Bibliographic record
Abstract
Sweet potatoes ( Ipomoea batatas ) have received increasing attention in recent years due to their high nutrition and the variety of active substances they contain. In this article, we have sorted out the main components of different parts of sweet potatoes, such as leaves and roots, and focused on introducing dietary fiber, beta-carotene, anthocyanins, vitamin C, minerals, etc., as well as the possible benefits they may bring to health. Nowadays, there are numerous in vitro experiments, animal experiments, and A small number of human studies, all of which show that eating sweet potatoes may have many benefits, such as improving vitamin A status, regulating blood sugar and lipid levels, antioxidation, anti-inflammation, protecting the cardiovascular system, anti-cancer, and even helping intestinal health. Generally speaking, sweet potatoes with orange flesh contain A lot of beta-carotene, which is helpful in preventing vitamin A deficiency. Purple sweet potatoes have a high content of anthocyanins and a stronger antioxidant effect. Sweet potato leaves themselves are also a good source of protein, minerals and polyphenols, and have high nutritional value. Although sweet potatoes have been regarded as a crop with high nutritional density and the ability to promote health, there are still not enough high-quality human clinical studies at present. The mutual influence among different genotypes, environmental conditions and processing methods also requires further research. In the future, cooperation among different disciplines should be strengthened to enable sweet potatoes to play a greater role in functional foods, nutritional intervention and breeding, and to promote more innovation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".