Phylogenetic and genomic insights of <i>Flavobacterium</i> diversity in Quebec’s fish farms
Bibliographic record
Abstract
The genus Flavobacterium constitutes a vast pool of microorganisms living in multiple environmental niches including fish pathogens and species in the fish microbiome. Veterinary reports have identified flavobacteria in fish from Quebec’s fish farms, confirming their association with infections. However, these reports have not conducted in-depth characterization, and the diversity of nonpathogenic flavobacteria in Quebec remains unknown. This study is the first step in assessing the diversity of Flavobacterium in Quebec’s fish farms, without focusing solely on pathogenic strains. Seventeen isolates were collected from different fish farms, from either the water or fish. Microbial species identification was performed using PCR genotyping of the gyrB gene, whole-genome sequencing, and phylogenetic analysis. Antimicrobial susceptibility tests for tetracycline and florfenicol, the two most commonly used antibiotics in Quebec aquaculture, along with predictive tools, were employed to assess resistance. This study revealed potential new species among the isolates. No known pathogenic species were detected, and all 17 isolates clustered within CIIIb or CIIIc, recently described phylogenetic clades of Flavobacterium found in various environments, and the majority showed resistance to antibiotics. This study highlights the expanding diversity of Flavobacterium, particularly among species associated with fish, and underscores the need for further research in Quebec.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".