Whole-Genome Sequence Datasets: A Powerful Resource for the Food Microbiology Laboratory Toolbox
Bibliographic record
Abstract
Whole-genome sequencing (WGS) technologies are rapidly being adopted for routine use in food microbiology laboratories worldwide. Examples of how WGS is used to support food safety testing include gene marker discovery (e.g., virulence and anti-microbial resistance gene determination) and high-resolution typing (e.g., cg/wgMLST analysis). This has led to the establishment of large WGS databases representing the genomes of thousands of different types of food pathogenic and commensal bacteria. This information constitutes an invaluable resource that can be leveraged to develop and validate routine test methods used to support regulatory and industry food safety objectives. For example, well-curated raw and assembled genomic datasets of the key food pathogens (Salmonella enterica, Listeria monocytogenes, and Shiga-toxigenic Escherichia coli) have been used in our laboratory in studies to validate bioinformatics pipelines, as well as new molecular methods as a prelude to the laboratory phase of the “wet lab” validation process. The application of genomic information to food microbiology method development will decrease the cost of test development and lead to the generation of more robust methodologies supporting risk assessment and risk management actions.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.035 |
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".