Cross-border climate risks of Canada's fruit and vegetable supply
Bibliographic record
Abstract
Fruit and vegetable supply is essential for a healthy diet, yet their vulnerability to climate risks is often overlooked, particularly in terms of cross-border risks. In this study, we assess the exposure of Canada's fruit and vegetable supply to future weather extremes. To do so, we first develop provincial scale food flows for 18 fruits and 16 vegetables from 2018 to 2022, resolving for interprovincial and international trade. Integrating this novel dataset with up-to-date global climate simulations, we assess Canada's consumption-based and cross-border future exposure to excessive rain, dry spells, cold snaps, and heat stress, both by produce and province. Our findings reveal the most exposed sourcing regions for different produce and weather extremes, emphasizing how climate risks cross borders. We underscore the need for comprehensive approaches to evaluate climate risks and develop resilience strategies in Canada's fruit and vegetable supply chain to ensure nutritional security.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".