Gene discovery in cereals through quantitative trait loci and expression analysis in water‐use efficiency measured by carbon isotope discrimination
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
Drought continues to be a major constraint on cereal production in many areas, and the frequency of drought is likely to increase in most arid and semi-arid regions under future climate change scenarios. Considerable research and breeding efforts have been devoted to investigating crop responses to drought at various levels and producing drought-resistant genotypes. Plant physiology has provided new insights to yield improvement in drought-prone environments. Crop performance could be improved through increases in water use, water-use efficiency (WUE) and harvest index. Greater WUE can be achieved by coordination between photosynthesis and transpiration. Carbon isotope discrimination (Δ(13) C) has been demonstrated to be a simple but reliable measure of WUE, and negative correlation between them has been used to indirectly estimate WUE under selected environments. New tools, such as quantitative trait loci (QTL) mapping and gene expression profiling, are playing vital roles in dissecting drought resistance-related traits. The combination of gene expression and association mapping could help identify candidate genes underlying the QTL of interest and complement map-based cloning and marker-assisted selection. Eventually, improved cultivars can be produced through genetic engineering. Future efficient and effective breeding progress in cereals under targeted drought environments will come from the integrated knowledge of physiology and genomics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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