Analysis on food poisoning incidents in Longgang Sub-district from 2004-2012
Bibliographic record
Abstract
[Objective]To analyze the occurrence regularity and epidemiological characteristics of food poisoning incidents in Longgang Sub-district of Longgang District in Shenzhen City,and provide scientific evidence for developing effective measures of prevention and control of food poisoning.[Methods]According to the archives collected of food poisoning in Longgang sub-district from 2004-2012,the descriptive statistical analysis was used to analyze the characteristics of food poisoning incidents. [Results]In the past 9 years,totally 37 food poisoning incidents were reported,with 403 poisoning cases and 2 deaths. The main reason of poison was green beans poisoning in the first quarter and microbial food poisoning in the second and third quarter. Vibrio parahaemolyticus ranked first of all pathogens. Majority of food poisoning occurred in the canteens,in which the poisoining incidents and cases accounted for 70. 27% and 65. 26%,respectively. [Conclusion]The key measures to prevent food poisoning in Longgang sub-district is to improve the propaganda of food safety knowledge( especially microorganism and green beans),to strengthen the daily supervision of canteen in factory and enterprise.
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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 | 0.003 | 0.003 |
| 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) | 0.001 | 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".