[Proposal of reference values of microbiological environment monitoring in foodservice establishments].
Bibliographic record
Abstract
INTRODUCTION: To propose a new reference of microbiological environment monitoring in foodservice establishments. OBJECTIVE: The present work shows the determination and evaluation of the microbiological contamination generated in a foodservice establishment Method: It is based on surface sampling (microbial build-up) using mixed cellulose ester membrane filters and on air sampling (hourly microbial adhesion) using Petri dishes. RESULTS: Limits of contamination are established before and during the food elaboration, by means of the microbiological analysis of the environment, surfaces and equipment systems, until reliable limits and levels of acceptance are established of each selected point. Finally, a program of environmental microbiological monitoring was established including the evaluation of all parameters that compose and are implicit in the area, thus assuring and supporting its continuity with the documentation and registers developed for a safety area. Samples for microbiological examination were collected over a period of one moth on ten different days, at two different times. Twelve selected points having previously been identified as hazardous were monitored. Furthermore, foods though to be of high risk were periodically collected for microbiological analysis. CONCLUSIONS: The possibility to use of an ample range of selective media, well over the limited number used in this study, allows the analysis of many single microbial species.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".