Table_2_Source Tracking Based on Core Genome SNV and CRISPR Typing of Salmonella enterica Serovar Heidelberg Isolates Involved in Foodborne Outbreaks in Québec, 2012.DOCX
Bibliographic record
Abstract
<p>Whole-genome sequencing (WGS) is the method of choice for bacterial subtyping and it is rapidly replacing the more traditional methods such as pulsed-field gel electrophoresis (PFGE). Here we used the high-resolution core genome single nucleotide variant (cgSNV) typing method to characterize clinical and food from Salmonella enterica serovar Heidelberg isolates in the context of source attribution. Additionally, clustered regularly interspaced short palindromic repeats (CRISPR) analysis was included to further support this method. Our results revealed that cgSNV was highly discriminatory and separated the outbreak isolates into distinct clusters (0–4 SNVs). CRISPR analysis was also able to distinguish outbreak strains from epidemiologically unrelated isolates. Specifically, our data clearly demonstrated the strength of these two methods to determine the probable source(s) of a 2012 epidemiologically characterized outbreak of S. Heidelberg. Using molecular cut-off of 0–10 SNVs, the cgSNV analysis of 246 clinical and food isolates of S. Heidelberg collected in Québec, in the same year of the outbreak event, revealed that retail and abattoir chicken isolates likely represent an important source of human infection to S. Heidelberg. Interestingly, the isolates genetically related by cgSNV also harbored the same CRISPR as outbreak isolates and clusters. This indicates that CRISPR profiles can be useful as a complementary approach to determine source attribution in foodborne outbreaks. Use of the genomic analysis also allowed to identify a large number of cases that were missed by PFGE, indicating that most outbreaks are probably underestimated. Although epidemiological information must still support WGS-based results, cgSNV method is a highly discriminatory method for the resolution of outbreak events and the attribution of these events to their respective sources. CRISPR typing can serve as a complimentary tool to this analysis during source tracking.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".