A Review of Winter Ulcer Disease and Skin Ulcer Outbreaks in Atlantic Salmon (Salmo salar)
Bibliographic record
Abstract
Winter ulcer disease (WUD) is widely recognized as a serious threat to animal welfare and a major contributor to revenue loss within the aquaculture sector, particularly affecting the salmon-farming industry. This highlights the significant impact of WUD on both animal well-being and the economic sustainability of fish farming. WUD causes hemorrhagic signs and results in dermal lesions and ulcers. This disease can lead to higher mortality rates and a considerable decline in the fish’s market value. Moritella viscosa, a Gram-negative bacterium, is predominantly, but not exclusively, correlated with the emergence of WUD, mostly during the colder seasons. Waterborne transmission is the primary way for spreading the bacterium within a population. However, there is remarkable variation in the prevalence and characteristics of WUD in different regions. In Europe, this disease often occurs in the winter, and the intensity and occurrence of outbreaks are influenced by water temperature and salinity. In contrast, outbreaks are typically observed in the summer and mid-autumn in Eastern Canada. Despite the administration of various polyvalent vaccines, outbreaks of skin ulcers have been documented in Canada, and studies have highlighted the possible roles of other bacterial pathogens in Atlantic salmon. This review discusses the etiology, pathogenesis, and potential mitigation or prevention strategies for WUD, mainly in Atlantic salmon. Moreover, it underscores the necessity of conducting further investigations to discover the potential unknown causative agents of ulcerative disease and design appropriate vaccines or preventive strategies for these pathogens.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".