TYGS and LPSN in 2025: a Global Core Biodata Resource for genome-based classification and nomenclature of prokaryotes within DSMZ Digital Diversity
Bibliographic record
Abstract
The List of Prokaryotic names with Standing in Nomenclature (LPSN, https://lpsn.dsmz.de/) is an authoritative, expert-curated resource on prokaryotic nomenclature. Its sister database, the Type (Strain) Genome Server (TYGS, https://tygs.dsmz.de/), is a high-throughput platform for genome-based taxonomy. Here we present updates to these two platforms. New tools include improved interoperability with other DSMZ Digital Diversity databases, as well as easy-to-use query and access functions, and more comprehensive submission forms. The database content has expanded considerably, particularly through the inclusion of thousands of cyanobacterial names and the compilation of the List of Recommended Names for bacteria of medical importance (LoRN). LPSN now contains over 59 000 taxon names, and over 23 500 genome sequences have been added to TYGS. LPSN and TYGS have been updated to incorporate and reflect changes to the official rules governing prokaryotic nomenclature, as ratified by the International Committee on Systematics of Prokaryotes (ICSP) in recent years.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.042 |
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".