Bibliographic record
Abstract
Probiotic microorganisms can be found in a variety of consumer products in the Canadian marketplace, namely as ingredients in foods and natural health products (commonly known as dietary supplements), both of which are regulated by Health Canada. In regulating probiotics in foods, Health Canada has adopted the Food and Agriculture Organization (FAO)/World Health Organization (WHO) definition of probiotics as “live micro-organisms which when administered in adequate amounts confer a health benefit on the host” ( FAO/WHO, 2006 ). This definition also forms the basis for the regulation of probiotics in natural health products: the Natural Health Product Regulations specifically define a probiotic as “a monoculture or mixed-culture of live micro-organisms that benefit the microbiota indigenous to humans” ( Government of Canada, 2008 ). While probiotics may be used as ingredients in other consumer products, including over-the-counter drugs, prescription drugs, cosmetics, and pet supplements, and as additives and processing agents in a variety of products, a discussion of these particular uses is beyond the scope of this chapter. Instead, we will focus on the regulation of probiotics used as ingredients in foods and natural health products intended for humans.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".