Analysis of the association between microparasites and sea lice (Lepeophtheirus salmonis and Caligus clemensi) infecting wild juvenile pink, chum, and sockeye salmon in British Columbia
Bibliographic record
Abstract
Concurrent infection by multiple pathogens could have important fitness consequences for wild Pacific salmon in BC, particularly during their early marine life when they acquire marine pathogens from wild and farmed fish that share the marine environment. Co-infection may be facilitated by sea lice, or may reflect low host condition. To discern whether sea-louse infection influences the likelihood of co-infection by other pathogens, I coupled molecular-genetics infectious-agent data with counts of sea lice (Lepeophtheirus salmonis and Caligus clemensi) for juvenile pink, chum, and sockeye salmon migrating through the strait east of Vancouver Island in 2015 and 2016. I found inconsistent relationships between the presence or load of sea lice and the likelihood of co-infection or load for 14 “agents of interest” (i.e., those with 5–95% prevalence in at least one salmon species), suggesting that sea lice and pathogens operate independently for the juvenile salmon analyzed in this study.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".