‘What’s in a name? Fit-for-purpose bacterial nomenclature’: meeting report
Bibliographic record
Abstract
Rapid and economical DNA sequencing has resulted in a revolution in phylogenomics. The impact of changes in nomenclature can be perceived as an absolute necessity of scientific rigour, coupled with the slight inconvenience of needing to re-learn names. In relation to practical aspects of microbiology, for example, infectious disease diagnosis, there may, however, be potential dangers. Historically, prokaryote classification has been based on multiple metabolic, physiological, biochemical and descriptive characteristics combined with the environmental source. Whole-genome sequence data have transformed our ability to determine evolutionary relationships. In addition, metagenomic and metataxonomic sequencing have resulted in the discovery of novel microbes, many of which are yet to be cultured. As a result, occasional name changes and additional prokaryote discoveries have accelerated at an unprecedented pace. Herein is a report of a Microbiology Society supported meeting of representatives of the communities of specialist taxonomists, phylogeneticists and applied microbiologists. Discussion included: recent advances in phylogenomics and the potential impact of nomenclature change on practical microbiology, e.g. plant pathology, food security, industrial microbiology, clinical microbiology and infectious diseases; the need, or lack thereof, for wider consideration and consultation prior to nomenclature change proposals which impact on practical microbiology; the application of the intricate and highly necessary rules of prokaryote nomenclature, which sometimes appear unfathomable to the non-specialist; and genome-based phylogenomics and the relationship with the International Code of Nomenclature of Prokaryotes. The meeting resulted in the formation of the Ad Hoc Committee for Mitigating Changes in Prokaryotic Nomenclature under the auspices of the International Committee on Systematics for Prokaryotes.
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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 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; 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".