Effect of severe weather events on the shedding of Shigatoxigenic Escherichia coli in slaughter cattle and phenotype of serogroup O157 isolates
Bibliographic record
Abstract
High-event periods (HEPs) occur sporadically when beef carcasses and meat have episodes of acute contamination with Shiga toxin-producing Escherichia coli (STEC). In this study, severe weather events were investigated as catalysts for HEPs based on PCR and isolate prevalence of seven E. coli serogroups in slaughter cattle feces. Winter ambient temperatures with daily means 10.5oC warmer or 12.3°C colder than seasonal norms (-10.4°C) most altered STEC shedding. Fecal samples yielded increased proportions (P < 0.05) of O26 and O157 isolates during winter warm periods, and reduced (P < 0.05) O45 isolates during cold periods compared to samplings during seasonal norms. Based on changing PCR prevalence and isolates collected, O157 was the serogroup most responsive to severe weather events. Consequently, O157 isolates (n = 219) were evaluated for heat resistance, biofilm-forming potential and virulence gene subtypes. Two isolates had heat-resistant phenotypes with thermal death time at 60°C (D60) > 10 min and one also had strong biofilm-forming potential. However, this isolate lacked eae and stx genes. Severe weather can influence STEC shedding, particularly of O157, and could possibly trigger HEPs. However, our data suggest that it is unlikely for isolates to carry virulence genes and possess phenotypes capable of evading post-harvest microbiological interventions.
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 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".