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.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".