Bibliographic record
Abstract
Objective To understand the prevalence of food poisoning in Guangzhou city and to provide scientific evidence for the prevention of food poisoning.Methods The analysis was conducted for the number of cases,the number of suffered individuals,the distribution of the time and place,and the causes of food paisoning occurred in the city from 1997 to 2007.Results Totally 462 cases of food poisoning occurred with 8682 poisoning cases and 37 fatalities.The average annual morbidity was 10.70/100000.The incidence rate of poisoning was higher in the second and the third quarter of the year and the mortality of food poisoning was higher in the first quarter of the year.The collective canteens were the main places of the occurrence of food poisoning and meat,meat products,fruits,and vegetables were the major food contaminated.ConclusionThe key point to prevent food poisoning is controlling the contaminations of meat,meat proiduct,fruit,and vegetable in the high incidence season.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".