Agroeconomic Production and Antioxidant Activity of the Hibiscus Cultivated Under Organic Practices and Plant Arrangements
Bibliographic record
Abstract
In order to obtain better productivity and a good level of secondary metabolites for cultivating plants with organic residues, it is necessary to define the method of application and the best combination considering plant density. Our objective was to study the influence of the plant arrangement and methods of application of poultry manure in the soil over agroeconomic performance and the antioxidant activity of the hibiscus. Treatments in the field consisted of two plant arrangements (single or double rows, both with 0.50 meters between plants) and four methods of application of poultry manure to the soil [incorporating (10 t ha-1), mulching (10 t ha-1), mulching (5 t ha-1) + incorporating (5 t ha-1) and a control—no poultry manure] and, in the laboratory, antioxidant activity in relation to the treatments in the field and preparation methods (maceration or infusion). Treatments were arranged in randomized blocks of 2 × 4 with four replicates. The greatest production of dried calyx (0.68 t ha-1) and of capsules (1.32 t ha-1), the greatest number of fruits (2.10 million ha-1) and the greatest net income (R$ 36,115.42) resulted of plants cultivated in single rows with the poultry manure covering the crops. The antioxidant activity of the hibiscus calyces showed no variation in relation with field treatments or with the method of preparation. Cultivation in single rows covered with poultry manure is adequate for the hibiscus plant.
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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".