Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation
Bibliographic record
Abstract
The metaverse is a virtual world, consisting of a collective virtual shared space where users can interact with one another through avatars and computer-generated objects. Its goal is to mimic our real world as closely as possible, integrating elements of various trends like AI, immersive reality, advanced connectivity, and Web3. While there is currently no universally accepted definition of the metaverse, the emergence of metaverse technologies across multiple sectors, including animal farming, is rapidly gaining momentum. The potential value of the metaverse, particularly in relation to its capacity for solving complex problems (e.g., climate change and sustainability) in precision food production systems makes it an exciting endeavor. However, it is crucial to consider ethical implications during the development of metaverse technologies for modern animal farming, given the sensitive and controversial nature of animal welfare. Failure to address these ethical considerations could lead to a lack of credibility and insensitivity towards the adoption of metaverse technologies in the animal farming sector. It is therefore important to ensure that the development of metaverse technologies does not prioritize technology over animal welfare, ethics, and socio-economic implications. Additionally, addressing the topic of diversity and equity in the context of animal farming and the metaverse is crucial to avoid perpetuating existing inequalities during the implementation of metaverse technologies. The purpose of this critical review is to stimulate dialogue among stakeholders such as farmers, animal scientists, bioengineers, veterinarians, policymakers, consumers, and business-to-business clients. It aims to help them better understand the potential and power of the metaverse, identify ethical implications and strategic imperatives, and act as a force for its positive evolution.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".