AI & Food Systems: The Future of the Canadian Economy
Bibliographic record
Abstract
This major research project explores the problem space of food systems driving climate change and climate change in turn, threatening the resilience of food system infrastructure and food security in Canada. A literature review covers the problems space in more detail, and potential solutions in the circular economy framework and the strategic application of AI. Synthesis of the literature review with an expert-informed Three Horizons workshop generated strategic intervention points for AI to address food system resilience and security while driving progress towards circularity. An affinity mapping exercise on the data from Horizon Three, which represent a co-envisioned future food system for Canada led to the development of a novel framework to align desired outcomes for future food systems with the core values workshop participants co-developed. This framework – LASERRS (Localism, Accessibility, & Ethics for Regeneration, Resilience, and Security), can provide a map to orient future research, development, projects, and policies in food systems such that it aligns with the values and desired co-envisioned by workshop participants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.013 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".