Tracing the Origin of Planktonic Protists in an Ancient Lake
Bibliographic record
Abstract
Ancient lakes are among the most interesting models for evolution studies, because their biodiversity is the result of a complex combination of migration and speciation. Here, we investigate the origin of single celled planktonic eukaryotes from the oldest lake in the world – Lake Baikal. By using 18S rDNA metabarcoding we recovered 1,427 Operational Taxonomic Units (OTUs) belonging to protists populating surface waters (1-50 meters) and belonging to pico- and nano-plankton size fractions. The recovered communities resembled other lacustrine freshwater assemblages found elsewhere, especially the unclassified protists. However, our results suggest that a fraction of Baikal protists originated evolutionary recently from marine/brackish ancestors. Moreover, our results suggest that rapid radiation may have occurred among some protist taxa, partially mirroring what was already shown for multicellular organisms in Lake Baikal. We found potential species flocks in Stramenopiles, Alveolata, Opisthokonta, Archaeplastida, Rhizaria and Hacrobia. Putative flocks predominated in Chrysophytes, which are highly diverse in Lake Baikal. Some of the species, especially those from these flocks, may be endemic from Lake Baikal, because their 18S rDNA differed > 10% from known DNA. Overall, our study points to novel diversity of planktonic protists in Lake Baikal, some of which may have emerged in situ after evolutionary diversification.
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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 | 0.001 | 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".