Discovery and surveillance of viruses from salmon in British Columbia using viral immune-response biomarkers, metatranscriptomics, and high-throughput RT-PCR
Bibliographic record
Abstract
Abstract The emergence of infectious agents poses a continual economic and environmental challenge to aquaculture production, yet the diversity, abundance and epidemiology of aquatic viruses are poorly characterised. In this study, we applied salmon host transcriptional biomarkers to identify and select fish in a viral disease state but only those that we also showed to be negative for established viruses. This was followed by metatranscriptomic sequencing to determine the viromes of dead and dying farmed Atlantic ( Salmo salar ) and Chinook ( Oncorhynchus tshawytscha ) salmon in British Columbia. We found that the application of the biomarker panel increased the probability of discovering viruses in aquaculture populations. We discovered viruses that have not previously been characterized in British Columbian Atlantic salmon farms. To determine the epidemiology of the newly discovered or emerging viruses we conducted high-throughput RT-PCR to reveal their prevalence in British Columbia (BC), and detected some of the viruses we first discovered in farmed Atlantic salmon in Chinook and sockeye salmon, suggesting a broad host range. Finally, we applied in-situ hybridisation to confirm infection and explore the tissue tropism of each virus.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".