ASSESSING THE FUNCTIONAL FOODS AND NATURAL HEALTH PRODUCTS INDUSTRY: A COMPARATIVE OVERVIEW
Bibliographic record
Abstract
Functional foods and natural health products (NHP) have become a relatively new component of the human diet with important policy implications. Increased consumer interest in healthier food products is driven by a variety of factors including growing awareness of the link between diet and health, the desire to age ‘gracefully ’ by maintaining good health, greater convenience in meeting nutritional needs and preventing chronic diseases such as diabetes, cancer, cardiovascular diseases and respiratory diseases. Consumers are more conscious of the maintenance of good health through diet, thereby gravitating towards foods that offer additional benefits beyond that provided by conventional food and are demonstrating a willingness to pay a premium for these products. Interest amongst policy makers in the functional foods and NHP industry is also increasing, due to rising public health care costs, especially in countries with publicly-funded health care systems such as Canada, increased incidence of chronic and sedentary lifestyle-related diseases, aging populations, new growth opportunities in the food industry, new R&D applications, and an increase in overall income in some countries. Ever increasing health care costs have led governments, health professionals and researchers to examine measures that promote well-being and reduce the risk of disease.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".