Environmental Impacts on Skin Microbiomes of Sympatric High Arctic Salmonids
Bibliographic record
Abstract
In the region of King William Island, Nunavut, in the Canadian high Arctic, populations of salmonids including Arctic char (Salvelinus alpinus), cisco (Coregonus autumnalis and C. sardinella) as well as lake whitefish (C. clupeaformis) are diadromous, overwintering in freshwater and transitioning to saline waters following ice melt. Since these fish were sampled at the same time and from the same traditional fishing sites, comparison of their skin structures, as revealed by 16S rRNA gene sequencing, has allowed an assessment of influences on wild fish bacterial communities. Arctic char skin microbiota underwent turnover in different seasonal habitats, but these striking differences in dispersion and diversity metrics, as well as prominent taxa involving primarily Proteobacteria and Firmicutes, were less apparent in the sympatric salmonids. Not only do these results refute the hypothesis that skin communities, for the most part, reflect water microbiota, but they also indicate that differential recruitment of bacteria is influenced by the host genome and physiology. In comparison to the well-adapted Arctic char, lake whitefish at the northern edge of their range may be particularly vulnerable, and we suggest the use of skin microbiomes as a supplemental tool to monitor a sustainable Indigenous salmonid harvest during this period of change in the high Arctic.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".