Rich Food Biodiversity Amid Low Consumption of Food Items in Kilosa District, Tanzania
Bibliographic record
Abstract
BACKGROUND: Indigenous foods, which contribute largely to the majority of the households' food basket in rural Tanzanian communities, have not been fully characterized or documented. OBJECTIVES: The study aimed to document foods available and consumed in Kilosa District, Tanzania, in an attempt to promote, revive use, and build evidence for sustainable utilization of the rich local biodiversity. METHODS: Data were collected from 307 households in 3 agroecological zones in Kilosa District during the beginning of the rainy season (February-May) and immediately after harvest (September-October). A list of food items was generated, and 24-hour recall was performed. Descriptive statistics were calculated and a student t test statistic was used to compare the means of the Food Biodiversity Score between the agricultural seasons. RESULTS: A total of 183 edible food items were reported by households with more reported in the rainy season (n = 82) compared to harvest season (n = 64). The mean number of food items consumed per day during the rainy season was 4.7 (95% CI: 4.5-5.0) compared to 5.9 (95% CI: 5.7-6.1) during harvest season. About 50% of the households mentioned that wild edible foods were less accepted by household members. CONCLUSION: Despite the rich local food biodiversity, households relied on few food items which may be due to limited awareness and knowledge about the biodiversity of foods in the community. It is important to educate communities on the rich and affordable food base available locally to improve their food diversity, income, and nutritional status.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".