Identification of Traditional Foods with Public Health Potential for Complementary Feeding in Western Kenya
Why this work is in the frame
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Bibliographic record
Abstract
The diversity of traditional foods in Kisumu West District of Western Kenya was assessed with an aim to identify the foods with a potential for complementary feeding. Leaves were the most consumed plant part amongst vegetables, while a few fruits were consumed together with their seeds. Amaranthus cruentus L. was found to be consumed as a leafy vegetable while another variety, Amaranthus hybridus L. was found to be consumed as a grain. Four species of winged termites, a grasshopper, black ant and dagaa fish were also identified. Twelve of the traditional foods were found to be associated with nutritional and health benefits as perceived by the locals. Traditional food processing methods such as boiling, fermentation and sun drying were identified. Thus exploitation of the species possessing nutrient, health and processing benefits needs to be explored in complementary feeding.
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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 | 0.003 | 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 | 0.000 | 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) | 0.000 | 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