Tackling community undernutrition at lake Bogoria, Kenya: The potential of spirulina (Arthrospira fusiformis) as a food supplement
Bibliographic record
Abstract
Undernutrition remains a major public health concern for many developing nations, particularly in sub-Saharan Africa.In Kenya, undernutrition affects a substantial portion of the Kenyan population, especially children and those living in rural areas.Local and sustainable means of addressing undernutrition is still lacking in many communities in urban, but more so in rural areas of Kenya.Spirulina (Arthrospira fusiformis), a cyanobacterium from alkaline inland waters, high in nutrient content, is a potential means of treating undernutrition in the developing world, where it can be easily grown.This paper presents a feasibility study on the harvest of Spirulina from Lake Bogoria in the Kenyan Rift Valley for use as a food supplement for undernutrition mitigation in the surrounding rural communities.A nutrition survey revealed the local population to be deficient in a number of micronutrients, specifically vitamins E and B12 that could be provided through dietary supplementation with Spirulina.A sample of Spirulina was collected from Lake Bogoria and analyzed for nutrient content and the presence of toxins.It was found that Lake Bogoria Spirulina had a dry protein content of 14.6% and is a rich source of dietary iron, with an iron content of 1.86%.A toxicity analysis revealed that Lake Bogoria Spirulina contained 1.15ng/g of microcystins (a group of hepatotoxic small polypeptides produced by several strains of cyanobacteria), which is within levels safe for human consumption according to World Health Organization standards.It was concluded that Lake Bogoria Spirulina is an easily accessible source of food and has the potential to be a sustainable means for the Lake Bogoria community to tackle undernutrition.Finally, using the data gathered, consultation sessions were arranged with key community groups and members to discuss the feasibility and potential for incorporation of Spirulina into the local dietan ongoing collaborative process between researchers and the community that has already been met with some success.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".