Options and considerations for validation of prokaryotic names under the SeqCode
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it