Investigation on Conditions of Microbial Contamination in Self-prepared Cold Dishes among Dietary Units in Urban Areas of Nanyang City in 2009
Bibliographic record
Abstract
[Objective]To understand the conditions of microbial contamination in self-prepared cold dishes among dietary units in urban areas of Nanyang city,to provide scientific basis for making measures for food security supervision.[Methods]In 2009,self-prepared cold dishes were collected among dietary units in urban areas of Nanyang city to examine the conditions of microbial contamination.[Results]Examination was made on 821 of 6 kinds of cold dish,the microorganism qualified rate was 62.12%.The examination qualified rate,the barbecue,77.98%,the fried,81.19%,the cooked meat,69.60%,the bean product,65.81%,the cold food in sauce meat dish,43.62%,the cold food in sauce vegetarian dish,54.70%(P0.01).In the first quarter,75.69%,the second quarter,59.29%,the third quarter,49.77%,the fourth quarter,66.33%(P0.01);With the cold dish specially processing room,the qualified rate was 70.57%,with none,it was 48.05%(P0.01).Colony total exceeding the allowed rate was 30.57%,the coli colony exceeding the allowed rate was 26.92%,pathogenic bacteria exceeding the allowed rate was 1.09%.[Conclusion]The microbial contamination is severe in self-prepared cold dishes among dietary units in urban areas of Nanyang city,most severe in sauce meat dish and in sauce vegetarian dish,most serious in the second and the third quarter,especially serious in the cold dish without processing room.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".