Bibliographic record
Abstract
Members of the genus Pantoea are Gram-negative bacteria isolated from various environments. Taxonomic affiliation based on multilocus sequence analysis (MLSA) is used routinely for inferring accurate phylogeny and identification of bacterial species. Partial nucleotide sequences of five genes (fusA, gyrB, leuS, rpoB, and pyrG) were extracted from 206 draft or complete genomes of Pantoea strains publicly available in databases and analyzed together with the representative sequences of the 25 validly published Pantoea type strains to verify and assess their phylogenetic assignations. Of a total of 159 strains assigned to species level, 11.3% of the non-type strains were incorrectly assigned within suitable Pantoea species . The highest proportion of misidentified strains was recorded in Pantoea vagans, 8 out of 15 (53.3%) inaccurate assignations at the species level. One probable reason for this incorrect classification could be the method previously used for strain identification. Forty-seven (22.8%) genome sequences were from strains identified at the genus level only(Pantoea sp.). A combination of MLSA, average nucleotide identities (ANI and MuMmer-based ANI [ANIm]), tetranucleotide usage pattern (TETRA) and genome-based DNA-DNA hybridization (gDDH) data was used to accurately assign 23 of the 47 strains to validly published Pantoea species, while 19 strains could be assigned as putative novel species within the genus Pantoea. Positive and significant correlation coefficients were computed between MLSA and all the indices derived from whole-genome sequences being proposed for species delimitation. gDDH exhibited the best correlation with MLSA while TETRA was the worst. Accurate species-level identification is key to a better understanding of bacterial diversity and evolution. The MLSA scheme used here could be instrumental to determine the correct taxonomic status of new whole-genome sequenced Pantoea strains, especially non-type strains, before depositing into public databases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".