Plant Growth, Antioxidative Enzymes and Lipid Peroxidation in Sunflower Seedlings Supplemented With Eichhornia crassipes Organic Fertilizer Under Drought Stress Conditions
Bibliographic record
Abstract
In the semiarid region of the Brazilian Northeast, there is still the occurrence of soils with low concentrations of organic mass and nutrients. Eichhornia crassipes (water hyacinth) is recognized as one of the top ten endemic herbs in the world. However, its accumulation capacity means it can be an alternative source of nutrients. The objective of this study was to analyse the effects of macrophyte organic residue (ROM) on plant growth, antioxidative enzyme activity and membrane lipid peroxidation in leaves and roots of sunflower seedlings submitted to drought stress conditions. The experiment was conducted under greenhouse conditions at the Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Maracanaú Campus, Brazil. Samples of E. crassipes were collected in the Parangaba Lagoon, Fortaleza, Ceará, Brazil. The treatments were: 1) 100% (by volume) sand; 2) sand + fertilizer (following the recommendation of 80 kg of N/ha), and 3) sand + 100% of the recommended nitrogen in macrophytes (RN). In general, the use of ROM caused better seedling growth in relation to the other treatments in all conditions studied. Increases in antioxidative enzyme activity and reductions in the deleterious effects of drought stress on plant growth were observed.
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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 | 0.000 |
| 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".