Smart growth strategies, transportation and urban sprawl: simulated futures for Hamilton, Ontario
Bibliographic record
Abstract
North American cities have undergone dramatic changes over the last century. Locations that were once inconvenient have become accessible through extensive road networks leading to population decentralization from the traditional urban centre to suburbia, creating polycentric sprawls from once monocentric communities. Hamilton, Ontario is one such city. The decentralization and urban decline of the city is widely attributed to sprawling development. This change in the sociospatial structure creates challenges for transportation planners as we see greater automobile dependency, greater commuting distances and increased congestion. Smart growth policies such as urban residential intensification (URI) aim to increase population densities in the urban core. This exploratory study estimates the benefits of such policies from a transportation aspect. It is predicted that the City of Hamilton will experience household growth of approximately 80,000 households over the period 2005–2031. Using IMULATE, an integrated urban transportation and land‐use model, a variety of development scenarios model this anticipated growth. Changes in vehicular emissions, traffic congestion and energy consumption as a result of URI are examined. Models of the land‐use/transportation relationship demonstrate how increasing population densities within a city's urban centre drastically reduce congestion, emissions and gasoline consumption .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".