Origin of Contamination and Genetic Diversity of <i>Escherichia coli</i> in Beef Cattle
Bibliographic record
Abstract
The possible origin of beef contamination and genetic diversity of Escherichia coli populations in beef cattle, on carcasses and ground beef, was examined by using random amplification of polymorphic DNA (RAPD) and PCR-restriction fragment length polymorphism (PCR-RFLP) analysis of the fliC gene. E. coli was recovered from the feces of 10 beef cattle during pasture grazing and feedlot finishing and from hides, carcasses, and ground beef after slaughter. The 1,403 E. coli isolates (855 fecal, 320 hide, 153 carcass, and 75 ground beef) were grouped into 121 genetic subtypes by using the RAPD method. Some of the genetic subtypes in cattle feces were also recovered from hides, prechilled carcasses, chilled carcasses, and ground beef. E. coli genetic subtypes were shared among cattle at all sample times, but a number of transient types were unique to individual animals. The genetic diversity of the E. coli population changed over time within individual animals grazing on pasture and in the feedlot. Isolates from one animal (59 fecal, 30 hide, 19 carcass, and 12 ground beef) were characterized by the PCR-RFLP analysis of the fliC gene and were grouped into eight genotypes. There was good agreement between the results obtained with the RAPD and PCR-RFLP techniques. In conclusion, the E. coli contaminating meat can originate from cattle feces, and the E. coli population in beef cattle was highly diverse. Also, genetic subtypes can be shared among animals or can be unique to an animal, and they are constantly changing.
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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) | 0.000 | 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 | 0.000 |
| 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".