Traditional food diversity predicts dietary quality for the Awajún in the Peruvian Amazon
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: Our goal was to assess the potential for evaluating strengths of the Awajún traditional food system using dietary assessment, a traditional food diversity score and ranking of local foods. DESIGN: The method was used for dietary data obtained from mothers and children in the Awajún culture of the Peruvian Amazon where >90% of the dietary energy is derived from local, traditional food. Traditional food diversity scores were calculated from repeat 24-hour recalls. Group mean intakes of energy, fat, protein, iron, vitamin A and vitamin C from each food item were used to rank foods by nutrient contribution. SETTING: The study took place in six remote communities along the lower Cenepa River in the Amazonas District of Peru, South America. SUBJECTS: Dietary data were collected from 49 Awajún mothers and 34 children aged 3-6 years, representative of the six communities. RESULTS: Higher traditional food diversity was associated with greater protein, fibre, vitamin and mineral intakes when controlling for energy (partial correlations = 0.37 to 0.64). Unique sources for iron, total vitamin A and vitamin C were found in the Awajún traditional food system. CONCLUSIONS: A traditional food diversity score was a useful tool for predicting nutrient adequacy for the Awajún. Promotion of the Awajún traditional food system should focus on dietary diversity and unique nutrient-dense local foods.
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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 | 0.018 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 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 it