Evaluation of Drought Tolerant in Some Wheat Genotypes to Post-anthesis Drought Stress
Bibliographic record
Abstract
Water deficit is the major cause of wheat (Triticum aestivum L.) yield losses in Iran and many other regions where the crop is not normally irrigated. The aim of the present study was to evaluate the ability of several selection indices to identify drought-resistant wheat genotypes. Twenty-one bread wheat genotypes were evaluated under two field experiments (post-anthesis drought stress and normal conditions). The experiments were arranged in a randomized complete block design with three replications in two successive growing seasons (2007/2008 and 2008/2009). The results showed that yields in the normal conditions were positively correlated with yields in the stress conditions. Several genotypes with good performance under both conditions were identified. Correlation analysis indicated that the most suitable drought tolerance criteria for screening substitution genotypes were mean productivity (MP), geometric mean productivity (GMP) and stress tolerance index (STI) (Group A genotypes) and when the stress was severe, stress susceptibility index (SSI) was found to be more useful index in discriminating resistant genotypes. Based on different drought indices, genotypes G4, G14 and G9 had the best ranking. In addition bi-plot and cluster analysis cleared superiority of these three genotypes in both seasons.
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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 | 0.000 |
| 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 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".