Bibliographic record
Abstract
Ghrita is a popular milk product prepared indigenously in most households and is widely available commercially. It has high nutraceutical values. Ghrita by its nature has; Madhura rasa (sweetish taste), Madhura vipaka (post-digestion sweet taste), Laghu (easy to digest), Sheet virya (cold in potency). Ayurveda proposes certain rules for its consumption and specifies some adjuvant to contradict its ill effects. It is used as Pathya (diet) aahar in diseases as well as, an important ingredient in various medicinal formulations. With the advent of Urbanisation, Industrialisation and increasing work culture, human lifestyle and food habits have been drastically changed. Because of thesechanges, the population is gradually suffering from many nutritional deficiencies leading to a large number of metabolic and degenerative diseases. In recent years, an innovative pharmaceutical product, “Nutraceutical” has made a special place in the field of nutritional supplementation which can be correlated to Pathya Kalpana in Ayurveda. It not only provides health benefits but is also used forthe prevention and treatment of acute and chronic diseases. The present study aims to reveal the Ayurvedic perspective of Nutraceuticals with special reference to Ghrita by carrying out the pharmaceutical procedure and qualitative analysis.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".