The Microbiological Effects of Procedures Used in Commercial Practice for Cleaning Mechanical Tenderizing Equipment Used With Beef
Bibliographic record
Abstract
The microbiological effects of routine cleaning of a common type of blade tenderizing equipment (Ross TC700MC) used with beef at a retail store meat fabrication facility were investigated. Swab samples were obtained from various parts of the equipment before and after its use on each of 5 days, with 17 samples being obtained on each occasion. The median numbers of aerobes recovered before or after use each day were mostly not significantly different (P > 0.05) and > 3.5 log cfu/sample. Enterobacteriaceae and coliforms were recovered after use each day at total numbers of 2.5 – 4.2 and 2.4 – 3.2 log cfu, respectively; and sometimes before use at total numbers of 1.7 – 3.9 and 0.7 – 2.1 log cfu, respectively. With more careful performance of cleaning procedures by facility staff and storing in a chiller, the numbers of aerobes recovered from the tenderizer before use were 3 log units less than the numbers found on the used equipment, and Enterobacteriaceae and coliforms were not recovered. Studies at a laboratory with a tenderizer used with beef cuts showed that cleaning was equally effective for reducing numbers of aerobic bacteria by ? 3 log units when carried out using water of 90 °C or 55 °C; and that drying of equipment was necessary to prevent growth of Enterobacteriaceae and coliforms on cleaned equipment not stored at chiller temperatures.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".