SMARTer growth neighborhood design manual : application to existing neighborhoods
Bibliographic record
Abstract
Urbanization is putting immense pressure on global infrastructure. Uncontrolled rapid urbanization and motorization of cities are one of the main causes of Urban Sprawl. This sprawl alters the structure and pattern of cities, making it socially, economically, and environmentally unsustainable. Urban Sprawl is associated with traffic fatalities, physical inactivity, obesity, and increased GHG emissions. Urban planners and engineers are researching new methods to eliminate the negative impacts of urban sprawl. SMARTer Growth (SG) Neighborhood design or previously know as Fused Grid (FG) Neighborhood Design has been identified as a reliable planning technique that can effectively fight urban sprawl while making neighborhoods more sustainable and liveable. Macro-level collision prediction models were developed in this study to evaluate the traffic safety condition of the neighborhoods under study. SG was introduced to evaluate two existing neighborhoods, and macro-level collision prediction models were applied to assess the traffic safety of the existing neighborhoods and the retrofitted designs. The comparison between the existing neighborhoods and the retrofitted designs showed a 62% reduction in the total number of collisions for the retrofit design of Capri-Landmark, Kelowna, BC, Canada, and 56% reduction in the total number of collisions for the retrofit design for Gulshan, Dhaka, Bangladesh. Additionally, the suite of tools toward SMARTer Growth (SG) Neighborhood Design Manual was applied to evaluate these retrofitted designs. Based on the evaluation, the health outcomes and quality of life of the residents in the proposed retrofit designs were discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".