Legume endosymbionts: Drought stress tolerance in second‐generation chickpea (<i>Cicer arietinum</i>) seeds
Bibliographic record
Abstract
Abstract Chickpea is an important leguminous crop grown worldwide due to its nutritional and economic value. However, abiotic stress, primarily caused by drought, has limited chickpea production. This study highlights endosymbiotic plant growth promotion as well as alleviation of abiotic stress in germinating chickpea seeds and seedlings under drought stress conditions. Seed produced by F1 endosymbiotic plants under controlled environment was used to conduct this second‐generation (F2) study in the greenhouse. Fungal and bacterial endosymbionts improved seed germination and enhanced root and shoot growth in second‐generation seeds produced by applying drought stress without endophytes. Expression levels of antioxidant genes, proline, SOD‐superoxide dismutase and dehydrin, were downregulated, which characterizes enhanced oxidative stress tolerance and reduced reactive oxygen species (ROS) in host cells. The endosymbiont beneficial effect on plant resilience and improved phenotypes was translated into increased nutrient quality of second‐generation seed. This study indicates the potential of the fungal and bacterial endosymbionts to moderate drought stress in plants by triggering epigenetic changes inherited across chickpea generations which correlated with enhanced resilience and improved agricultural traits in this globally important crop.
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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".