Review: Breeding wheat for enhanced micronutrients
Bibliographic record
Abstract
Xu, Y., An, D., Li, H. and Xu, H. 2011. Review: Breeding wheat for enhanced micronutrients. Can. J. Plant Sci. 91: 231–237. Low concentrations or deficiencies of bioavailable iron (Fe), zinc (Zn) and other essential micronutrients in human food afflict a large proportion of the world's population. Plant biofortification, to improve the mineral concentrations in the edible portions of crop plants by conventional breeding or modern transgenic approaches, is regarded as the most economical and sustainable strategy. Many researchers have demonstrated that there are significant differences in grain mineral element concentrations among wheat (Triticum aestivum L.) and its relatives. Compared with cultivated wheat, wild wheats are potential genetic resources for enhancing micronutrient in wheat grain. An ancestral wild tetraploid wheat (T. turgidum ssp. dicoccoides) carrying the allele Gpc-B1, which is associated with increased Fe, Zn, and protein concentrations in grain, was cloned using a positional cloning strategy. Combining conventional breeding with modern genetic engineering approaches, such as introgression of genes from wild relatives into wheat, synthetic hexaploid wheat, quantitative trait locus (QTL) analysis, and even gene cloning and genetic transformation, are important for developing wheat cultivars higher in micronutrients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".