Sistem Informasi Geografis Untuk Visualisasi Daerah Rawan Kecelakaan Lalu Lintas Jalan Arteri Primer Kota Surabaya
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accidents are an event that often occurs on the highway, especially in big cities, one of which is Surabaya City. Accidents are one of the main problems for the safety of road users. Almost all activities carried out require transportation facilities, if the transportation facilities do not run well due to traffic accidents, the activities carried out will not run well. Therefore, a solution is needed to reduce accidents by building an accident-prone area information system. In determining the criteria for accident-prone areas, it is taken from the Republic of Indonesia Police, the Department of Transportation, and Public Works, based on the number of accidents, the number of fatalities of victims, and road conditions. This research is an effort to visualize the occurrence of accidents using spatial data of Surabaya City road network maps and non-spatial data, namely accident data and road data obtained from the police and the Bina Marga Service. In the final result of this research, an application is obtained that can provide visualization of accident-prone areas such as Ahmad Yani road which is a road with a high accident rate with the number of incidents and has the highest victim fatality weighting results among 9 other primary arterial roads. Keywords:Accident Prone Areas, Geographic Information System, Surabaya City, Traffic, Transportation
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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