Prioritas Penanganan Lokasi Rawan Kecelakaan (LRK) di Provinsi Sumatera Utara
Bibliographic record
Abstract
Provinsi Sumatera Utara adalah provinsi ke lima dengan jumlah kecelakaan tertinggi setelahJawa Timur, Jawa Tengah, Jawa Barat, dan Sulawesi Selatan dengan jumlah korban meninggaldunia 1649 jiwa, korban luka berat 1759 jiwa, korban luka ringan 5897 jiwa, dan jumlahkerugian sebesar Rp.12.157.821.000,-. Begitu banyak lokasi kecelakaan yang terjadi berdasarkandata Polda Sumatera Utara. Oleh karena itu perlu dilakukan pemrioritasan penanganan lokasirawan kecelakan (LRK) di Provinsi Sumatera Utara. Jumlah kecelakaan dari 5335 kejadiankecelakaan kemudian dipilih menjadi 2587 kejadian berada di ruas Jalan Nasional, penyaringankejadian memenuhi kriteria ? 2 kejadian tiap lokasi menjadi 438 LRK, kemudian dilakukananalisis dengan metode angka ekivalen kecelakaan (AEK), tingkat kecelakaan (Tk), dan UpperControl Limit (UCL) sehingga diperoleh 52 LRK. Dengan penggabungan 24 lokasi tipikal danlokasi yang berdekatan maka dihasilkan 40 LRK. Selanjutnya 40 LRK tersebut disurvei rinci dandisusun Rencana Teknik Akhir yang lengkap termasuk Rencana Anggaran Biayanya. Padaakhirnya prioritas penanganan disesuaikan dengan dana yang tersedia.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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".