COMPARATIVE ANALYSIS OF DIFFERENT STRATEGIES FOR THE CONTROL OF CLASSICAL SWINE FEVER IN THE REPUBLIC OF SERBIA USING MONTE CARLO SIMULATION
Bibliographic record
Abstract
Several diff erent strategies for control and eradication of Classical Swine Fever (CSF) were compared using a Monte Carlo method-based simulationmodel. Th e control strategy analyzed in this paper, in addition to other CSF control measures, includes application of biosecurity measures on pigfarms and rural backyard holdings. Elements of the control strategy are based on applicable regulations and include the simulation of detection ofthe disease, setting up the protected and surveillance zones, standstill of pig movements and restricted movement of animals, vehicles, equipment, and people with strong control measures in protection and surveillance zones, euthanasia of susceptible pigs, protective vaccination of pigs, compensation etc. During the simulation, diff erent output parameters were compared such as: duration of epizootic of a disease, number of aff ected holdings and animals, direct costs such as those for dead or culled animals, costs of surveillance, disposal of infectious materials, cleaning and disinfection.Depopulation of aff ected animals with early diagnostics and vaccination in protection and surveillance zone proved to be the most eff ective measures to stop spreading and eradication of the disease. However, during the simulation, systematic implementation of biosecurity measures in all pig production clusters demonstrated to be appropriate strategy for sustainable control of CSF and setting up a stable epizootiological situation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".