The spatial distribution of infectious agents in wild Pacific salmon along the British Columbia coast
Bibliographic record
Abstract
Although infectious agents can act as strong population regulators, knowledge of their spatial distributions in wild Pacific salmon is limited, especially in the marine environment. Characterizing pathogen distributions during early marine residence, a period considered a survival bottleneck for Pacific salmon, may reveal where salmon populations are exposed to potentially detrimental pathogens. Using high-throughput qPCR, we determined the prevalence of 56 infectious agents in 5719 Chinook, 2032 Coho and 4062 Sockeye salmon, sampled between 2008 and 2018, in their first year of marine residence along coastal Western Canada. We identified high prevalence clusters, which often shifted geographically with season, for most of the 41 detected agents. A high density of infection clusters was found in the Salish Sea along the east coast of Vancouver Island, an important migration route and residence area for many salmon populations, some experiencing chronically poor marine survival. Maps for each infectious agent taxa showing clusters across all host species are provided. Our novel documentation of salmon pathogen distributions in the marine environment contributes to the ecological knowledge regarding some lesser known pathogens, identifies salmon populations potentially impacted by specific pathogens, and pinpoints priority locations for future research and remediation.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".