Genetic Variation for Salinity Tolerance in Pakistani Rice (<i>Oryza sativa</i> L.) Germplasm
Bibliographic record
Abstract
Abstract Soil salinity is one of the major production constraints. Development and planting of salt‐tolerant varieties can reduce yield losses due to salinity. We screened 185 rice genotypes at germination stage in petri dishes under control, 50, 100 and 150 mm salt stress, and at seedling stage in Yoshida's hydroponic nutrient solution under control, 50 and 100 mm salt stress. At germination stage, 15 genotypes including Nona Bokra, Sonahri Kangni, 7421, 7423 and 7467, whereas at seedling stage, 28 genotypes including Nona Bokra, Jajai‐77, KSK‐133, KSK‐282, Fakhr‐e‐Malakand, Pakhal, IR‐6, Khushboo‐95, Shahkar and Shua‐92 were found salt tolerant. Basmati‐370, Mushkan, Homo‐46 and accessions 7436, 7437 and 7720 were sensitive to salinity at both germination and seedling stage. We further screened a subset of 33 salt‐tolerant and salt‐sensitive genotypes with SSR markers. Four SSR markers (RM19, RM171, RM172 and RM189) showed significant association with two or more of the studied traits under 50, 100 and 150 mm salt stress. These markers may be further tested for their potential in marker‐assisted selection. The salt‐tolerant genotypes identified in this study may prove useful in the development of salt‐tolerant rice varieties in adapted genetic background.
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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".