Planning and Growth Management Committee Chief Planner and Executive Director, City Planning Division
Bibliographic record
Abstract
This report summarizes the findings of two consultant studies that were commissioned as part of the Zoning By-law Project to develop new parking standards for selected land uses. The study conducted by the IBI Group looks at parking standards for office, restaurant and retail uses. The other study, undertaken by Cansult Limited, addresses the parking needs of condominium and rental apartments as well as townhouses with common parking areas. The IBI study draws on the results of parking utilization surveys of some 800 commercial parking lots and the Cansult study utilizes the survey returns from approximately 5,000 households living in apartments across the City. Both studies take into account the policy directions of the Official Plan and have regard for existing parking standards in Toronto and other comparable cities. The proposed parking standards vary among different parts of the City as defined by the Official Plan’s urban structure map. There are separate parking standards for each of the mixed use, transit-oriented, targeted growth areas (Downtown and Central Waterfront, Centres, and Avenues) and the rest of the City. A common aim of the consultant studies is to identify parking standards that require the minimum responsible amount of parking for a given land use. Maximum standards are also proposed in the targeted growth areas to guard against an oversupply
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.121 | 0.042 |
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".