Role of Salicylic Acid in Alleviating Cobalt Toxicity in Wheat (Triticum aestivum L.) Seedlings
Bibliographic record
Abstract
Heavy metals pollution of soils become the most serious environmental problem resulted in decreased soil fertility and crop yield losses. Cobalt (Co), as a beneficial element, can be a contaminant in soils due to agricultural additives or metal refineries, it causes irreversible damage to several physiological and metabolic constituents at higher concentrations. In this investigation we study the effects of different Co concentrations (0, 50,150, 250, 350 µM as CoCl2) on growth and physiological processes in wheat (Triticum aestivum) plants and discuss the role of spraying with salicylic acid (0.5 mM) for ameliorating effect of cobalt toxicity in wheat leaves. Cobalt causing a significant reduction in fresh and dry biomass, shoot height, water content and total photosynthetic pigments. This was accompanied with accumulation of H2O2 and MDA contents and induction of some antioxidant enzymes activities. There was an increase in SOD activity with increasing Co concentration then decline, a significant increase in APX and PPO activities while CAT was significantly declined. Spraying wheat seedlings with salicylic acid can be recognized to significantly increase in all tested growth parameters and that was attributed to decrease, to some extent, the accumulation of H2O2 and MDA and improve the activity of antioxidant enzymes.
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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".