Impacts of algal blooms and microcystins in fish on small-scale fishers in Winam Gulf, Lake Victoria: implications for health and livelihood
Bibliographic record
Abstract
Lake Victoria, bordered by Kenya, Tanzania, and Uganda, provides one of the largest freshwater fisheries in the world and supports millions in small-scale fishing communities. Historical environmental change, including population growth, nutrient loading, introduced invasive species, and rising temperatures, has resulted in eutrophication and persistent cyanobacterial harmful algae blooms (cyanoHABs) over recent decades, particularly in the shallower gulfs, bays, and inlets. CyanoHABs impact fisheries and food web dynamics and compromise food and water security for nearshore fisher populations. In this study, we examine the social-ecological impact of freshwater blooms on fisher health in one of these eutrophic regions, Winam Gulf in Lake Victoria. CyanoHABs persist for months and produce microcystins and hepatotoxins at levels unsafe for human health. We assessed potential risk and contribution of microcystin exposure through fish consumption, in addition to exposure through water source, and conducted 400 fisher and 400 household surveys. Average microcystin concentrations exceeded the World Health Organization (WHO) guideline for drinking water consistently during the long dry season, and cyanobacterial cell counts surpassed WHO standards for recreational risk in 84% of samples. Hazard quotients for fish consumed by young children were 5 to 10 times higher than permissible levels. In addition, fishers chronicled profound ecosystem changes with direct impact on livelihood, fisheries, and water quality with 77.4% reporting a decline in profit or catch, 83.1% reporting adverse impacts of cyanoHABs on fish in the lake, and 98.2% reporting indicators of declining water quality in the lake overall. Through the application of a social-ecological lens to a public health model, we identified spheres of influence that modify how fishers experience HABs related stressors and risks to provide a starting point at which to identify sustainable strategies to improve food and water security and livelihood for the millions in nearshore communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".