Plant Growth and Antioxidative Enzymes in Sunflower Supplemented With Selenium
Bibliographic record
Abstract
The use of soil additives such as selenium can positively influence the antioxidative system of plants, making them more tolerant to abiotic stresses. The aim of this work was to evaluate the concentration of sodium selenite or sodium selenate applied to the substrate that causes improve in plant growth and antioxidative enzyme activities in sunflower plants. The treatments were divided in: control (absence of selenium); 0.2 mg L-1 of sodium selenate; 0.4 mg L-1 of sodium selenate; 0.8 mg L-1 of sodium selenate; 0.2 mg L-1 of sodium selenite; 0.4 mg L-1 of sodium selenite and 0.8 mg L-1 of sodium selenite. The analysis of Shoot Dry Mass (SDM) production and activities of the antioxidantive enzymes: Ascorbate Peroxidase (APX), Guaiacol Peroxidase (GPX) and Catalase (CAT) was performed. For SDM and APX the concentration of 0.8 mg L-1 of sodium selenite caused higher values. CAT showed greater activity in treatments that received 0.4 and 0.8 mg L-1 of sodium selenate and 0.4 and 0.8 mg L-1 of sodium selenite than the control treatment. GPX showed superior activity in the treatments 0.8 mg L-1 of sodium selenate, 0.2 mg L-1 of sodium selenite and 0.8 mg L-1 of sodium selenite than the control treatment. It was concluded that selenium promoted improvements in the antioxidant activity and in the production of shoot dry mass of sunflower plants.
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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) | 0.001 | 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) | 0.001 | 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".