Identifying sustainable foods from among those culturally acceptable by Portuguese children and adolescents (3–17 years old)
Bibliographic record
Abstract
Promoting sustainable diets requires identifying those foods that balance nutritional quality, environmental impact, and cost, and taking cultural preferences into account. However, research on the sustainability of dietary habits in non-adult populations are limited. This cross-sectional study aims to investigate the relationship between different sustainability indicators and identify sustainable foods among those culturally acceptable by Portuguese children and adolescents. Dietary intake of 521 children and 633 adolescents was determined using food-diaries and 24-h recalls, respectively. Nutritional, environmental, and economic indicators were assessed for each food item identified as culturally acceptable among Portuguese children and adolescents. Spearman correlations were computed to assess the relationship between sustainability indicators. A sustainability score (0-3) was calculated to identify the most sustainable foods. Nutritional quality was positively correlated with greenhouse gas emissions and cost and inversely correlated with food industrial processing. Only around 10% of foods received a maximum sustainability score, namely fresh and processed vegetables, fresh fruit and fruit jars, legumes, pasta, rice and other grains, potatoes and other starchy tubers, natural and 100% fruit juices, and nectars. Overall, the most nutritious foods tend to have a higher environmental impact and cost, and few food options are simultaneously nutrient-rich, environmentally friendly and affordable.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".