Pathogen and Gene Expression Profiles of Atlantic Salmon From an Endangered Population
Bibliographic record
Abstract
ABSTRACT The role of pathogens in impacting the behaviour and fate of salmonids has been studied extensively for some selected pathogens such as sea lice. However, the whole pathobiome of the fish are seldom considered and may confound and influence the study species in situ. In this study, we investigated the presence of pathogens in returning adult wild and hatchery salmon in the river Vosso using gill samples analysed in high‐throughput PCR with a selection of assays targeting different fish pathogens. In addition, the samples were analysed for gene expressions that have been previously linked to imminent mortality, thermal stress, and inflammation related biomarkers. These data were linked to the behaviour of the individual fish collected from acoustic telemetry tags inserted in the abdomen of the fish. Previous analyses have suggested that the behaviour of the hatchery and wild salmon in this study area is different; however, there was no evidence that the pathobiome or the gene expression of the two groups of salmon (39 wild and 14 hatchery) could explain the behavioural differences between these two groups. Furthermore, neither pathogen profile nor gene expressions had a significant relationship between metrics of behaviour or survival of the fish. Results suggest that gene expression and pathogen screenings are insufficient to predict fates of migrating salmon. The Vosso salmon is a threatened population in Norway after collapsing in the 1980s; these data contribute to ongoing efforts to identify factors that are limiting the recovery of this population after decades of poor returns.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".