Perencanaan Peningkatan Emplasemen Stasiun Sukabumi Untuk Mendukung Operasional Jalur Ganda Bogor-Sukabumi
Bibliographic record
Abstract
Sukabumi City is an area in West Java that has experienced rapid development compared to other regions. Mass transportation facilities are needed to support the movement of people from one place to another. In 2019 to 2021 the Directorate General of Railways will construct a double track railway. The construction of the double track railway is expected to reduce the density of road traffic, especially Jakarta-Bogor-Sukabumi. Along with the construction of the double track railroad, the supporting facilities for the operation of the double track railroad must also be improved. The supporting facilities for the operation of the railway line are the Station and the Operations Facility and the signaling system. This research focuses on planning the improvement of the Sukabumi Station emplacement. The steps to improve Sukabumi station emplacement are problem identification, station emplacement planning, calculating effective path length, platform planning, operating facility planning and signal placement. Based on the results of the analysis of the track requirements for planning the improvement of the Sukabumi station emplacement are 4 lines with a minimum effective path length of 160 meters. Sukabumi platform planning uses side platform and island platform platform width of at least 4 meters and it is proposed to move from platform to platform using JPO assuming the width of the stairs is 2.75, the platform width needed is 6 meters. The Signaling System planned is the Closed Block Electrical Signaling System. The pattern of operations at Sukabumi Station is adjusted to the needs of operations at Sukabumi Station.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".