Effect of Papaya Skin Extract on Growth and Yield of Mungbean Under Drought Stress Conditions
Bibliographic record
Abstract
Dryness is among the limiting abiotic factors of plant productivity.Papaya skin extract contains phytochemical antioxidants, such as phenolic compounds, flavonoids, and vitamin C. The study aimed to analyze how papaya skin extract affects mungbean grown under drought stress conditions.The experiment was arranged in a factorial randomized block design.The first factor was drought stress consisting of 3 levels (100% Field Capacity (FC) = control, 70% FC = mild stress, and 40% FC = medium stress), the second factor was papaya skin extract consisting of 4 concentration levels (0%, 1%, 2%, and 3%), with 3 replicates.The results of the study showed a significant interaction effect of papaya skin extract and drought stress on pod size and the number of seeds (p = 0.05).Independently, the growth rate and the dry seed were influenced by drought stress.An increase in drought stress from 100% to 40% FC resulted in increased stunted growth, as reflected in the decrease in plant height and leaf area, as well as a decrease in seed yield to 28.3% compared to the control.In addition, the components of growth and yield were significantly affected (p = 0.05) by papaya skin extract.In comparison to the control, the use of papaya skin extract improved plant growth and potentially increased seed yields by 11.2%.These findings suggest that papaya skin extract has the potential to mitigate drought stress in mungbean cultivation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".