Classifying sources of low- and no-calorie sweeteners within the Canadian food composition database
Bibliographic record
Abstract
Low- and no-calorie sweeteners are sugar substitutes that impart sweetness. Examining exposure to low- and no-calorie sweeteners is challenging because the amounts of sweeteners in food and beverage products are not standard elements of food composition databases. We identified food codes representing sources of low- and no-calorie sweeteners in the food composition database used for Canadian surveillance data using multiple approaches. First, food code descriptions were searched for keywords (e.g. low calorie) potentially representing low and no-calorie sweeteners. Next, the U.S. Food and Nutrient Database for Dietary Surveys food code descriptions, matched to food codes within the Canadian database, were examined for keywords representing confirmed sweetener sources. Finally, using websites for three Canadian grocers, ingredient lists for brand-specific products were examined for sources of low- and no-calorie sweeteners. Recipe codes often required an examination of ingredient-level food codes. Of 5180 food codes, 76 were classified as sources of low- and no-calorie sweeteners and an additional 46 recipe codes were identified as containing a source of sweetener. The classification system can be applied to national survey data to describe exposure to low- and no-calorie sweeteners and identify key sources of sweeteners. Standardized identification of food codes as sources of low- and no-calorie sweeteners will contribute to an evidence base that can be synthesized to inform nutrition policy. • A text-based search strategy identified sources of low- and no-calorie sweeteners (LNCSs). • Using Canada’s food composition database, 76 food codes were classified as sources of LNCS. • Examination of recipe food codes at the ingredient level is needed to accurately capture LNCSs.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".