Microbial connectivity and sorting in a High Arctic watershed
Bibliographic record
Abstract
Aquatic ecosystems in the High Arctic are facing unprecedented changes as a result of global warming effects on the cryosphere. Snow pack is a central feature of northern landscapes, but the snow microbiome and its microbial connectivity to adjacent and downstream habitats have been little explored. To evaluate these aspects, we sampled along a hydrologic continuum at Ward Hunt Lake (latitude 83°N) in the Canadian High Arctic, from snow banks, water tracks in the permafrost catchment, the upper and lower strata of the lake, and the lake outlet and its coastal marine mixing zone. The microbial communities were analyzed by high-throughput sequencing of 16 and 18S rRNA to determine the composition of potentially active Bacteria, Archaea and microbial Eukarya. Each habitat had distinct microbial assemblages, with highest species richness in the subsurface water tracks that connected the melting snow to the lake. However, up to 30% of phylotypes were shared along the hydrologic continuum, showing that many taxa originating from the snow can remain in the active fraction of downstream microbiomes. The results imply that changes in snowfall associated with climate warming will affect microbial community structure throughout all spatially connected habitats within snow-fed polar ecosystems.
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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 | 0.002 |
| Science and technology studies | 0.002 | 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".