Freshwater microbial community diversity in a rapidly changing High Arctic watershed
Bibliographic record
Abstract
Current models predict increases in High Arctic temperatures and precipitation that will have profound impacts on the Arctic hydrological cycle, including enhanced glacial melt and thawing of active layer soils. However, it remains uncertain how these changes will impact the structure of downstream resident freshwater microbial communities and ensuing microbially driven freshwater ecosystem services. Using the Lake Hazen watershed (Nunavut, Canada; 82°N, 71°W) as a sentinel system, we related microbial community composition (16S rRNA gene sequencing) to physicochemical parameters (e.g. dissolved oxygen and nutrients) over an annual hydrological cycle in three freshwater compartments within the watershed: (i) glacial rivers; (ii) active layer thaw-fed streams and waterbodies and (iii) Lake Hazen, into which (i) and (ii) drain. Microbial communities throughout these freshwater compartments were strongly interconnected, hydrologically, and often correlated with the presence of melt-sourced chemicals (e.g. dissolved inorganic carbon) as the melt season progressed. Within Lake Hazen itself, water column microbial communities were generally stable over spring and summer, despite fluctuating lake physicochemistry, indicating that these communities and the potential ecosystem services they provide therein may be resilient to environmental change. This work helps to establish a baseline understanding of how microbial communities and the ecosystem services they provide in Arctic watersheds might respond to future 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".