Polyamines-mediated regulation of enzymatic antioxidative response to excess soil moisture in soybean (Glycine max L.)
Bibliographic record
Abstract
Excess soil moisture creates an oxygen deficient growth environment that adversely affects plant growth and has led to greater agricultural losses than any other abiotic stress factor affecting the Canadian prairies, especially Manitoba. The hypoxic environment limits cellular respiration in root tissues and disrupts the antioxidative systems of plants, resulting in accumulation of reactive oxygen species which react with and damage essential cellular components. The antioxidative capacity of plants and therefore their tolerance to stress conditions such as excess moisture can be improved by treatments with growth regulators such as polyamines, which play important roles in mediating plant response to their environment. This thesis investigated the role of treatments with polyamines in improving seedling growth and enzymatic antioxidant capacity in the tissues of seedlings and young vegetative soybean plants under excess soil moisture conditions. The results indicate that treatment with polyamines can improve the growth of excess moisture-stressed seedlings and this is associated with an enhanced antioxidative response with improved expression of antioxidative genes and activity of the corresponding enzymes. Furthermore, the results of this study indicate that polyamine treatments also increase the enzymatic antioxidant capacity in the leaf tissues of young soybean plants exposed to excess moisture.
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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".