Supplemented foods: Scientific and Canadian regulatory framework for botanicals added to foods as ingredients with putative health benefits
Bibliographic record
Abstract
Botanical ingredients from diverse species have been part of the human diet throughout evolution. Their modern prepared foods to which botanical ingredients, their extracts and isolates, including vitamins, mineral nutrients, amino acids, caffeine, and other bioactives, have been added for putative health benefits. Supplemented foods may be consumed by the general population ad libitum, are less likely than dosage-form supplements to be checked by consumers for labelling with conditions of use and cautions, and with few exceptions have not been subject to pre-market regulatory review. However, as industry develops innovative food products with high levels of botanical ingredients added for health benefits rather than just nutritive, taste, texture or technical functions, this presents unique challenges for a regulatory framework that was developed primarily for conventional foods. Health Canada is consulting consumers, manufacturers, scientists, dieticians and other health professionals on a proposed approach to allow market access while working with stakeholders to address outstanding gaps in the information needed to inform regulatory amendments that will provide for the long term safety of supplemented foods under the Canadian Food and Drug Regulations.
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 | 0.077 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.013 | 0.005 |
| Research integrity | 0.020 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".