Strain-level identification of bacteria persisting in food and in food processing facilities: when do two isolates represent the same strain and which tools identify a strain?
Bibliographic record
Abstract
Strain-level identification informs decisions on whether two isolates represent the same strain and is used in investigations of outbreaks of foodborne disease. The same concept has only rarely been applied to nonpathogenic microbes in food or food processing facilities. Strain-level monitoring of food microbes requires definitions and tools that have only partially been developed. The review defines the concept of ‘microbial strains’ to guide the tracking of strains in food and food processing facilities. In addition, we discuss whole genome sequencing (WGS) and single-nucleotide polymorphism (SNP) calling as suitable tools for strain identification. Limitations of WGS and SNP analysis are also examined. Food spoilage causes food waste and fermented foods and probiotic foods are widely consumed. Therefore, strain identification and tracking of the food-associated microbes address a potential approach to eradicate pathogens and spoilage organisms in processing facilities and to ensure the quality of fermented foods and probiotic foods. • Genome sequences allow strain-level identification of microbes in food. • The definition of a strain depends on the context. • SNP calling requires high-quality genome sequences. • The accuracy of SNPs depends on the sequencing platform and the SNP pipeline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".