The Response of Cowpea (Vigna unguiculata) Plants to Three Abiotic Stresses Applied with Increasing Intensity: Hypoxia, Salinity, and Water Deficit
Bibliographic record
Abstract
Exposing plants to gradually increasing stress and to abiotic shock represents two different phenomena. The knowledge on plants’ responses following gradually increasing stress is limited, as many of the studies are focused on abiotic shock responses. We aimed to investigate how cowpea (Vigna unguiculata (L.) Walp.) plants respond to three common agricultural abiotic stresses: hypoxia (applied with the increasing time of exposure to nitrogen gas), salinity (gradually increasing NaCl concentration), and water deficit (gradual decrease in water supply). We hypothesized that the cowpea plants would increase in tolerance to these three abiotic stresses when their intensities rose in a stepwise manner. Following two weeks of treatments, leaf and whole-plant fresh weights declined, soluble sugar levels in leaves decreased, and lipid peroxidation of leaves and roots and the levels of leaf electrolyte leakage increased. Polyphenol oxidase activity in both roots and leaves exhibited a marked increase as compared to catalase and peroxidase. Leaf flavonoid content decreased considerably after hypoxia, while it increased under water deficit treatment. NO emission rates after 3 h in the hypoxically treated plants were similar to the controls, while the other two treatments resulted in lower values of NO production, and these levels further decreased with time. The degree of these changes was dependent on the type of treatment, and the observed effects were more substantial in leaves than in roots. In summary, the responses of cowpea plants to abiotic stress depend on the type and the degree of stress applied and the plant organs.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".