Identification of novel source of salt tolerance in local bread wheat germplasm using morpho-physiological and biochemical attributes
Bibliographic record
Abstract
Abstract Salt tolerant wheat cultivars may be used as genetic resource for wheat breeding to ensure yield stability in future. The study was aimed to select salt tolerant cultivar(s) to identify novel source of salt tolerance in local wheat germplasm. Initially, 40 local wheat cultivars were screened at 150 mM NaCl stress at seedling stage. Selected salt-tolerant (three; S-24, LU-26S and Pasban-90) and salt-sensitive (four; MH-97, Kohistan-97, Inqilab-91 and Iqbal-2000) wheat cultivars were further evaluated using growth, yield, biochemical and physiological attributes. Growth and yield of selected cultivars were reduced under salt stress due to decline in plant water status, limited uptake of macronutrients (N, P and K), reduced K + /Na + ratio, photosynthetic pigments and quantum yield of PSII. Wheat plants tried to acclimate salt stress by osmotic adjustment (accumulation of total soluble sugars, proline and free amino acids). Degree of salinity tolerance in cvs. S-24 and LU-26S found to be associated with maintenance of K + /Na + ratio, osmo-protectant and photosynthetic activity and can be used as donor for salt tolerance in wheat breeding program at least in Pakistan. These cultivars can be further characterized using molecular techniques to identify QTLs/genes for salt exclusion, osmo-protectant and photosynthetic activity for molecular breeding.
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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) | 0.002 | 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".