Status of the Global Burden of Animal Diseases program: methodology and applications
Bibliographic record
Abstract
The Global Burden of Animal Diseases (GBADs) program was initiated in 2018, with the aim of systematizing the analysis and quantification of the economic impact of animal diseases worldwide. This article provides an overview of the methodological approach developed by the program to date, from estimating the biomass and economic value of livestock to the impact of animal diseases at the farm level and on national economies. Several case studies are presented to illustrate the different stages of the analysis: economic value, loss envelope, attribution of losses to specific causes, and impacts on society. These case studies cover different countries, Denmark, Ethiopia, Indonesia, Ireland, Senegal, South Africa, and Switzerland, and different animal species. An overview of the challenges and opportunities faced by the consortium is provided.
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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 | 0.070 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".