Reply to Oren et al., “New Phylum Names Harmonize Prokaryotic Nomenclature”
Bibliographic record
Abstract
We thank Oren et al., 2022 (1) for providing the International Committee on Systematics of Prokaryotes (ICSP) Executive Board's response to the recent opinion piece “Harmonizing Prokaryotic Nomenclature: Fixing the Fuss over Phylum Name Flipping” (2). \n \nHerein, we (a group of concerned microbiologists) offer several additional comments on the naming of prokaryotic phyla. Our concerns arise from the perception that the ICSP Executive Board is not directly addressing the plight of microbiologists around the world concerning the long-lasting confusion that the proposed phylum name changes will create throughout the scientific literature. Instead of selectively pointing out certain ICSP rules, we would have appreciated opening a door for a more inclusive discussion about the process of naming phyla. As previously stated (2), we are in favor of the International Code of Nomenclature of Prokaryotes (ICNP) rules for the recognition of the rank of “phylum” and the usage of “-ota” as the suffix for naming phyla. However, we feel that the drastic name changes of six phyla at the prefix level will create significant dissonance and confusion between the old and new names when considering past and future publications.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.054 | 0.063 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".