Traditional food diversity predicts dietary quality for the Awajún in the Peruvian Amazon
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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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".