Working donkey welfare assessment and owner survey in Meru County, Kenya
Bibliographic record
Abstract
) in developing countries. To-date, however, there has been limited work assessing the welfare of donkeys in many parts of Africa, including Kenya. This study aimed to characterise the unique welfare concerns of working donkeys in Meru County, Kenya. Baseline information was gathered, concerning challenges with feeding, working conditions and disease faced by owners and drivers with differences between pack and cart donkeys investigated. To this end, 102 donkeys underwent evaluation using a Standardised Equine Based Welfare Assessment Tool (SEBWAT) and 58 owners were surveyed. Important welfare concerns, including low body condition scores (BCS) (median [IQR] 2 [1.5, 2.5 out of 5]), hobbling (81/102; 79%) and mutilation wounds (49/102; 48%) were identified in all donkeys. The following categories registered significant physical differences between cart and pack donkeys: signalment (cart 100% male, pack 21% male); BCS (median cart 2.0, pack 1.5); and presence of skin wounds on the neck (cart 30%, pack 0%). Behaviour was assessed with differences noted in chin contact avoidance (cart 56%, pack 97%), tail tuck presence (cart 46%, pack 97%), number of donkeys owned (median cart 2, pack 1), reported administration of de-worming medication by owners (cart 95%, pack 17%), and occurrence of reported illness (cart 81%, pack 38%). This initial survey addresses welfare concerns related to the Meru County donkey population and will serve as a useful benchmark for future assessments as well as targeted interventions, including the introduction of modified carts to the region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".