Genetic Analysis And Character Association In Bread Wheat (Triticum Aestivum L.) Under Terminal Heat Stress
Bibliographic record
Abstract
Wheat is a thermo-sensitive long day crop. High temperature during reproductive and grain filling stages causes significant yield losses to wheat. The characters affecting yield under heat stress are predicted by correlation analysis. In this study 25 germplasm, developed and recommended by the International Centre for Agricultural Research in the Dry Areas, Beirut and Lebanon were selected for performing genetic analysis and character association study under terminal heat stress. Field emergence, Days to 50% heading, Days to maturity, Plant Height(cm), Number of effective tiller/Plant, Spike length(cm), Biomass(kg), Thousand grain weight(g) and Yield(g/m²) were estimated for genetic parameters. All of the 13 morphological traits were found statistically significantly different among the 25 genotypes. Grain yield was found highest in Terbol followed by 29872(200.33 g/m2 ), 29502(154.00 g/m2 ) and 29821(147.09 g/m2 ).The PCV values were higher than the GCV values in all cases, indicating the influence of the environmental effect on the expression of these characters. Thousand Grain Weight, Relative Water Content and Biomass were found with high heritability coupled with high genetic advance, so selection may be rewarded based on those features. 29945, 29610, 30140, 29690, 29992, 29703, 29872, 29502 have been identified as the most promising germplasm for terminal heat tolerance, which can be used in future breeding programmes
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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".