SeqCode facilitates naming of South African rhizobia left in limbo
Bibliographic record
Abstract
South Africa is well-known for the diversity of its legumes and their nitrogen-fixing bacterial symbionts. However, in contrast to their plant partners, remarkably few of these microbes (collectively referred to as rhizobia) from South Africa have been characterised and formally described. This is because the rules of the International Code of Nomenclature of Prokaryotes (ICNP) are at odds with South Africa's National Environmental Management: Biodiversity Act and its associated regulations. The ICNP requires that a culture of the proposed type strain for a novel bacterial species be deposited in two international culture collections and be made available upon request without restrictions, which is not possible under South Africa's current national regulations. Here, we describe seven new Mesorhizobium species obtained from root nodules of Vachellia karroo, an iconic tree legume distributed across various biomes in southern Africa. For this purpose, 18 rhizobial isolates were delineated into putative species using genealogical concordance, after which their plausibility was explored with phenotypic characters and average genome relatedness. For naming these new species, we employed the rules of the recently published Code of Nomenclature of Prokaryotes described from Sequence Data (SeqCode), which utilizes genome sequences as nomenclatural types. The work presented in this study thus provides an illustrative example of how the SeqCode allows for a standardised approach for naming cultivated organisms for which the deposition of a type strain in international culture collections is currently problematic.
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 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".