Exogenous selenium application enhances the photosynthetic pigment and antioxidant defense of mash bean (Vigna mungo) to confer tolerance to salt stress
Bibliographic record
Abstract
Mash bean is an important crop enriched with proteins and highly praised in Pakistan due to its nutritional values. However, due to abiotic stresses like salinity, its production is decreased. This study was conducted to investigate the effect of selenium on mash bean to produce salt tolerance. Mash bean seeds were sown in plastic pots filled with sand. Three levels of NaCl (0 mM, 100 mM, 200 mM) and five levels of selenium (0 ppm, 20 ppm, 40 ppm, 80 ppm, 120 ppm) were applied with Hoagland solution. Salinity reduced plant height (28%), leaf area (33%), chlorophyll a (14%), chlorophyll b (9%), carotenoids (20%), potassium ions, calcium ions, superoxide dismutase, peroxidases, catalase, salt tolerance index and increased sodium ions (21%), H2O2 content as well as secondary metabolites. However, selenium application in low concentration enhanced plant height (31%), leaf area, chlorophyll a (17%), chlorophyll b (12%), carotenoids (40%), potassium, calcium, superoxide dismutase, peroxidases, catalase, salt tolerance index, proline, flavonoids, total phenol, while decreased sodium ions (25%) and hydrogen peroxide content under salt stress. Findings showed important function of selenium in improving physical characteristics, absorption of ions, photosynthetic pigments, and antioxidant defense in plants under salinity. Applying selenium at 40 ppm concentrations showed greatest efficacy in alleviating negative impacts of salt stress (100 mM) on plant growth and biochemical attributes. Maximum positive results of selenium application (40 ppm) were obtained at 0 mM of salinity.
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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".