A surveillance tool to assess diets according to Eating Well with Canada's Food Guide.
Bibliographic record
Abstract
BACKGROUND: A surveillance tool was developed to assess dietary intake collected by surveys in relation to Eating Well with Canada's Food Guide (CFG). The tool classifies foods in the Canadian Nutrient File (CNF) according to how closely they reflect CFG. This paper describes the validation exercise conducted to ensure that CNF foods determined to be "in line with CFG" were appropriately classified. DATA AND METHODS: With statistical modelling, 8,000 simulated diets (500 for each of the 16 Dietary Reference Intake [DRI] age/sex groups) were generated using commonly consumed foods classified as "in line with CFG." Criteria for assessing the energy content and nutrient distributions of the simulated diets were based on factors considered in the development of CFG, including Estimated Energy Requirement (EER) and Dietary Reference Intake (DRI) values. RESULTS: The median energy content of the simulated diets was at or below reference EERs. Most age/sex group distributions had macronutrient profiles that met the assessment criterion of 80% of the distribution within the Acceptable Macronutrient Distribution Range, and almost all age/sex group distributions had a low prevalence (less than 10%) of micronutrient profiles below the Estimated Average Requirements. Overall, the findings indicate that diets consisting of foods that are commonly consumed by Canadians and that are "in line with CFG" have a low probability of energy excess and nutrient inadequacy. INTERPRETATION: The classification of foods in the CNF accurately reflects CFG recommendations and can be used to assess surveillance data.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".