CHARACTERIZATION OF CIMMYT BREAD WHEAT GERMPLASM FOR RESISTANCE TO YELLOW RUST AND ENVIRONMENTAL FACTORS
Bibliographic record
Abstract
One of the major tasks faced by wheat breeders in Uzbekistan is to enhance wheat genotypes’ adaptability to soil and other environmental conditions and improve the grain quality, making the country self-sufficient in wheat grains and later becoming an exporter. Better results are achievable using the world wheat collection, including the CIMMYT germplasm. Determination of the positive correlation of physiological traits of new wheat cultivars, the importance of physiological indicators of water balance with productivity, and the vital role of these indicators in productivity level were the chief concerns. The timely study evaluated heat resilience, rust resistance, and grain yield in bread wheat genotypes. Bread wheat germplasm obtained from CIMMYT with a background of artificial infection of yellow rust incurred scrutiny for their rust resistance. A 15% incidence was detectable in seven genotypes; however, necrosis quickly formed around the symptoms of the disease in plant leaves, preventing its further development. Resistant wheat accessions, i.e., 1088, 1164 (R), 1006, and 1251 (MR), occurred as moderately resistant, showing high leaf area and grain yield. The average value of the genotypes was low, mainly due to the two rust-resistant samples, and the 1000-grain weight was 34.8 g and 34.6 g, respectively, and the grains per spike and grain yield were lower than the average. The chlorophyll a and b, total chlorophyll, carotenoid content, relative water content, flag leaf area, and production traits of the genotypes К-1088 and К-1164 gained assessment, revealed to be physiologically effective under the field conditions of Tashkent, Uzbekistan. Quantitative indicators of the productivity of these wheat genotypes indicated positive differentiation. Selection ensued for promising accessions to develop initial sources for producing the wheat genotypes with rust resistance and high grain yield under the environmental conditions of Uzbekistan.
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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 | 0.001 |
| 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".