After the storm—Perspectives on the taxonomy of Lactobacillaceae
Bibliographic record
Abstract
In 2020, a taxonomic reorganization of the lactic acid bacteria reclassified over 300 species in 7 genera and 2 families into one family, the Lactobacillaceae , with 31 genera including 23 new genera to include organisms formerly classified as Lactobacillus species. This communication aims to provide a debrief on the taxonomic reorganization of lactobacilli to identify shortcomings in the proposed taxonomic framework, and to outline perspectives and opportunities provided by the current taxonomy of the Lactobacillaceae . The current taxonomy of lactobacilli not only necessitates becoming familiar with 23 new genus names but also provides substantial new opportunities in scientific discovery and regulatory approval of these organisms. First, description of new species in the Lactobacillaceae is facilitated and a solid framework for description of novel genera is provided. Second, the current taxonomy greatly enhances the resolution of genus-level sequencing approaches (e.g., 16S rRNA–based metagenomics) when identifying the composition and function of microbial communities. Third, the current taxonomy greatly facilitates the formulation of hypotheses linking phylogeny to metabolism and ecology of lactobacilli.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".