What Can Environmental Sequences Tell Us About the Distribution of Low‐Rank Taxa? The Case of <i>Euplotes</i> (Ciliophora, Spirotrichea), Including a Description of <i>Euplotes enigma</i> sp. nov.
Bibliographic record
Abstract
Environmental sequences have become a major source of information. High-throughput sequencing (HTS) surveys have been used to infer biogeographic patterns and distribution of broad taxa of protists. This approach is, however, more questionable for addressing low-rank (less inclusive) taxa such as species and genera, because of the increased chance of errors in identification due to blurry taxonomic boundaries, low sequence divergence, or sequencing errors. The specious ciliate genus Euplotes partially escapes these limitations. It is a ubiquitous, monophyletic taxon, clearly differentiated from related genera, and with a relatively well-developed internal systematics. It has also been the focus of several ecological studies. We present an update on Euplotes biogeography, taking into consideration for the first time environmental sequences, both traditional (Sanger) and HTS. We inferred a comprehensive small subunit rRNA gene phylogeny of the genus including a newly described marine species, Euplotes enigma, characterized by a unique question mark-shaped macronucleus. We then added available environmental sequences to the tree, mapping associated metadata. The resulting scenario conflicts on many accounts with previously held views, suggesting, for example, that a large diversity of anaerobic Euplotes species exist, and that marine representatives of mainly freshwater lineages (and vice-versa) might be more common than previously thought.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".