Bacterial abundance on the skin, gills and intestines of <i>Cyprinus carpio</i> in Lake Naivasha, Kenya: Implications for public health and fish quality
Bibliographic record
Abstract
Abstract Cyprinus carpio is the most important fish species in the Lake Naivasha fishery, comprising 51% of the total catch in the lake. Microflora, especially enteric bacteria of human or animal origin, are the causative agent for fish contamination and spoilage. Poor sanitation standards and poor sewage treatment and disposal methods within Lake Naivasha and its catchment pose a great threat for degrading the quality of C. carpio . The potential impact is rejection of the fish in the local, regional and international markets, risking the collapse of the Lake Naivasha fishery. This study determined the bacterial quality of water and C. carpio from three different sites within Lake Naivasha, namely Malewa River mouth, sewage discharge point and a mid‐lake site, based on plate count techniques. Physicochemical parameters characterizing the lake water also were also measured in situ . This study results indicated that both the fish and water in Lake Naivasha exhibited poor bacterial quality. All the physicochemical parameters were within the recommended range for fish culture, although they also were conducive to the proliferation of bacteria. Most of the sampling sites exhibited significant spatial variation in their bacterial abundance ( P < 0.05). The sewage discharge sampling site exhibited the highest mean density values for bacterial densities and clearly degrade the quality of the fish in the lake. Proper sewage treatment, and the installation of modern sanitation facilities, is recommended to improve the bacterial quality of the fish.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".