Eukaryotes in Arctic and Antarctic cyanobacterial mats
Bibliographic record
Abstract
Cyanobacterial mats are commonly found in freshwater ecosystems throughout the polar regions. Most mats are multilayered three-dimensional structures with the filamentous cyanobacteria embedded in a gel-like matrix. Although early descriptions mentioned the presence of larger organisms including metazoans living in the mats, there have been few studies specifically focused on the microbial eukaryotes, which are often small cells with few morphological features suitable for identification by microscopy. Here, we applied 18S rRNA gene clone library analysis to identify eukaryotes in cyanobacterial mat communities from both the Antarctic and the extreme High Arctic. We identified 39 ribotypes at the level of 99% sequence similarity. These consisted of taxa within algal and other protist groups including Chlorophyceae, Prasinophyceae, Ulvophyceae, Trebouxiophyceae, Bacillariophyceae, Chrysophyceae, Ciliophora, and Cercozoa. Fungi were also recovered, as were 21 metazoan ribotypes. The eukaryotic taxa appeared habitat-specific with little overlap between lake, pond, and ice shelf communities. Some ribotypes were common to both Arctic and Antarctic mats, suggesting global dispersal of these taxa and similarity in the environmental filters acting on protist communities. Many of these eukaryotic taxa likely benefit from protected, nutrient-rich microhabitats within the cyanobacterial mat environment.
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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 | 0.002 | 0.001 |
| 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) | 0.000 | 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".