Rekomendasi Rencana Anggaran Biaya dari Audit Keselamatan Jalan Tahap Detail Engineering Design (DED) pada Jalan Nasional Provinsi Jambi
Bibliographic record
Abstract
From 2016 – 2018 in Jambi province there were 3,543 traffic accidents, with 1,085 deaths. What causes traffic accidents is caused by three factors, human factors themselves, vehicle factors and road infrastructure factors. To reduce the occurrence of traffic accidents, you can eliminate or reduce the causes of traffic accidents, such as improving the safe condition of road infrastructure. The aim of this research is to calculate recommendations for budget plans from the results of road safety audits at the detailed engineering design (DED) stage, totaling six detailed engineering design (DED) documents on Jambi province national roads. This research uses road safety audit guidelines Pd 03 – 2019 – B. From the results of the analysis, the percentage between the safety cost recommendation results compared to the construction cost design (owner estimate) at DED 1 = 6.17%, DED 2 = 0.93%, DED 3 = 16.13%, DED 4 = 7.33%, DED 5 = 0.75% and DED 6 = 8.83% and the combined percentage obtained is 7.05%. Thus, a detailed engineering design (DED) still requires attention in producing a detailed engineering design (DED) that is oriented towards road safety and the costs of physical implementation of the results of road safety recommendations.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".