Identification QTLs Controlling Genes for Se Uptake in Lentil Seeds
Bibliographic record
Abstract
Lentil (Lens culinaris Medik.) is an excellent source of protein and carbohydrates and is also rich in essential trace elements for the human diet. Selenium (Se) is an essential micronutrient for human health and nutrition, providing protection against several diseases and regulating important biological systems. Dietary intake of 55 μg of Se per day is recommended for adults, with inadequate Se intake causing significant health problems. The objective of this study was to identify and map quantitative trait loci (QTL) of genes controlling Se accumulation in lentil seeds using a population of 96 recombinant inbred lines (RILs) developed from the cross "PI 320937" × "Eston" grown in three different environments for two years (2012 and 2013). Se concentration in seed varied between 119 and 883 μg/kg. A linkage map consisting of 1,784 markers (4 SSRs, and 1,780 SNPs) was developed. The map spanned a total length of 4,060.6 cM, consisting of 7 linkage groups (LGs) with an average distance of 2.3 cM between adjacent markers. Four QTL regions and 36 putative QTL markers, with LOD scores ranging from 3.00 to 4.97, distributed across two linkage groups (LG2 and LG5) were associated with seed Se concentration, explaining 6.3-16.9% of the phenotypic variation.
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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 | 0.001 | 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".