The Business Case for Rooftop Urban Agriculture
Bibliographic record
Abstract
Our world is in crisis; with the impending doom of global warming, coupled with climbing global populations, and the growing demands of the world’s increasingly urban population, the need to reimagine our current food systems is evident. Rooftop urban agriculture offers a solution to this problem, making good use of the idle rooftop space that often goes unused in cities across the globe. However, the technology’s adoption seems to be stunted. In Toronto there are no solely commercial rooftop urban agriculture operations; this is surprising seeing as Toronto was actually the first city in North America to adopt the Green Roof Bylaw, which requires the construction of green roofs on all new developments over a certain size. While there has been extensive research done to investigate the environmental and social impacts of rooftop urban agriculture, the industry remains hindered. It had become clear that the technology’s ability to serve as a sustainable business opportunity was unsubstantiated, ultimately impeding its implementation on a wide scale. This research uses various business design tools to theorize, test, and illustrate the potential of open-air rooftop urban agriculture to thrive as a sustainable business.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".