Meta-Analysis of Sweet Potato Storage Methods and Their Impact on Quality
Bibliographic record
Abstract
Sweet potato is a globally significant staple crop known for its high nutritional value, yet maintaining quality during storage remains a persistent challenge. This study reviews traditional and modern storage techniques, comparing their effectiveness in preserving nutritional, physical, and sensory qualities, as well as extending shelf life across diverse regions. Employing rigorous selection criteria, we synthesized data from various studies, analyzing the influence of temperature, humidity, variety, and pre- and post-harvest practices on quality outcomes. Findings reveal that modern storage innovations, particularly those with controlled temperature and humidity, significantly improve quality preservation compared to traditional methods. However, the accessibility and environmental impact of these methods vary, raising considerations for sustainability and cost. The case study included highlights regional storage practices and their outcomes, offering insights for broader application in sweet potato cultivation areas. This analysis underscores the need for further research to address data gaps, optimize storage methods for different sweet potato varieties, and develop cost-effective, sustainable solutions for quality maintenance. Future research should explore the integration of modern storage methods with regional practices, providing a pathway to enhance storage efficacy for improved nutritional and economic value of sweet potatoes globally.
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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 | 0.032 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.036 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".