Bacterial divergence among the interconnected habitats of a High Arctic Lake
Bibliographic record
Abstract
Climate warming is likely to increase the physical connectivity of ecosystems with their surroundings. For Arctic lakes, increasing meltwater and precipitation may enhance the inputs of nutrients, organic matter and microorganisms from their catchments, and the increasingly ice-free, open-water conditions of the Arctic Ocean may favor increased inputs of marine aerosols, including microbiota. This study therefore aimed to determine how changing connectivity to terrestrial and marine habitats may affect the dispersal, sorting, and establishment of bacterial communities in a coastal High Arctic lake. Three habitats in this model system were sampled for ice, water, and snow: the lake, inflowing water tracks over permafrost soils, and an adjacent ice-dammed bay connected to the Arctic Ocean. Lake water chemistry confirmed the hydrological connection between the lake and terrestrial habitats, with the lake fed by terrestrial carbon sources via snow and groundwater run-off. Sequencing of 16S rDNA and rRNA showed evidence of a small marine and terrestrial influence on the lake, but few bacterial phylotypes were common to all three connected habitats. These results imply ongoing strong environmental filtering by habitat type, despite the apparent and potentially rising connectivity, and provide an example of bacterial resilience in a region of rapid climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".