Genotypic Variability and Genotype × Environment Interaction for Iron and Zinc Content in Lentil under Nepalese Environments
Bibliographic record
Abstract
To ascertain the variability in Fe and Zn concentrations in lentil (Lens culinaris Medikus ssp. culinaris) seeds, a set of 58 lentil genotypes were evaluated in randomized complete blocks at eight locations in Nepal during 2006 to 2012. Micronutrient contents of the seeds were analyzed. The mean Fe varied from 72.4 μg g−1 at Surkhet in 2009 to 98.2 μg g−1 at Rampur in 2012, and the mean Zn from 23.9 μg g−1 at Surkhet in 2012 to 85.1 μg g−1 at Parwanipur in 2009. The genotype effect and genotype × location interaction were significant for Fe (P = 0.01–0.03). For Zn, the genotype effect was significant (P = 0.008), and the genotype × location interaction was not (P = 0.46). The variance component estimates for the genotype × year interaction within locations were zero for both the minerals, indicating genotypic stability over the years. The best line for Fe was ILL7723 (81.0 μg g−1) and RL6 for Zn (56.2 μg g−1). The lines that were within the top 20% for high concentrations of both minerals were Barimasoor4, RL6, RL9, ILL8006, RL11, RL12, ILL9926, and ILL6819. The lowest concentrations of the minerals were found in Shital for Fe (74.2 μg g−1) and Black musuro for Zn (51.4 μg g−1). These results provide a useful foundation for the development of new lentil cultivars that have high mineral content and could be used to develop more nutritious varieties of lentil and reduce mineral element deficiencies in Nepal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".