Modelling the Movement of Livestock Trade under Uncertain Conditions
Bibliographic record
Abstract
Livestock movement is an intrinsic part of animal husbandry (i.e., breeding, maintenance, slaughter of livestock), significantly impacting global economies and public health due to its potential for contagious disease spread. This dissertation focuses on two key areas: understanding uncertainties in cattle movement networks and their correlation with epidemic modelling, and introducing a methodology that characterises mobility patterns as flows while considering spatial aspects to realistically capture uncertainties and associated risks. There are an estimated one billion cattle heads in the world used for various products. Delivering these animal goods efficiently leads to stress in the system by increasing the number of animals kept, traded, and their possible contacts. This situation poses significant economic threats due to highly contagious diseases like brucellosis and foot-and-mouth disease, which spread easily through contact. While some countries have precise monitoring through electronic tags (e.g., Australia, Canada), many major producers lack such tracking systems (e.g., Mexico, USA), enabling unregulated cattle trade and introducing uncertainty into official movement data. These uncertainties directly impact epidemic behaviour estimation, leading us to model three common uncertainties in cattle movement networks and evaluate them through simulations in synthetic and real networks from Minas Gerais, Brazil. We introduce a general methodology to complement the benefits of network analyses. Our proposed methodology converts movement networks into vector-field representations, significantly accelerates simulations and enhances understanding of risk areas, trade predictability, and interest points like sinks and sources. While demonstrating its effectiveness in cattle trading, this approach is flexible and can be applied to various areas ( if we model the phenomena as cases of origin-destination (O-D) flows), including human mobility.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".