FAKTOR-FAKTOR PENDORONG PENYEBAB TERJADINYA KEMACETAN STUDI KASUS : KAWASAN SUKUN BANYUMANIK KOTA SEMARANG
Bibliographic record
Abstract
Sukun region located in the southern city of Semarang is an activity node meetings between Semarang Upper and Lower part. In addition to the node activity, regional transport node Sukun is also because of the intersection between Setia Budi roads and highways. The rapid growth of traffic is felt at Setia Budi roads, this is because of the way as the initial point of entry into the city of Semarang from the south ( Yogyakarta - Solo ) both vehicles are going to Semarang and the entrance to the highway with a wide range of purposes. And trading activity and the presence of onsite services that are in the area resulted in increased activities of road users, the incidence of traffic generation and the high side barriers, which at certain hours of congestion and delays often occur. The research methodology used in this research is by using Deductive Quantitative Methods Rationalistic. With the technique of factor analysis and analysis of transportation, so it can be determined Level Of Service and factors - factors driving the cause of congestion in the area Sukun Banyumanik. The final results obtained from the analysis of the factors driving the causes of congestion on area Sukun is that congestion is due to the on site activity, the high capacity of the road, next to the barrier height and geometric conditions of the road.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.012 |
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".