Insights into the circulating microbiome of Atlantic and Greenland halibut populations: the role of species-specific and environmental factors
Bibliographic record
Abstract
Establishing long-term microbiome-based monitoring programs is critical for managing and conserving wild fish populations in response to climate change. In most cases, these studies have been conducted on gut and, to a lesser extent, skin (mucus) microbiomes. Here, we exploited the concept of liquid biopsy to study the circulating bacterial microbiome of two Northern halibut species of economic and ecological importance. Amplification and sequencing of the 16S rRNA gene were achieved using a single drop of blood fixed on FTA cards to identify the core blood microbiome of Atlantic and Greenland halibut populations inhabiting the Gulf of St. Lawrence, Canada. We provide evidence that the circulating microbiome DNA (cmDNA) is driven by genetic and environmental factors. More specifically, we found that the circulating microbiome signatures are species-specific and vary according to sex, size, temperature, condition factor, and geographical localization. Overall, our study provides a novel approach for detecting dysbiosis signatures and the risk of disease in wild fish populations for fisheries management, most notably in the context of climate change.
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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 | 0.001 | 0.000 |
| 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".