Assessing the Land Use Distribution of the Mass Rapid Transit Pedestrian Catchment Area (PCA) and its Geographical Context in the Kuala Lumpur Conurbation, Malaysia
Bibliographic record
Abstract
Walking is the most common means of accessing transit globally. An encouraging factor for walking is related to the land use distribution of the station's pedestrian catchment area (PCA). There were two objectives conducted in the study: i) to assess the land use distribution of the PCA using Geographic Information System (GIS), and ii) to understand the station's geographical context based on the distribution of land use in the Kuala Lumpur conurbation. A 400m-station buffer PCA was designated in accordance to the policy guideline in Greater Klang Valley (GKL) Land Public Transport Master Plan. The distribution and concentration of specific land use category were geographically varied. There was a different land use distribution pattern observed in Kuala Lumpur and Petaling Jaya. In Kuala Lumpur, the PCA in inner city areas mainly comprised commercial use while residential use was found more in the outer city areas. In Petaling Jaya, land use distribution was mainly commercial. The concentration of commercial land use reduces as one moves to outer suburbs and subsequently is replaced by residential land use. In conclusion, land use distributions can be distinguished based on their geographical location as Kuala Lumpur conurbation seems to uphold a concentric land use planning structure, while Petaling Jaya adapts to the multiple nuclei land use planning model.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".