Sistem Informasi Geografis Untuk Visualisasi Daerah Rawan Kecelakaan Lalu Lintas Jalan Arteri Primer Kota Surabaya
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
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".