EukRef‐Ciliophora: a manually curated, phylogeny‐based database of small subunit rRNA gene sequences of ciliates
Bibliographic record
Abstract
High-throughput sequencing (HTS) surveys, among the most common approaches currently used in environmental microbiology, require reliable reference databases to be correctly interpreted. The EukRef Initiative (eukref.org) is a community effort to manually screen available small subunit (SSU) rRNA gene sequences and produce a public, high-quality and informative framework of phylogeny-based taxonomic annotations. In the context of EukRef, we present a database for the monophyletic phylum Ciliophora, one of the most complex, diverse and ubiquitous protist groups. We retrieved more than 11 500 sequences of ciliates present in GenBank (28% from identified isolates and 72% from environmental surveys). Our approach included the inference of phylogenetic trees for every ciliate lineage and produced the largest SSU rRNA tree of the phylum Ciliophora to date. We flagged approximately 750 chimeric or low-quality sequences, improved the classification of 70% of GenBank entries and enriched environmental and literature metadata by 30%. The performance of EukRef-Ciliophora is superior to the current SILVA database in classifying HTS reads from a global marine survey. Comprehensive outputs are publicly available to make the new tool a useful guide for non-specialists and a quick reference for experts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".