Data from: Descriptive multi-agent epidemiology via molecular screening on Atlantic salmon farms in the northeast Pacific Ocean
Bibliographic record
Abstract
Abstract Rapid expansion of salmon aquaculture has resulted in high-density populations that host diverse infectious agents, for which surveillance and monitoring are critical to disease management. Screening can reveal infection diversity from which disease arises, differential patterns of infection in live and dead fish that are difficult to collect in wild populations, and potential risks associated with agent transmission between wild and farmed hosts. We report results from a multi-year infectious-agent screening program of farmed salmon in British Columbia, Canada, using quantitative PCR to assess presence and load of 58 infective agents (viruses, bacteria, and eukaryotes) in 2931 Atlantic salmon (Salmo salar). Our analysis reveals temporal trends, agent correlations within hosts, and agent-associated mortality signatures. Multiple agents, most notably Tenacibaculum maritimum, were elevated in dead and dying salmon. We also report detections of agents only recently shown to infect farmed salmon in BC (Atlantic salmon calicivirus, Cutthroat trout virus-2), detection in freshwater hatcheries of two marine agents (Kudoa thyrsites and Tenacibaculum maritimum), and detection in the ocean of a freshwater agent (Flavobacterium psychrophilum). Our results provide information for farm managers, regulators, and conservationists, and enable further work to explore patterns of multi-agent infection and farm/wild transmission risk.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".