Response Estimation of Wheat Synthetic Lines to Terminal Heat Stress Using Stress Indices
Bibliographic record
Abstract
Twenty eight synthetic wheat lines were evaluated for terminal heat tolerance by normal (non-stress) and late (stress) planting in field in randomized block design with three replications for two crop seasons; 2008-09 and 2009-10. The genotypes differed significantly for thousand grain weight in non-stress and stress conditions. The stress susceptibility and tolerance indices were calculated for thousand grain weight and genotypes differed significantly for stress indices also. The stress tolerance indices; Stress tolerance index, Geometric mean production and Mean production had significant positive correlation with thousand grain weight in non-stress and stress conditions. The first two principal components explained more than 89 and 98% of variation during two crop seasons respectively. The study using bilpot analysis revealed that stress tolerance indices can be selection criteria for identification of tolerant genotypes. Using three dimensional plot, the genotypes which performed well in both environments or in one of the environments only or in none of the environments were identified. The synthetic wheat lines S9, S37, S44 and S57 had high thousand grain weight with heat tolerance during both years and genotypes S8, S22, S23, S49 and S77 had poor performance in both environments.
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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 | 0.000 | 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) | 0.001 | 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".