IDENTIFYING URBAN DESIGN PRINCIPLES AND ITS ATTRIBUTES FOR WATERFRONT TRANSIT-ORIENTED DEVELOPMENT (WTOD)
Bibliographic record
Abstract
The booming development of transit-oriented development (TOD) worldwide, especially in high-density urban areas, has shown that many cities have started to transform their urban spaces into more sustainable and liveable cities. TOD integrates the mixed-use development around the public transport stations and promotes a pedestrian-and bicycle-friendly environment as a city lifestyle. However, such developments often lack integration with the contextual condition of the surrounding area, which in the case examined in this study is the waterfront. Many cities in developed countries such as Singapore, U.S. and Canada have started to reintegrate their TODs with the water bodies. Recent trends show that TODs are widely established in areas near the water body such as dockland and waterfront, as part of urban regeneration. Various urban design principles of waterfront transit-oriented development are available, but most of them are scattered across many documents. Thus, this research aimed to identify the urban design principles for waterfront transit-oriented development. The principles were developed using a qualitative method that included content analysis ofjournals, reports, and guidelines on transit-oriented development (TOD), waterfront development, and waterfront transit-oriented development (WTOD). In the end, the urban design principles of WTOD can guide professional stakeholders such as architects, planners, engineers, and others relevant to the field to reintegrate the TOD with the waterfront in future planning.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".