Improvement of animal welfare by strategic analysis and logistic optimization of animal slaughter transportation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The transportation of animals to slaughterhouses is a major welfare concern. The number of slaughterhouses has decreased over time in Europe due to centralisation. This is expected to increase transport time for animals and as a consequence negatively affect animal welfare. We propose an optimisation model based on a facility location model to perform strategic analysis to improve transportation logistics. The model is tested on the Swedish slaughter transport system. We show that, by strategic planning and redirection of transports while keeping the slaughterhouse capacities as of the original data, the potential exists to reduce transport distance by 25% for pigs and 40% for cattle. Furthermore, we demonstrated that approximately 50% of Swedish slaughterhouses can be shut down with a minimal effect on total transport distances. This implies that in terms of the overall welfare picture, the decision of which animals to send where plays a far more significant role than the number of slaughterhouses. In addition, by changing relative weights on distances in the optimisation function the amount of individual transports with long journey times can be decreased. We also show results from altered slaughterhouse capacity and geographical location of slaughterhouses. This is the first time an entire country has been analysed in great detail with respect to the location, capacity and number of slaughterhouses. The focus is mainly on the analysis of unique and detailed information of actual animal transports in Sweden and a demonstration of the potential impact redirection of the transports and/or altering of slaughterhouses can have on animal welfare.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it