STUDI LOKASI RAWAN KECELAKAAN LALU LINTAS DI JALANRAYA BOGOR SEKSI KEDUNG HALANG – PABUARAN
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
Tujuan penelitian ini adalah mengidentifikasi lokasi rawan kecelakaan lalu lintas sepanjang 13 km yakni dari Kedung Halang – Pabuaran . Dilanjutkan menganalisis data korban Meninggal Dunia (MD), Luka Berat (LB), Luka Ringan (LR), Kendaraan terlibat (K) yang terdata lokasi rawan kecelakaan di Jalan Raya Bogor seksi Kedung Halang – Pabuaran. Setelah itu, dianalisis dengan metode angka kecelakaan (AEK) dan batas kendali atas (BKA). Hasilnya terdapat 4 segmen rawan kecelakaan pada STA 7, STA 8, STA 9, dan STA 12. Penerapan marka kejut, penutupan arus putar balik (U-TURN), melengkapi rambu lalu lintas, penambahan lampu penerangan jalan umum (LPJU), penerapan zona selamat sekolah (ZoSS).
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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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