Seasonal and Community Size-Related Patterns of Population Dietary Energy, Macronutrients, and Fiber Purchased in Grocery Stores across Nunavut, Canada
Bibliographic record
Abstract
Background: Food composition-linked grocery sales data provide time- and resource-efficient, low-bias population nutrition insights. This is particularly valuable in the Inuit-majority territory of Nunavut, Canada, where up-to-date diet-related data are lacking amid a nutrition transition. Objectives: ) 5 community size levels (with the territorial capital, Iqaluit, representing the highest level). Methods: Each of 24,463 unique products sold between 1 February, 2013, and 31 July, 2019 was matched to its closest nutritional equivalent in the Canadian Nutrient File or the United States FoodData Central database for energy, macronutrient, and fiber values to be multiplied by product amount. Per capita standardization was performed with 2016 Census data. Analysis of variance tested for the statistical significance of differences in means. Results: Consumer nutrition patterns were seasonally consistent. In the 4 community size levels other than Iqaluit, percentage of energy from protein was relatively low (9%-10%), percentage of energy from carbohydrate was high (63%-66%), and food energy density was high (295-319 kcal/100 edible g). Purchases were least energy-dense in Iqaluit and the largest community size quartile (276 and 295 kcal/100 edible g, respectively). Among 16 categories and across community size levels, Beverages and Juices & Drinks constituted roughly one-fifth of energy and one-third of carbohydrates sold. Sale of fiber was consistently low (6-7 g/capita/day). Conclusions: To our knowledge, this is the first analysis of nutrient-linked grocery sales data in the Arctic. Our findings reveal energy-dense, high-carbohydrate, low-protein, low-fiber store-bought grocery sales in the context of an advanced nutrition transition in Nunavut.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".