Guidance for creating individual and batch latinized binomial virus species names
Bibliographic record
Abstract
The International Committee on Taxonomy of Viruses recently adopted, and is gradually implementing, a binomial naming format for virus species. Although full Latinization of these names remains optional, a standardized nomenclature based on Latinized binomials has the advantage of comparability with all other biological taxonomies. As a language without living native speakers, Latin is more culturally neutral than many contemporary languages, and words built from Latin roots are already widely used in the language of science across the world. Conversion of established species names to Latinized binomials or creation of Latinized binomials de novo may seem daunting, but the rules for name creation are straightforward and can be implemented in a formulaic manner. Here, we describe approaches, strategies and steps for creating Latinized binomials for virus species without prior knowledge of Latin. We also discuss a novel approach to the automated generation of large batches of novel genus and species names. Importantly, conversion to a binomial format does not affect virus names, many of which are created from local languages.
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 | 0.000 |
| 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".