Metaverse for Enhancing Animal Welfare - Leveraging Sensor Technology and Ethical Considerations
Bibliographic record
Abstract
The metaverse, a virtual world where real-world aspects merge with artificial intelligence, immersive experiences, and high-level digital connectivity, is increasingly being applied in animal farming. This integration offers significant opportunities for addressing climate change and promoting sustainable food production. However, it also raises several ethical issues, particularly concerning animal rights. This paper evaluates these ethical considerations, emphasizing the need for a thorough examination of how sensor technology affects animals' perception and autonomy. Key findings indicate that while metaverse technologies can enhance animal welfare through improved monitoring and optimized living conditions, they also pose risks of detachment and commodification. The design of animal-friendly environments must balance technological advancement with ethical approaches to animal welfare. Critical factors such as ethical reflection, socio-economic impact, and the ability to retrieve meaningful information must be considered to maintain sensitivity and trust in these technologies. Moreover, the paper highlights the importance of addressing inequalities in access and adoption of metaverse technologies, which can significantly benefit animal farming. The potential of the metaverse to revolutionize the agri-food sector, particularly in animal agriculture, remains vast but requires further research to fully understand its implications. This paper concludes that a conscientious and ethical approach is essential for integrating metaverse technologies into animal farming, ensuring that animal welfare and equitable practices are prioritized for a sustainable future.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".