Could Toronto provide 10% of its fresh vegetable requirements from within its own boundaries? Matching consumption requirements with growing spaces
Bibliographic record
Abstract
Is it feasible for Toronto to produce and market 10% of its fresh vegetable requirements from within its own boundary, without competing with existing Ontario vegetable producers? We used zoning maps, aerial photography, and numerous exclusionary and inclusionary criteria to identify potential food production sites across the city and, after identifying organic vegetable production yields, to calibrate supply potentials against current vegetable consumption estimates for the Toronto population. It was determined that Toronto required 2,317 hectares (5,725 acres) of food production area to meet current demand, if all production were organic to fulfill other municipal environmental objectives. Of this, 1073.5 ha (2,653 acres) of land could be available from existing Census farms producing vegetables, lands currently zoned for food production, certain areas zoned for industrial uses, and over 200 small plots (0.4–2 ha or 1–4.9 acres) dotted throughout the northeast and northwest of the city. In addition, 1243.5 ha (3,072.8 acres) of rooftop space would also be required. The land and rooftop space available suggests, however, that there would be difficulties meeting requirements for land-extensive crops such as sweet corn, squash, potatoes, cabbage, carrots, and asparagus.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".