Microbial community diversity and heterotrophic production in a coastal Arctic ecosystem: A stamukhi lake and its source waters
Bibliographic record
Abstract
Stamukhi lakes are vast but little‐explored Arctic ecosystems. They occur throughout winter, spring, and early summer near large river inflows along the Arctic coastline, and are the result of freshwater retention behind the thick barrier of rubble ice (stamukhi) that forms at the outer limit of land‐fast sea ice. We examined the molecular biodiversity within all three microbial domains (Bacteria, Archaea, and Eukaryota) and the heterotrophic productivity in Lake Mackenzie, a stamukhi lake in the western Canadian Arctic, and made comparative measurements in the freshwater (Mackenzie River) and marine (Beaufort Sea) source waters. Bacterial and eukaryotic communities in the stamukhi lake differed in composition and diversity from both marine and riverine environments, whereas the archaeal communities were similar in the lake and river. Bacteria 16S ribosomal RNA sequences from the lake were mostly within freshwater clusters of Betaproteobacteria and Bacteroidetes and the Archaea were within the Lake Dagow sediment and Rice cluster‐V clusters of Euryarchaeota. The eukaryotes were mainly ciliates from the subclass Choreotrichia, and there was a notable lack of flagellates. Heterotrophic production rates in the lake were lower than in the river and more similar to those in the sea, despite much higher bacterial concentrations than in either. The lake samples had markedly higher ratios of 3H leucine to 3H thymidine incorporation than in the river and sea, implying some physiological stress. Lake Mackenzie is an active microbial ecosystem with distinct physical and microbiological properties. This circumpolar ecosystem type, vulnerable to the ongoing effects of climate change, likely plays a key functional role in processing riverine inputs to the Arctic Ocean.
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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 | 0.002 | 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".