Options and considerations for validation of prokaryotic names under the SeqCode
Bibliographic record
Abstract
Stable taxon names for Bacteria and Archaea are essential for capturing and documenting prokaryotic diversity. They are also crucial for scientific communication, effective accumulation of biological data related to the taxon names and for developing a comprehensive understanding of prokaryotic evolution. However, after more than a hundred years, taxonomists have succeeded in valid publication of only around 30 000 species names, based mostly on pure cultures under the International Code of Nomenclature of Prokaryotes (ICNP), out of the millions estimated to reside in the biosphere. The vast majority of prokaryotic species have not been cultured and are becoming increasingly known to us via culture-independent sequence-based approaches. Until recently, such taxa could only be addressed nomenclaturally via provisional names such as Candidatus or alphanumeric identifiers. Here, we present options and considerations to facilitate validation of names for these taxa using the recently established Code of Nomenclature of Prokaryotes Described from Sequence Data (SeqCode). Community engagement and participation of relevant taxon specialists are critical and encouraged for the success of endeavours to formally name the uncultured majority.
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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 | 0.210 | 0.302 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.018 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".