The Impact of Climate Change on the Food System in Toronto
Bibliographic record
Abstract
As part of its Climate Change and Health Strategy, in 2017, Toronto Public Health engaged stakeholders from across the food system to complete a high-level vulnerability assessment of the impact of climate change on the food system in Toronto. Using the Ontario Climate Change and Health Vulnerability and Adaptation Assessment Guidelines, the City of Toronto's High-Level Risk Assessment Tool, and a strategic framework developed by the Initiative for a Competitive Inner City, Toronto Public Health identified the most significant extreme weather event risks to food processing, distribution and access in Toronto. Risks associated with three extreme weather events that are the most likely to occur in Toronto due to climate change were analyzed: significant rain and flooding, an extended heat wave, and a major winter ice storm. The analysis finds that while extreme weather events could potentially disrupt Toronto's food supply, the current risk of an extended, widespread food supply disruption is relatively low. However, the findings highlight that a concerted effort across the food system, including electrical and fuel providers, is needed to address other key vulnerabilities that could impact food access, especially for vulnerable populations. Interruptions to electricity will have food access and food safety impacts, while interruptions to the transportation network and fuel will have food distribution and access impacts. Actions to mitigate these risks could include addressing food access vulnerabilities through ongoing city-wide strategies and integrating food access into the City's emergency response planning. The next steps will include engaging with multiple partners across the city to understand and strengthen the "last mile" of food distribution and develop community food resilience action plans for vulnerable neighbourhoods.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 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".