Bacterial community and cyanotoxin gene distribution of the Winam Gulf, Lake Victoria, Kenya
Bibliographic record
Abstract
Abstract The Winam Gulf (Kenya) is frequently impaired by cyanobacterial harmful algal blooms (cHABs) due to inadequate wastewater treatment and excess agricultural nutrient input. While phytoplankton in Lake Victoria have been characterized using morphological criteria, our aim is to identify potential toxin‐producing cyanobacteria using molecular approaches. The Gulf was sampled over two successive summer seasons, and 16S and 18S ribosomal RNA gene sequencing was performed. Additionally, key genes involved in production of cyanotoxins were examined by quantitative PCR. Bacterial communities were spatially variable, forming distinct clusters in line with regions of the Gulf. Taxa associated with diazotrophy were dominant near Homa Bay. On the eastern side, samples exhibited elevated cyrA abundances, indicating genetic capability of cylindrospermopsin synthesis. Indeed, near the Nyando River mouth in 2022, cyrA exceeded 10 million copies L −1 where there were more than 6000 Cylindrospermopsis spp. cells mL −1 . In contrast, the southwestern region had elevated mcyE gene (microcystin synthesis) detections near Homa Bay where Microcystis and Dolichospermum spp. were observed. These findings show that within a relatively small embayment, composition and toxin synthesis potential of cHABs can vary dramatically. This underscores the need for multifaceted management approaches and frequent cyanotoxin monitoring to reduce human health impacts.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".