Bibliographic record
Abstract
'Discretionary fortification' refers to the addition of vitamins and minerals to foods at the discretion of manufacturers for marketing purposes, but not as part of a planned public health intervention. While the nutrients added may correspond to needs in the population, an examination of novel beverages sold in Toronto supermarkets revealed added nutrients for which there is little or no evidence of inadequacy in the population. This is consistent with the variable effects of manufacturer-driven fortification on nutrient adequacy observed in the US. Nutrient intakes in excess of Tolerable Upper Intake Levels are now observed in the context of supplement use and high levels of consumption of fortified foods. Expanding discretionary fortification can only increase nutrient exposures, but any health risks associated with chronically high nutrient loads from fortification and supplementation remain to be discovered. Regulatory bodies are focused on the establishment of safe levels of nutrient addition, but their estimation procedures are fraught with untested assumptions and data limitations. The task of determining the benefits of discretionary fortification is being left to consumers, but the nutrition information available to them is insufficient to allow for differentiation of potentially beneficial fortification from gratuitous nutrient additions.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".