Rapid Identification of Stable Clusters in Bacterial Populations Using the Adjusted Wallace Coefficient
Bibliographic record
Abstract
Abstract Whole-genome sequencing (WGS) of microbial pathogens has become an essential part of modern epidemiological investigations. Although WGS data can be analyzed using a number of different approaches, such as traditional phylogenetic methods, a critical requirement for global systems for pathogen surveillance is the development of approaches for transforming sequence data into WGS-based subtypes, which creates a nomenclature that describes their higher-order relationships to one another. To this end, subtype similarity thresholds are needed to define clusters of subtypes representing lineages of interest. WGS-based subtyping presents a challenge since both the addition of novel genome sequences and small adjustments in similarity thresholds can have a dramatic impact on cluster composition and stability. We present the Neighbourhood Adjusted Wallace Coefficient (nAWC), a method for evaluating cluster stability based on computing cluster concordance between neighbouring similarity thresholds. The nAWC can be used to identify areas in in which distance thresholds produce robust clusters. Using datasets from Salmonella enterica and Campylobacter jejuni , representing strongly and weakly clonal bacterial species respectively, we show that clusters generated using such thresholds are both stable and reflect basic units in their overall population structure. Our results suggest that the nAWC could be useful for defining robust clusters compatible with nomenclatures for global WGS-based surveillance networks, which require stable clusters to be defined that both harness the discriminatory power of WGS data while allowing for long-term tracking of strains of interest.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".