High abundance and diversity of flagellates under ice cover in Lake Baikal revealed by microscopy and metabarcoding
Bibliographic record
Abstract
Lakes located in the north temperate zone may be covered with ice for long periods. Under the ice, a habitat is created, different from the open water period, which is characterized by low temperature and reduced light due to ice and snow cover. We investigated phytoplankton in sub-ice communities (SI) at the ice-water interface and the 0–25 m under-ice water column communities (UW) in the pelagic zone of Lake Baikal. Community structure was assessed using microscopy and metabarcoding of fragments of 18S rRNA gene. Flagellate diversity included 34 taxa from 64 microalgae identified by microscopy and 48 operational taxonomic units (OTUs) from 56 identified by metabarcoding. In conditions of complete snow-covered ice, high diversity and abundance of flagellates (up to 14 million cells L −1 ) were observed, with an advantage due to their mobility and different feeding modes. Both microscopy and metabarcoding data show that the taxonomic structure of SI and UW is different. SI communities of Lake Baikal showed mass development of a mixed group of nanoflagellates, which consisted of “ Spumella- and Chlamydomonas -like flagellates”. SI communities were less taxonomically diverse, suggesting that available resource gradients on the ice bottom are more tightly constrained, and taxa have to be ice bottom specialists to survive there. The relative spatial heterogeneity of SI communities is reflective of greater homogenization of habitat parameters in the winter water column and more dynamic conditions on the ice bottom. The data obtained expand the understanding of the diversity and abundance of organisms in under-ice habitats in north temperate latitudes.
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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) | 0.001 | 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 | 0.000 | 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".