Assessment of Fungi Infection on Clarias anguillaris (Linnaeus, 1758) and Oreochromis niloticus (Linnaeus, 1758), Two Fish Species Farmed in Burkina Faso
Bibliographic record
Abstract
Worlwide, fungi are by far the most common pathogen in fish farming. They are responsible of important economic losses in the fishery sector. However, in Burkina Faso knowledge of fungi infecting farmed fish are scarce. As a consequence, the prevalence as well as the effect of these pathogens are not known. A study was carried out on fungal infection of farmed fish. It aimed to evaluate the diversity of fungi associated with Oreochromis niloticus (Linnaeus, 1758) and Clarias anguillaris (Linnaeus, 1758) farmed in Burkina Faso. In total, 89 individuals of fish consisted of 47 specimens of Oreochromis niloticus and 42 specimens of Clarias anguillaris were collected from eleven fish farms. Swabs were taken on fish bodies and inoculated in Malt Extract Agar (MEA). The chloramphenicol was added to avoid bacterial contamination. Seventy-seven (77) fish were infected by fungi (85.51%). Four genera including Aspergillus (four species), Rhizopus (one species), Penicillium (one species) and Mucor (one species) were isolated. Small and big fish were all infected. This study shows fungi as a potential factor that impacts fish farming and suggests a need for more research on their effect on fish. Knowledge of fish parasites like fungi will allow to set effective means of fish pathology control, which once implemented will lead to an increase in fish farming productivity.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".