Characterization of an extremely heat-resistant Escherichia coli obtained from a beef processing facility
Bibliographic record
Abstract
AIMS: This study aimed to determine the survival of Escherichia coli strains during steam and lactic acid decontamination interventions currently used by the beef-processing industry, and to determine their heat resistance. METHODS AND RESULTS: Strains were grouped into cocktails of five strains each differing in their RAPD patterns for subsequent identification. Steam and lactic acid treatments on meat reduced cell counts of E. coli strain cocktails by 90-99%. The 20 slaughter plant isolates exhibited only minor variation in their resistance to steam and lactic acid treatments but were more resistant than reference strains (three strains) or isolates from live cattle (seven strains). D(60) values of strains from live cattle, and reference strains ranged from 0·1 to 0·5 min, in keeping with literature data. However, D(60) values of current slaughter plant isolates ranged between 15 for E. coli DM18.3 and 71 min AW 1.7. Cell counts of E. coli AW 1.7 were reduced by <5 log(10) CFU g(-1) in ground beef patties cooked to an internal temperature of 71°C. CONCLUSIONS: Strains of E. coli that survive cooking of ground beef to the recommended internal temperature of 71°C can be isolated from beef-processing facilities. SIGNIFICANCE AND IMPACT OF THE STUDY: Pathogen interventions in current commercial beef slaughter may select for extremely heat-resistant strains of E. coli.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".