TINJAUAN LITERATUR : IMPLEMENTASI KONSEP TRANSPORTASI BERKELANJUTAN TERHADAP PERENCANAAN KAWASAN PERMUKIMAN PERKOTAAN TERPADU SOREANG
Bibliographic record
Abstract
Research on sustainable transportation has been growing in recent years to support the Sustainable Development Goals (SDGs). Urban dynamics pose a challenge in achieving these goals, as they are influenced by various factors such as population growth, urbanization, and urban expansion, which occur globally. This trend is also evident in urban areas across Indonesia, including Bandung Regency. Research on sustainable transportation has been growing in recent years as part of efforts to support the Sustainable Development Goals (SDGs). Urban dynamics pose a challenge in achieving these goals, as they are influenced by various factors such as population growth, urbanization, and urban expansion, which occur globally. This trend is also evident in urban areas across Indonesia, including Bandung Regency. Bandung Regency plays a strategic role as a regional buffer zone, providing space for industrial activities, housing, and supporting infrastructure to sustain the capital city of West Java Province. To accommodate this growth, the Bandung Regency Government has developed a Detailed Spatial Plan (RDTR), which includes the development of the Soreang Integrated Urban Area. The increasing intensity of urban activities in Bandung Regency necessitates sustainable spatial management and development policies, particularly in transportation infrastructure provision.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.015 |
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".