Bibliographic record
Abstract
The nomenclature of prokaryotes is regulated by the rules of the International Code of Nomenclature of Prokaryotes (ICNP) and is based on the Linnaean binomial system. The current rules of the Code only cover the nomenclature of the cultivated minority. Proposals to incorporate the uncultivated majority of bacteria and archaea under the rules of the Code were recently rejected by the International Committee on Systematics of Prokaryotes. The provisional rank of Candidatus can be used to name uncultivated prokaryotes whose names cannot be validly published under the rules of the ICNP, but their names can now be validated under the Code of Nomenclature of Prokaryotes Described from Sequence Data (the SeqCode), which was recently established to cover the nomenclature of the uncultivated majority. Metagenomics, single-cell genomics, and high-throughput cultivation techniques have led to a flood of new organisms currently waiting to be named. Automated programs such as GAN and Protologger can assist researchers in naming and describing newly discovered prokaryotes, cultivated as well as uncultivated. However, Latin and Greek skills remain indispensable for proper quality control of names that must meet the standards set by the codes of nomenclature.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".