A Comparison Study On Anti-Microbial Activities Of Green Tea And Black Tea Leaves (Camellia Sinensis L) Extract
Bibliographic record
Abstract
Tea comes loaded with antioxidants and compounds like polyphenols and catechins that can fend off free radicals and reduce the risks of chronic diseases.Many tea leaves also come stuffed full of vitamins and minerals along with antiinflammatory properties and immune system boosters.Tea is not only a popular drink but also a drink with refreshing and functional properties.Thus, the green and black tea leaves were collected and used for preparing green and orthodox black tea to study Anti-microbial activity.The contemporary scientific community has presently recognized flavonoids to be a unique class of therapeutic molecules due to their diverse therapeutic properties.Of these, rutin, also known as vitamin P or rutoside, has been explored for a number of pharmacological effects.Tea leaves, apples, and many more possess rutin as one of the active constituents.Today, rutin has been observed for its nutraceutical effect.The present study highlights the anti-microbial effects of green and black tea leaves extract.
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.
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) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".