PATH-SAFE Consortium Recommendations for Genomic Surveillance of Food-Borne Diseases Escherichia Coli and Listeria Monocytogenes
Bibliographic record
Abstract
Whole-genome sequencing (WGS) for food-borne disease (FBD) surveillance provides many benefits, including new insights in disease transmission, virulence and antimicrobial resistance (AMR), fast and precise outbreak tracing and source attribution, as well as streamlined and reproducible analysis through digital data that, from a technical point of view, can be easily shared.The National foodborne disease genomic data platform (the PATH-SAFE platform) will offer a trusted environment for WGS data sharing and analysis for UK agencies involved in FBD surveillance.Following the successful implementation of the platform for Salmonella, in the second phase the platform will be expanded to Escherichia coli and Listeria monocytogenes.Where possible, the platform will draw on existing and validated solutions.For de novo genome assembly, EToKI and vanilla SPAdes provide the best results for E. coli, and Pathogenwatch provides the best results for L. monocytogenes.Analysis of genomic data is greatly enhanced by assigning genomes into well-defined cluster groups, which should be available on the PATH-SAFE platform.Specifically, tools for MLST
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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 | 0.034 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".