Comparative effects of gibberellic acid, kinetin and salicylic acid on emergence, seedling growth and the antioxidant defence system of sweet sorghum (Sorghum bicolor) under salinity and temperature stresses
Bibliographic record
Abstract
Salinity and high temperature are major abiotic stresses limiting sustainable crop production. Seed priming is a useful tool to enhance seedling growth and the antioxidant defence system of crops under salinity and temperature stress. This experiment was designed to determine the effects of gibberellic acid (GA3, 288.7 µm), kinetin (232.2 µm) and salicylic acid (362 µm) on some morphological and physiological parameters of sweet sorghum (Sorghum bicolor L. Moench) hybrid Yajin 13 under salinity (0, 100 and 200 mm NaCl) and temperature (25°C and 37°C) stress. Salinity and high temperature significantly reduced emergence percentage, shoot and root lengths, number of leaves, shoot fresh and dry weight, and chlorophyll a and b content. The activity of superoxide dismutase (SOD) and malondialdehyde (MDA) content were increased with an increase in both salinity and temperature stress. Hormone treatments positively affected all parameters except root fresh and dry weight, number of leaves, SOD activity and chlorophyll a. Under salinity stress at 200 mm NaCl, treatment with salicylic acid increased emergence percentage, emergence rate, chlorophyll b and protein content by 82.0%, 130%, 7.9% and 1.9%, respectively, relative to the control (no treatment). At 37°C, salicylic acid increased emergence percentage, emergence rate and number of roots by 72.5%, 108.5% and 63.8%, respectively, and decreased MDA content by 17.6% relative to the control. Our study indicated that seed priming with an appropriate concentration of exogenous hormones (salicylic acid, kinetin, GA3) is a useful, easy method for improving germination, seedling growth and the antioxidant defence system of sweet sorghum under conditions of high temperature and 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".