The Effects of some Compounds Found in Aronia and Goji Berries on Human Health
Bibliographic record
Abstract
Berries consumption is a current concern in order to highlight the important content of their compounds on human health. Aronia berry is known as chokeberry and is native in North America and Canada. Due to the therapeutic properties, black chokeberries are very popular and appreciated fruits. The most important compounds, polyphenols and antioxidants, have many biological actions such as antioxidative, anti-inflammatory, hypotensive, antiviral, anticancer, antidiabetic and antiatherosclerotic effects Goji berry is also another important source of carotenoids and antioxidants and it has been used as a medicine in China for centuries. Goji berry is considered as a super fruit due to bioactive compounds that offer protection against cardiovascular diseases, diabetes and other comorbidities. Black chokeberry and goji berry have an antioxidant capacity ten times higher than other berries. Due to their high biological and nutritional value, these berries are being used more and more frequently in human nutrition and the extracts in the pharmaceutical industry. Therefore, the main aim of this paper was to summarize and highlight the high content of bioactive compounds and beneficial effects of aronia and goji berries upon human health.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".