The Pathobiome of <scp><i>Salmo trutta</i></scp> From the North Sea to the Barents Sea
Bibliographic record
Abstract
ABSTRACT Aim Salmonids are some of the best studied species with respect to their pathobiome, and at the northern range limit, there is potential for pathogens to expand with both climate change and increased fish farming in the north. Location We sampled sea‐run brown trout from throughout Norway for gill tissue and conducted both pooled and individual screenings for a total of 47 pathogens. Time Period Samples were collected during spring in 2020 and 2021. Major Taxa Bacteria, viruses and parasites of sea‐run brown trout. Methods Brown trout were gill biopsied as part of the national sea lice monitoring programme and samples were sent for laboratory analysis using the Fluidigm system, which screened for a broad panel of different pathogenic species. Results Permutated multivariate analysis of variance revealed that the pathobiome richness of trout was more related to latitude than to fish farming biomass in the region where samples were taken. However, non‐metric multidimensional scaling revealed a significant association between the individual pathobiome and the number of copepodid‐stage Lepeophtheirus salmonis lice, which did reveal a south/central versus northern Norway segregation in pathogen distributions. Importantly, many pathogens positively associated with sea lice in southern/central Norway are known to be carried, and potentially transmitted by sea lice. Main conclusions In northern Norway, pathogens normally associated with infection and disease in trout were more commonly observed. However, given that most pathogens were detected from southern to northern Norway, it appears that further expansion of farms in the north are not likely to lead to further introductions of pathogens into northern areas of Norway, although it could amplify the prevalence of these pathogens on wild salmon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".