T.U.R.F. (Transformative Urban Rooftop Farming): Alleviating Food Insecurity in Toronto
Bibliographic record
Abstract
One in every eight Canadian households is food insecure. This accounts for 12.7% of the total population of Canada. Food insecurity, which refers to inadequate and insecure access to food due to social, physical, and economic constraints, has a severe effect on an individual’s health and well-being. The city of Toronto has many neighborhoods that face food insecurity within their communities. The city also has an abundance of vacant rooftop space that does not compete with other urban uses. How can urban agriculture on these vacant rooftops help in solving the problems of food insecurity in these vulnerable neighborhoods? Current urban agriculture practices in Toronto are limited to seasonal community farms aimed to feed a handful of the population and focus on enriching the community. However, research dictates that rooftops can be used for food production using the principles and technologies of building integrated agriculture (BIA). But little research is available to discuss how urban agriculture on a building can aid the food insecure population of the city. BIA on underutilized rooftops across the food insecure neighborhoods in the city of Toronto can act as an agent to alleviate the challenge of food insecurity. This research involves analyzing existing buildings in dense urban environments that have incorporated BIAs and understanding the different farming systems used by these buildings. Neighborhoods in Toronto that suffer from food insecurity are treated as test sites for implementing the researched BIA systems. The BIA proposal also aims to track the changes in the day-to-day life of the building residents. Integration of BIA within the city is beneficial for the people, the urban environment, and climate change in general. This local production of food will not only contribute towards alleviating food insecurity but also bring people closer to food production and reduce the impacts of food production on the climate by reducing food miles.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".