Detection for Salt Tolerance Character in Two Selected Genotypes of Wheat
Bibliographic record
Abstract
Using the genetic variation to improve salt tolerance in some wheat genotypes is a very important achievement to increase the production of salinized soils. The aim of this study is to determine the realized improvement in salt tolerance in some selected genotypes of wheat, which induced through plant breeding programs. The selected genotypes were derived from F2 populations after exposed to high salinity condition for six cycles of screening and selection. Salt tolerance of these selected genotypes (2H, N3) was tested during germination, early seedling and tillering stages in salinilized soils with three salinity levels (2, 8 and 15 ds/m) as compared with the local cultivar (Tamooze-2). Results showed that all selected genotypes were significantly superior in shoots and roots growth at 8 and 15 ds/m to those of the local cultivar. At all salt levels, the highest reduction in shoots and roots growth was in the local cultivar. Results also indicated that the highest values of K+/ Na+ and ca+2/ Na+ ratios were in the selected genotypes (2H, N3) at 8 and 15 ds/m. therefore, significant improvement in salt tolerance was achieved in the selected genotypes through plant breeding programs. The conclusion of these results is the salt tolerance of the selected genotypes correlated with the highest values of K+/ Na+ and ca+2/ Na+ ratios in their upper leaves, then the selected genotypes were more salt tolerance than local cultivar, which had the lowest K+/ Na+ and ca+2/ Na+ ratios in upper leaves.
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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) | 0.000 | 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".