Monitoring and analysis on the disinfection effect of 20 beadhouses in Changning district of Shanghai
Bibliographic record
Abstract
Objective To understand the disinfection effect of beadhouses.Method Using exposed nutrient agar plate to monitor the air.Tableware surface,environment object surface and the staff's hands were coated 25cm2 with a cotton swap moistened with sterile saline,all of those examined and evaluated according to the Nurseries environment,air,and object surface's hygienic standard(DB31/8-2004).Results The overall pass rate was 89.86%.The pass rates of the first,second and third quarter were 94.31%,77.61% and 91.67%,respectively.There was no significant difference between the first and the third quarter(χ2=0.59,P0.05).However,there were marked differences between the first and second quarter,the second and third quarter(χ2=10.20,P0.01;χ2=5.33,P0.05).Among the 4 test projects,the order of the overall past rate(highest to lowest) were room air(100.00%),environment object surface(93.42%),tableware surface(91.47%),staff hands(74.47%).In 29 positive objects,hand was accounted for 44.83%,environment objects surface was 37.93% and tableware surface was 17.24%.Coliforms and staphylococcus aureus were detected out.Conclusions We should draw up the management and disinfection measures direct towards to beadhouse's communicable disease,increase training of professional disinfectant knowledge,pay a key attention to disinfect hands and environment object surfaces and protect the health of elderly population.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".