Seasonal shifts in microbial diversity in the lakes of Fildes Peninsula, King George Island, Maritime Antarctica
Bibliographic record
Abstract
Abstract Fildes Peninsula, on King George Island, has been greatly influenced by recent rapid climate warming. Lakes are pervasive features of Fildes Peninsula landscapes, some of which are used as water sources for Antarctic stations. We studied seven Fildes Peninsula lakes to explore differences among lakes and between seasons in phytoplankton and bacterioplankton communities. We measured environmental variables, analysed pigments using high-performance liquid chromatography and examined bacterial DNA through high-throughput sequencing of the 16S rRNA gene. The main driver structuring microbial communities was the season (i.e. spring vs autumn). Chlorophyceae were the dominant phytoplankton group in all lakes and both seasons. Indicator bacteria for each season were identified, including Flavobacterium, Polaromonas and Oxalobacteraceae as indicators of spring conditions under thick ice, whereas Frankiales and Verrucomicrobia were indicator species of autumn following the ice-free summer. The indicator species for spring are generally observed in oligotrophic conditions, whereas many of the autumn indicators are commonly found in soils. There were lesser between-lake differences in microbial communities in autumn, at the end of the open-water period, than in spring at the end of the ice-covered period. This study will act as the basis for future assessments of changes in aquatic microbial communities.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".