient biofortification in lentil will require choice of field locations that minimize the spatial variability of soil Se content.
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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 | 0.001 | 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".