Innovative Rice Seed Coating (Oryza Sativa) with Polymer Nanofibres and Microparticles Using the Electrospinning Method
Bibliographic record
Abstract
Seed treatments are chemical or biological substances that are applied to seeds to control infection by disease-causing organisms, insects, or other pests. Seed treatment reduces production costs of seedlings, reduces the consumption of seeds, facilitates mechanization of sowing and improves the seedling establishment. The generation of nanofibres and microcapsules by the electrospinning technique is a novel approach for active ingredient controlled release. The study evaluates an innovative rice seed coating (Oryza sativa) with polymer nanofibres and microparticles using this method. Materials and Methods: Polymer nanofibres and microcapsules were applied by the electrospinning technique to irrigated rice seeds. The treatments consisted of: 1) Control, 2) Negative control - Polymer based microcapsule without fungicide. 3) Polymer based microcapsule with fungicide. Microbiological assays and germination tests were performed following the guidelines of the Seed Analysis Rules of the Ministry of Agriculture. Results: The applied polymer as a coating did not affect the physiological quality of the seeds, as attested by the result of the germination tests, and they proved to be effective in the control of fungi disease in crop seeds. Conclusion: The germination and phytosanitary characteristics were improved in the analyzed study.
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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) | 0.000 | 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".