Survival of O157 and non-O157 shiga toxin-producing Escherichia coli in Korean style kimchi
Bibliographic record
Abstract
Korean style kimchi contaminated with Shiga toxin-producing Escherichia coli (STEC) O157:H7 was the cause of an outbreak in Canada from December 2021 to January 2022. To determine if this STEC O157:H7 has greater potential for survival in kimchi than other STEC, the outbreak strain and six other STEC strains (O26:H11, O91:H21, O103:H2, O121:H19, and two O157:H7) were inoculated individually at 6 to 6.5 log CFU/g into commercially sourced kimchi and incubation at 4 °C. At intervals of seven days inoculated and control kimchi was plated onto MacConkey agar to enumerate lactose utilising bacteria. The colony counts were interpreted as enumerating the inoculated STEC, since no colonies were observed on MacConkey agar plated with uninoculated kimchi. Over eight weeks of incubation the pH was stable at 4.10 to 4.05 and the STEC strains declined by 0.7-1.0 log, with a median reduction of 0.9 log. The linear rate of reduction of kimchi outbreak STEC O157:H7 was -0.4 log per 30 days (Slope Uncertainty 0.05), which was not significantly different from the other O157 and nonO157 STEC strains (P = 0.091). These results indicate that the outbreak was not due to the presence of strain better adapted to survival in kimchi than other STEC, and that STEC can persist in refrigerated Korean style kimchi with a minimal decline over the shelf-life of the product.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".