A financial cost-benefit analysis of eradicating virulent footrot
Bibliographic record
Abstract
In 2008, virulent footrot was detected in sheep in south-west Norway. Footrot is caused by Dichelobacter nodosus, and the outbreak was linked to live sheep imported from Denmark in 2005. A large-scale program for eradicating the disease was implemented as a joint industry and governmental driven eradication project in the years 2008-2014, and continued with surveillance and control measures by the Norwegian Food Safety Authority from 2015. The cost of the eradication program including surveillance and control measures until 2032 was assumed to reach approximately €10.8 million (NOK 90 million). A financial cost-benefit analysis, comparing costs in the eradication program with costs in two simulated scenarios, was carried out. In the scenarios, designated ModerateSpread (baseline) and SlowSpread, it was assumed that the sheep farmers would undertake some voluntary measures on their own that would slow the spread of the disease. The program obtained a positive NPV after approximately 12 years. In a stochastic analysis, the probabilities of a positive NPV were estimated to 1.000 and to 0.648 after 15 years and to 0.378 and 0.016 after ten years, for the ModerateSpread and SlowSpread scenarios respectively. A rapid start-up of the program soon after the detection of the disease was considered crucial for the economic success as the disease would have become more widespread and probably raised the costs considerably at a later start-up.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".