HEALTH BENEFITS AND THERAPEUTIC EFFECTS OF GREEN LEAFY VEGETABLES
Bibliographic record
Abstract
Green leafy vegetables are under incredible pressure by humans even from the advancement in civilization. Vegetables have the ability of surviving even after facing the harsh environmental conditions of scarcities and drought. In rural areas as compared to urban areas GLVs are the main source of nutrition because exotic species cannot be easily available here, and also these GLVs provide better nutrition as compared to the costly exotic species. Essential amino acids, minerals, fiber and vitamins are present in GLVs to fulfill instant nutritional requirements. Pharmacological and medicinal importance of GLVs is due to the presence of chemical constituents. Consumption of GLVs is health beneficial for reducing risks of specific diseases like hepatotoxicity and cancer. Compounds having the anti-histaminic, anti-diabetic and anti-carcinogenic and hypo-lipidemic characteristics are found in excess in GLVs. To improve the body's defense system, restoring and healing capacity against the hypertension, insomnia, obesity, aging and cardiovascular diseases (CVDs) the consumption of these vegetables is necessary because there are found phytochemicals and a massive quantity of antioxidants in GLVs, which can overcome the nutritional and other health problems by improving the nutritional status of human beings. In conclusion it can be claimed that GLVs have the ability to be utilized and applied in numerous health beneficial purposes and development of different value added food products as used in their raw form in different regions of the world focusing on their benefits.
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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 | 0.000 | 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) | 0.005 | 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".