Hidden biofilms in a far northern lake and implications for the changing Arctic
Bibliographic record
Abstract
Shallow lakes are common across the Arctic landscape and their ecosystem productivity is often dominated by benthic, cyanobacterial biofilms. Many of these water bodies freeze to the bottom and are biologically inactive during winter, but full freeze-up is becoming less common with Arctic warming. Here we analyzed the microbiome structure of newly discovered biofilms at the deepest site of a perennially ice-covered High Arctic lake as a model of polar microbial communities that remain unfrozen throughout the year. Biofilms were also sampled from the lake's shallow moat region that melts out and refreezes to the bottom annually. Using high throughput small subunit ribosomal RNA sequencing, we found more taxonomic richness in Bacteria, Archaea and microbial eukaryotes in the perennially unfrozen biofilms compared to moat communities. The deep communities contained both aerobic and anaerobic taxa including denitrifiers, sulfate reducers, and methanogenic Archaea. The water overlying the deep biofilms was well oxygenated in mid-summer but almost devoid of oxygen in spring, indicating anoxia during winter. Seasonally alternating oxic-anoxic regimes may become increasingly widespread in polar biofilms as fewer lakes and ponds freeze to the bottom, favoring prolonged anaerobic metabolism and greenhouse gas production during winter darkness.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".