Development and molecular characterization of wheat –<i>Aegilops kotschyi</i>addition and substitution lines with high grain protein, iron, and zinc
Bibliographic record
Abstract
Over two billion people, depending largely on staple foods, suffer from deficiencies in protein and some micronutrients such as iron and zinc. Among various approaches to overcome protein and micronutrient deficiencies, biofortification through a combination of conventional and molecular breeding methods is the most feasible, cheapest, and sustainable approach. An interspecific cross was made between the wheat cultivar 'Chinese Spring' and Aegilops kotschyi Boiss. accession 396, which has a threefold higher grain iron and zinc concentrations and about 33% higher protein concentration than wheat cultivars. Recurrent backcrossing and selection for the micronutrient content was performed at each generation. Thirteen derivatives with high grain iron and zinc concentrations and contents, ash and ash micronutrients, and protein were analyzed for alien introgression. Morphological markers, high molecular weight glutenin subunit profiles, anchored wheat microsatellite markers, and GISH showed that addition and substitution of homoeologous groups 1, 2, and 7 chromosomes of Ae. kotschyi possess gene(s) for high grain micronutrients. The addition of 1U/1S had high molecular weight glutenin subunits with higher molecular weight than those of wheat, and the addition of 2S in most of the derivatives also enhanced grain protein content by over 20%. Low grain protein content in a derivative with a 2S-wheat translocation, waxy leaves, and absence of the gdm148 marker strongly suggests that the gene for higher grain protein content on chromosome 2S is orthologous to the grain protein QTL on the short arm of group 2 chromosomes.
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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".