Raw Extracts of Wild Plants Improve the Agronomic and Biochemical Quality of Tomato Fruits (Lycopersicum esculentum Mill.)
Bibliographic record
Abstract
This study aimed to determine the effect of 10 and 15% concentrations of Azadirachta indica oil (v/v) and Tithonia diversifolia and Thevetia peruviana liquid manure (w/v) on some key characteristics of tomato fruits. For this purpose, the extracts were sprayed on the tomato plants every two weeks until the fruits were harvested. The results show that the 15% T. diversifolia mash had the most significant positive impact (p < 0.05) on the size, weight, and ability of tomato fruits to protect DNA from denaturation. Indeed, compared to fruits harvested from untreated plants, this treatment increased the surface area by 75.38%, the weight by 72.74%, and the protective capacity of fruits against hydrogen peroxide-induced DNA denaturation by 82.96%. On the other hand, the highest lycopene content was obtained with A. indica at 10% (139.13 ± 4.35 μg/g MF), and that of phenols was observed with T. peruviana at 10% (31.07 ± 1.06 mg eq catechin/g MF). Also, there is a positive and significant correlation (p < 0.05) between phenol content and DPPH (2.2-diphenyl-1-picrylhydrazyl) and FRAP (ferric reducing antioxidant power) free radical scavenging activities of tomato fruits. Thus, this study shows that wild plant extracts are able to improve fruit quality.
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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".