<i>UniEuk</i>: Time to Speak a Common Language in Protistology!
Bibliographic record
Abstract
Universal taxonomic frameworks have been critical tools to structure the fields of botany, zoology, mycology, and bacteriology as well as their large research communities. Animals, plants, and fungi have relatively solid, stable morpho-taxonomies built over the last three centuries, while bacteria have been classified for the last three decades under a coherent molecular taxonomic framework. By contrast, no such common language exists for microbial eukaryotes, even though environmental '-omics' surveys suggest that protists make up most of the organismal and genetic complexity of our planet's ecosystems! With the current deluge of eukaryotic meta-omics data, we urgently need to build up a universal eukaryotic taxonomy bridging the protist -omics age to the fragile, centuries-old body of classical knowledge that has effectively linked protist taxa to morphological, physiological, and ecological information. UniEuk is an open, inclusive, community-based and expert-driven international initiative to build a flexible, adaptive universal taxonomic framework for eukaryotes. It unites three complementary modules, EukRef, EukBank, and EukMap, which use phylogenetic markers, environmental metabarcoding surveys, and expert knowledge to inform the taxonomic framework. The UniEuk taxonomy is directly implemented in the European Nucleotide Archive at EMBL-EBI, ensuring its broad use and long-term preservation as a reference taxonomy for eukaryotes.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.072 | 0.103 |
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".