Mapping the growing capacity of climate smart food in urban environments
Bibliographic record
Abstract
The practice of urban agriculture (UA) is a unique food system model that localizes the production of sustainable, geographically appropriate food. The environmental benefits inherent in UA aligns with the emerging field of climate smart agriculture (CSA). However, the agro-industry focus of CSA is beyond the scope of most UA initiatives. Instead, we put forward the term “climate smart food” as a more appropriate framework to examine the environmental impact of food production in an urban context. The purpose of this study, rooted in the recognition of underutilized private urban land resources for UA, is to assess the potential of urban land to grow climate smart food. The Bowness neighbourhood in Calgary, Alberta is used as a case study. A geospatial process of constraint mapping was applied to analyze suitable private land space that could be converted from lawns to cultivated gardens. Using data from a local food cooperative as a benchmark for local urban production capacity, it was determined that six urban farms in Calgary produced roughly 8,200 pounds of food from private gardens in 2016. In the Bowness neighbourhood, 42 percent of the land was held as private turf grass, and produced only about 800 pounds of food. This type of analysis serves to quantify the magnitude of underutilized land within an urban boundary that could produce significant amounts of climate smart food.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".