Elevated Seawater Temperature and Infection with Neoparamoeba perurans Exacerbate Complex Gill Disease in Farmed Atlantic Salmon (Salmo salar) in British Columbia, Canada
Bibliographic record
Abstract
Gill disorders and diseases are emergent health concerns affecting marine-farmed salmon, for which the causal factors are poorly understood in British Columbia (BC), Canada. This study sought to describe and compare spatial and temporal patterns of infection with Neoparamoeba perurans, the causal agent of amoebic gill disease, and visually assessed gill health scores in farmed Atlantic salmon. Gill tissue obtained during the Fisheries and Oceans Canada’s Fish Health Audit and Intelligence Program (DFO-FHAIP) between 2016 and 2020 were screened for N. perurans by qPCR. Semi-quantitative visual gill health assessments were conducted during the audits, and farms were assigned clinical AGD status based on microscopic visualization of N. perurans together with histopathological lesions. Seawater temperature and salinity data were collected from all active farms in the region during the study period. Trends in gill scores and associations with N. perurans infections were described and tested using an ordinal logistic mixed model. The amoeba was detected in 21% of 345 audited farms and in 12% of 1925 fish samples. Most (56%, n = 1898) samples had no visible gill damage (score = 0), and 23% had scores ≥ 2 (high). Distinct patterns of spatial and temporal variability in the rates of high gill scores and N. perurans infections are demonstrated. The model supported the statistically significant relationship observed between seawater temperature and the proportion of samples with elevated gill scores. The model also revealed a direct relationship between salinity and gill score but only in the presence of N. perurans. While the data suggest that histopathological lesions contributed to the gill scores, temperature and, to a lesser extent, salinity were significant risk factors of increased gill score. The results are discussed in the context of recently frequent thermal anomalies in the northeastern Pacific Ocean.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".