WELL-E: A Living Lab of Responsible Digitalization in the Canadian Dairy Industry
Bibliographic record
Abstract
Evolving theoretical frameworks of responsible and inclusive innovation argue that systems change must properly address real-world stakeholder needs and create positive impacts for society and the environment. These principles lie at the heart and mission of the Research and Innovation Chair in Animal Welfare and Artificial Intelligence (WELL-E), currently running at two locations: a university teaching farm and a vocational training farm of incarcerated persons. Our team works to integrate intentionally both stakeholder and domain expert knowledge with cutting-edge artificial intelligence (AI) methods and technological tools for the improvement of animal (and human) welfare. We have been working directly with farm staff and management to co-develop and pilot new practices for animal housing and management, as well as to test cutting-edge technologies and practices for the deployment of responsible AI tools on farms, embracing F.A.I.R. principles and empowering end users to be at the forefront of these innovations. Our collaborative approach promotes responsible and inclusive innovation through the integration of new technologies into the dairy industry and empowers producers and workers to be at the forefront of positive welfare developments, ensuring their sustainability and reinforcing the importance of stakeholder participation in innovative scientific research.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".