Meta-Analysis of Sweet Potato Storage Methods and Their Impact on Quality
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
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.
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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| 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