Study of Post-Harvest Preservation Techniques for Loquat and its Application in Reducing Post-Harvest Losses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Loquat ( Eriobotrya japonica ) holds significant importance in agriculture due to its nutritional value and economic relevance. However, post-harvest losses remain a major challenge due to its rapid ripening and sensitivity to environmental factors such as temperature and humidity. This study provides a comprehensive analysis of the post-harvest physiology of loquat, including key factors contributing to spoilage. Traditional preservation techniques, such as cold storage, modified atmosphere packaging (MAP), chemical treatments, and dehydration methods, are discussed, along with emerging technologies like nano-coating, natural antimicrobials, and smart packaging systems. Through a comparative analysis of these methods' efficiency, economic viability, and environmental impact, this study also evaluates the integration of preservation techniques within supply chains and their role in reducing post-harvest losses through a regional case study. Lastly, this study explores future directions for loquat preservation, focusing on technological advancements, regulatory frameworks, and sustainability. The findings are expected to provide insights into improving post-harvest management and reducing loquat losses.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it