ANI, Mash and Dashing equally differentiate between <i>Klebsiella</i> species
Bibliographic record
Abstract
ABSTRACT Species of the genus Klebsiella are among the most important multidrug resistant human pathogens, though they have been isolated from a variety of environments. Given the need for quickly and accurately classifying newly sequenced Klebsiella genomes, we compared 982 Klebsiella genomes using different species-delimiting measures: Average Nucleotide Identity (ANI), which is becoming a standard for species delimitation, as well as Mash, Dashing, and DNA compositional signatures, which can be run in a fraction of the time required to run ANI. ROC analyses showed equal quality in species delimitation for ANI, Mash and Dashing (AUC: 0.99), followed by DNA signatures (AUC: 0.96). The groups obtained at optimal cutoffs were largely in agreement with species designation. Using optimized cutoffs, we obtained 17 species-level groups using either ANI, Mash, or Dashing, all containing the same genomes, unlike DNA signatures which broke the dataset into 38 groups. Further use of Mash to map species after adding draft genomes to the dataset also showed excellent results (AUC: 0.99), producing a total of 28 Klebsiella species in the publicly available genome collection. The ecological niches of Klebsiella strains were found to neither be related to species delimitation, nor to protein functional content, suggesting that a single Klebsiella species can have a wide repertoire of ecological functions.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".