Technologies for automatic assessment of pig welfare using animal-based indicators in the slaughterhouse: a review
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
Most meat-producing species end their life at the slaughterhouse. Here, animals are gathered from diverse farms, allowing for extensive data collection, including on welfare status. Assessing animal welfare requires reliable indicators, particularly those that are animal-based. Automated welfare evaluation offers a continuous, objective, and consistent approach for monitoring large numbers of animals, eliminating human bias and fatigue associated with high-speed production lines, and decreasing farm visits. This review aims to identify animal-based welfare indicators for pigs that can be automatically measured at slaughterhouses and to examine commercially available Precision Livestock Farming (PLF) technologies used at the slaughterhouse, including prototypes and on-farm technologies that can be adapted and applied to slaughterhouses. A three-step methodology is used: first a systematic literature search, followed by a comprehensible commercial search, and finally an expert consultation survey to confirm that all technologies were identified. A total of 16 technologies for slaughterhouse applications and 71 technologies for on-farm use were identified. Among the on-farm technologies, 52 were deemed feasible for slaughterhouse implementation, while 19 were considered unsuitable due to mismatches with slaughterhouse purposes, such as feeding behaviour or heat detection. The results also highlight the need to address automated welfare assessment during the transport phase to ensure thorough understanding and continuous monitoring of animal welfare across the entire production chain. While automated systems for monitoring pig welfare show significant potential, challenges in practical implementation and widespread adoption remain, requiring collaboration between researchers, industry stakeholders, and technology developers to fully realise their potential. • Sensors can be used to monitor in-situ and retrospective welfare at slaughterhouses. • On-farm sensors could assess welfare at slaughterhouses, especially in lairage areas. • Technologies require validation for reliable use in slaughterhouse settings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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