Assessment of regional vulnerability to Africa swine fever in China during 2018/8‐2019/7 based on data envelopment analysis method
Bibliographic record
Abstract
In this study, we introduce a vulnerability index to measure the regional ASF epidemic and present the ASF severity ratings of the 31 provinces of mainland China. The index is defined based on the data from the investigation, national statistical yearbook and reports. The data to be used include pig breeding, financial resources, human resources, epidemic information of ASF and price fluctuation from the 31 provinces. Then, we use the data envelopment analysis (DEA) method to define the vulnerability index, the relative severity value for each region, which quantitatively reflects the damage degree caused by the epidemic of ASF. The method allows us to provide a systematic classification for the regional vulnerability level of ASF in China. Using this index, we find that the vulnerability of the whole country is at a high level, and there is no regional aggregation phenomenon. The vulnerability level of the 31 provinces is quite different and the provinces with high vulnerability level are dispersive geographically. For the five major prevention and control zones for ASF in China, the northern region has the highest vulnerability level, while the eastern zoon level is the lowest.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".