Spatial Relative Risk Mapping of Pseudorabies‐Seropositive Pig Herds in an Animal‐Dense Region
Bibliographic record
Abstract
This paper describes a new approach for spatial relative risk mapping as a tool for geographical risk assessment. The spatial epidemiological analysis is based on geographically referenced data about pseudorabies (Aujeszky's disease) virus infections at farm level in a region of high animal density in Germany at the beginning of the national eradication project. On the basis of serological findings 186 farms were classified as positive out of a total of 482 investigated farms listed in veterinary administrative registers. Geographical cluster analysis was used to identify two areas of high risk (RR = 2.4 and 3.3). Non-parametric density estimation was used to estimate the proportion of infected farms per square kilometre. Furthermore, the spatial relative risk function was approximated through the prevalence ratio defined by the ratio of the local prevalence and the overall prevalence of the farms outside the cluster regions. The corresponding approximated relative risk map indicates and quantifies a clear spatial pattern of disease frequency.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".