Parasitism perturbs the mucosal microbiome of Atlantic Salmon
Bibliographic record
Abstract
Interactions between parasite, host and host-associated microbiota are increasingly understood as important determinants of disease progression and morbidity. Salmon lice, including the parasitic copepod Lepeophtheirus salmonis and related species, are perhaps the most important problem facing Atlantic Salmon aquaculture after feed sustainability. Salmon lice parasitize the surface of the fish, feeding off mucus, scales and underlying tissue. Secondary bacterial infections are a major source of associated morbidity. In this study we tracked the diversity and composition of Salmo salar skin surface microbiota throughout a complete L. salmonis infection cycle among 800 post-smolts as compared to healthy controls. Among infected fish we observed a significant reduction in microbial richness (Chao1, P = 0.0136), raised diversity (Shannon, P < 7.86e-06) as well as highly significant destabilisation of microbial community composition (Pairwise Unifrac, beta-diversity, P < 1.86e-05; P = 0.0132) by comparison to controls. While undetectable on an individual level, network analysis of microbial taxa on infected fish revealed the association of multiple pathogenic genera (Vibrio, Flavobacterium, Tenacibaculum, Pseudomonas) with high louse burdens. We discuss our findings in the context of ecological theory and colonisation resistance, in addition to the role microbiota in driving primary and secondary pathology in the host.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".