Community dynamics and function of algae and bacteria during winter in central European great lakes
Bibliographic record
Abstract
Abundant phytoplankton and bacteria were identified by microscopy and high-throughput 16S rRNA tag Illumina sequencing of samples from water- and ice phases collected during winter at two central European Great Lakes, Balaton and Fertő (Neusiedlersee). Bacterial reads at all sites were dominated (>85%) by Bacteroidetes and Proteobacteria. Amongst phototrophs, microscopy and 16S sequencing revealed that both phytoplankton and cyanobacteria were represented, with a median of 1500 cyanobacterial sequence reads amongst 13 samples analyzed. The sequence analysis compared replicate Balaton and Fertő ice and water samples with an outgroup from three Hungarian soda lakes. In particular, both water and ice from Fertő contained high contributions from cyanobacteria. Two percent of total reads identified to the level of family in water at Fertő were dominated by a single operational taxonomic unit (OTU) of a cyanobacterium within the Rivulariaceae, which was largely absent from ice. Conversely, ice samples from both lakes yielded an abundant OTU assigned to a Flavobacterium sp. known to be associated with freshwater ice. Principal Coordinates Analysis (PCoA) revealed that the ice communities from all sites were similar to one another, and that the water communities did not cluster together. Fluorescence emission spectra obtained at 77 K confirmed the presence of intact cyanobacteria in Fertő water and ice. Photosynthetic characterization of phototrophs resident in water and ice analyzed by assay of acid-stable photosynthetic H14CO3– incorporation showed that communities from both phases were photosynthetically active, thus adding to growing recognition of ice-covered lakes as viable habitat for phototrophs.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".