Enabling Responsible AI-Driven Agri-Food Innovation in Ontario: Challenges and Opportunities
Bibliographic record
Abstract
Adopting artificial intelligence (AI) in Ontario’s agri-food sector can enhance productivity and boost competitiveness, market opportunities, and sector strength. However, without responsible innovation that addresses societal concerns, the sector might face issues like adoption indifference, job displacement, inequality, and ethical dilemmas. AI adoption varies by sector and value chain, necessitating different skills for workers based on the AI technologies implemented. Responsible innovative technologies in agriculture have the potential to generate jobs that attract multiple skills. Initiatives focusing on skill development in digital farming technologies can increase the employability of individuals from various backgrounds and promote inclusivity in the agricultural sector. This research employs a mixed-methods approach to explore key factors influencing AI adoption in Ontario’s horticultural and livestock sectors, associated challenges and opportunities, and essential skills and knowledge for agri-food workers. The goal is to understand how AI can responsibly increase the competitiveness and growth of Ontario’s agri-food sector. It will provide opportunities for the improved support of agri-food workers, enhanced public policy, and strategic investments in programs that can empower a broader range of skills needed to contribute to the industry's technological transformation. Preliminary findings highlight key competencies for AI adoption, associated challenges, and potential benefits.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".