Review: Breeding wheat for enhanced micronutrients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it