Intrinsic and extrinsic factors associated with the spatio-temporal distribution of infectious agents in early marine Chinook and coho salmon
Bibliographic record
Abstract
Understanding the factors driving the spatial distribution of infectious agents in populations is key to predicting infectious agent distributions under future ecological and anthropogenic scenarios. We applied a geostatistical analysis to a data set of 59 infectious agents assayed in thousands of Chinook and coho salmon in their first marine year to identify intrinsic and extrinsic factors associated with the probability and density (infectious agent load) of infection. Meta-analysis of the pathogen-specific geostatistical models indicated that sea surface salinity was the extrinsic factor most frequently associated with infection probability and density for a majority of infectious agents. In addition, agents that were categorized as having a moderate risk of transmission from aquaculture to wild salmon were more likely to occur, and at higher infection densities, in fish collected closer to active aquaculture facilities. Although hypotheses pertaining to other intrinsic and extrinsic factors, including age at ocean entry, known hatchery origin, and sea surface temperature deviation, were not supported by the meta-analysis results, some individual agents demonstrated strong associations with these factors. Our results suggest that climate-change-driven shifts in coastal seawater salinity (and to a lesser extent, temperature) may result in changes to the infection dynamics of several infectious agents. In addition, our results contribute to existing evidence characterizing the risk of infectious agent transmission from netpen aquaculture to free-ranging 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".