Delineating catchment areas of selected KTM komuter stations in the kuala lumpur conurbation using a gis-based approach
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Bibliographic record
Abstract
Park-and-ride schemes are an important component of the public transportation systems of many cities. An analysis delineating the catchment areas of rail-based park-and-ride stations is thus important in providing a better understanding of these schemes. Geographic information systems (GIS) technology is applied in order to delineate the catchment areas and calculate the access distances of the respective stations. The methodology includes carrying out a questionnaire interview at the park-and-ride sites via random sampling. With information on the origins of park-and-ride users and using MapInfo and ArcView GIS 3.2, the catchment areas of the respective stations were then delineated. The paper provides a detailed description of the methodology and the output in a GIS environment for the Kuala Lumpur conurbation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it