Experimental induction of mouthrot in Atlantic salmon smolts using <i>Tenacibaculum maritimum</i> from Western Canada
Bibliographic record
Abstract
Mouthrot, or bacterial stomatitis, is a disease which mainly affects farmed Atlantic salmon, (Salmo salar, L.), smolts recently transferred into salt water in both British Columbia (BC), Canada, and Washington State, USA. It is a significant fish welfare issue which results in economic losses due to mortality and antibiotic treatments. The associated pathogen is Tenacibaculum maritimum, a bacterium which causes significant losses in many species of farmed fish worldwide. This bacterium has not been proven to be the causative agent of mouthrot in BC despite being isolated from affected Atlantic salmon. In this study, challenge experiments were performed to determine whether mouthrot could be induced with T. maritimum isolates collected from outbreaks in Western Canada and to attempt to develop a bath challenge model. A secondary objective was to use this model to test inactivated whole-cell vaccines for T. maritimum in Atlantic salmon smolts. This study shows that T. maritimum is the causative agent of mouthrot and that the bacteria can readily transfer horizontally within the population. Although the whole-cell oil-adjuvanted vaccines produced an antibody response that was partially cross-reactive with several of the T. maritimum isolates, the vaccines did not protect the fish under the study's conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".