EMERGING SCOPE FOR FUNCTIONAL FOODS- GLOBAL APPROACH
Bibliographic record
Abstract
Foods or food ingredients that provide a health benefit beyond normal nutritional effects through modulation of specific target functions are generally known as functional foods. All foods are functional to some extent as they provide taste, aroma, nutrients required for normal metabolism, growth and maintenance. However, foods are now being examined intensively because of latest trend towards preventive health care for added physiological, psychological and specific health benefits, which may reduce chronic disease risk and optimize health. Nutrients, herbals, fish, meat, dairy products and dietary supplements are major constituents of functional foods. India is the home of a large number of medicinal herbs, spices and tree species that have a substantially large domestic market with lesser foreign competitors at present. Over a long period, there were no strict pharmaceutical regulations on Ayurvedic and nutraceutical products in India. In the present scenario, nutraceuticals and functional foods industries have grown in to multi-million dollar industries. It is estimated that Canadian functional food industry is likely to grow up to $50 billion US dollars. Japan is reported to have the second largest functional food and neutraceutical markets in the world.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.006 |
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".