Wild Animal Densities as Predictor of Cattle Disease Risks and Breed Types in South-Western Uganda
Bibliographic record
Abstract
Abstract Background This study investigated the spatial distribution of wild ungulates that pastoralist communities perceive as culprits in the transmission of cattle diseases outside protected areas in south-western Uganda. Diseases are hypothesized as having influence on pastoralists’ choice of cattle breed types. Until the time of his study no information was available on spatial patterns of wild animal species and cattle breeds reared in Lake Mburo Conservation Area, and how diseases transmitted therein potentially influence cattle breed herd sizes. Methods Animal population survey was carried out on cattle and wild ungulate species along transect lines laid perpendicular to the northern boundary of Lake Mburo National Park (LMNP). Household survey generated data on costs of disease control through interview. Data were analyzed for unit cost of disease control at herd level and correlated with spatial distribution patterns of cattle breeds and wild ungulate populations. Results Our results show inverse association of distance away from LMNP with wild ungulate populations and the cost of disease control, which in turn favored higher abundance of Friesian cattle. Cost of disease control and cattle abortion incidences were much lower in rangelands far away from LMNP (R² = 0.965, P < 0.001). Mean population of indigenous cattle significantly decreased while those of exotic breed increased with increasing distance away from the park boundary. Conclusion Spatial distribution of wild ungulates in cattle grazing rangelands potentially influence cattle disease prevalence levels which had significant associated with spatial patterns of cattle breeds and breed herd sizes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".