Mercury and Arctic Char Gill Microbiota Correlation in Canadian Arctic Communities
Bibliographic record
Abstract
Arctic char is a top predator in Arctic waters and is threatened by mercury pollution in the context of changing climate. Gill microbiota is directly exposed to environmental xenobiotics and play a central role in immunity and fitness. Surprisingly, there is a lack of literature studying the effect of mercury on gill microbiota. To fill this knowledge gap, our primary goal was to measure to what extent gill exposure to mercury may alter gill microbiota activity in Arctic char. Specifically, we calculated the correlation between the taxonomic distribution of gill-associated bacterial symbiont activity and total mercury concentration in livers and muscles in wild populations of Arctic char in the Canadian Arctic. Our results showed that total mercury concentrations in tissues were higher in Ekaluktutiak (Nunavut) than in the other sites in Nunavik. Proteobacteria was the main phylum correlated to mercury concentration in both tissues, followed by Bacteroidetes and Cyanobacteria. In the most contaminated sites, Aeromonas and Pseudomonas (Proteobacteria) were predominant, while mercury concentration negatively correlated with Photobacterium (Proteobacteria) or Cerasicoccus (Verrucomicrobia). In summary, we found that mercury contamination correlates with active gill microbiota composition, with potential implications of strains in modulating mercury toxicity, making them interesting for future biomarker studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".