Analysis of flavonoids and phenolic acids in grass of Desmodium canadense (L) DC
Bibliographic record
Abstract
Plants have many useful substances – and one of them is flavonoids with other phenolic compounds. This is a very big group of biological active compounds. Flavonoids have been referred to as "nature's biological response modifiers" because of their ability to modify the body's reaction to other compounds such as allergens, viruses, and carcinogens. They show anti-allergic, anti-inflammatory, and anti-cancer activity. In addition, flavonoids act as powerful antioxidants, providing remarkable protection against oxidative and free radical damage. As a result, consumers and food manufacturers have become increasingly interested in flavonoids for their healthful properties, especially their potential beneficial role in the prevention of cancer and cardiovascular diseases. Phenolic acids are interesting of their protective role against oxidative damage diseases (coronary heart disease, stroke, and cancers). One of possible sources of flavonoids and phenolic acids – Canadian thick-trefoil (showy trefoil) – Desmodium canadense (L) DC. This plant is not researched as good as many other plants, but there are some works on it. In Lithuania, KUM, there were some research works on Desmodium canadense (L.) DC. There was performed analysis of grass, collected in different phase of vegetation. Also was researched influence of mineral fertilising to amounts of flavonoids and phenolic acids.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".