Exploring fungal diversity in shrimp aquaculture: One health approach for addressing food safety and human health concerns
Bibliographic record
Abstract
Fungal infections in shrimp aquaculture pose rising concerns for food safety and public health, particularly in light of climate change. To address this, farm-to-table surveillance strategies have been explored in this study by establishing a workflow for isolating, identifying, and assessing fungi from shrimp, sediment, and water samples collected in Selangor, Malaysia. As a result of fungal propagation, 31 fungal isolates were obtained, comprising 11 species from 5 genera, with Aspergillus, Penicillium, and Fusarium being dominant. PCR-based 18S rRNA sequencing enabled phylogenetic and haplotype analyses, while Vitek® MS (MALDI-TOF MS) identified five isolates as potentially human-pathogenic. To complement culture-dependent methods, eDNA metagenomics was applied to water samples as a quick strategy to capture the abundance of fungal diversity and its potential effectiveness in evaluating shrimp environmental health. Together, these approaches highlight genomic surveillance in monitoring fungal hazards and support the development of sustainable, One Health–aligned aquaculture management strategies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".