Analysis and Supervision Suggestions of Food Safety in Henan Province Based on Sampling Data
Bibliographic record
Abstract
This paper summarized the sampling results of food safety supervision issued by Henan Provincial Administration for Market Regulation in 2021, and analyzed them from multiple dimensions such as food category, unqualified items, sampling areas, packaging type, time, etc, so as to find out food safety risks and provide a reference for the regulatory authorities. It was found that among a total of 73918 batches of inspected food samples, 1417 batches were unqualified, with an overall qualified rate of 98.08%. The inspected samples covered 32 food categories in total. The qualified rates of 10 categories, including roasted seeds and nuts, frozen drinks, catering food, edible agricultural products, convenience food and so on, were lower than the overall qualified rate of 98.08%. The main risk indicators were excessive residues of pesticides and veterinary drugs, microbial contamination, unqualified quality index and excessive use of food additives, accounting for 88.08% of the total number of unqualified items. The unqualified rate in the fourth quarter was significantly higher than that in the first three quarters. The unqualified rate of bulk foods was higher than that of prepackaged foods. In 2021, the overall situation of food safety in Henan Province was good, but there were still varying degrees of risks in some categories and indicators. It was suggested to strengthen the supervision of high-risk categories, indicators, as well as enterprises with frequent problems, and enhance source management.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".