Next Generation Sequencing (NGS) for the Determination of Fish Flesh Microbiota
Bibliographic record
Abstract
The objective of the study is the assessment of the microbial ecology and safety of fish in Greece using next-generation sequencing (NGS) and the correlation of the species of microbial flora with the production of histamine. Fourteen different fish species were obtained from local fish stores (Greece) within 1 day from capture. The initial microbiota in fish flesh was determined using NGS. The main pathogenic bacterial species identified in the tested fish samples included Vibrio spp., Clostridium spp., Staphylococcus, Flavobacterium and Janthinobacterium representing both native freshwater habitats and contaminants arising from different sources, including sewage and direct contamination by wild animals, livestock, and feed. The initial spoilage microbiota of fish consisted of several psychrotrophic Gram-negative bacteria, such as Pseudomonas, Acinetobacter, Moraxella, Shewanella, Psychrobacter, Lactobacillus, Brochothrix and Photobacterium. The results of the study show the potential of the application and the usefulness of NGS for the determination of microbial flora associated with food-borne diseases and spoilage in fish products. Histamine formation correlated with the valid reads (concentration and number of bacteria) and slightly with the genus of the identified microorganisms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".