Urban growth management: Evaluating the European city model for residential intensification for the Guelph context
Bibliographic record
Abstract
The City of Guelph is located in the Greater Golden Horseshoe Area, which is currently the fastest growing urban centre in Ontario, Canada. The Provincial government has developed a 30-year growth management plan, the Places to Grow Plan (PGP), which is intended to improve upon past planning strategies. The Plan's 40% intensification target for new development has been set and Guelph must meet this target by 2015 and maintain it thereafter until 2031. To address the issue of residential intensification for Guelph, a pattern language of European residential intensification is identified and applied to a study site within Guelph, and is evaluated on the extent to which the site corresponds to this pattern language, which consist of various targets and policies within both the PGP and Guelph's Official Plan. Results of this test may offer insight into a feasible and potentially more sustainable option for residential intensification in Guelph.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".