Examination of hyper-palatable foods and their nutrient characteristics using globally crowdsourced data
Bibliographic record
Abstract
Hyper-palatable foods (HPF), characterized by specific nutrient combinations at moderate to high levels (e.g., fat and sodium), have been suggested to increase energy intake and obesity risk due to their strong reinforcing properties. The study examined patterns in HPF availability, nutrient characteristics, and overlap with ultra-processed foods (UPF) across countries from a globally crowdsourced, open-source dataset. Food data (N = 314229 food items) from 17 countries were analyzed. Compared to the US, foods from most European countries examined, as well as Canada and Mexico, were significantly less likely to be identified as HPF (ORs = 0.70 to 0.93) and had lower % of calories from fat, sugar, starchy carbohydrates, and sodium compared to HPF items from the US (ORs = 0.76 to 0.98). Across countries, items identified as HPF substantially exceeded HPF threshold criteria by 70-229%. Foods identified as being both HPF and ultra-processed foods ranged from 33% (Bulgaria) to 50% (US). Overall, findings from 17 countries across Europe, North America, and South America highlighted foods from the US as being more likely to be hyper-palatable relative to most other countries examined. Results highlighted variability in the degree to which foods met criteria as HFP and UPF across countries.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".