PERENCANAAN PENINGKATAN DAYA DUKUNG PERKERASAN RUNWAY BANDARA INTERNASIONAL I GUSTI NGURAH RAI-BALI
Bibliographic record
Abstract
Number (PCN) sebesar 83 F/C/X/T. Saat ini Bandara Internasional I Gusti Ngurah Rai-Bali, melayani beberapa tipe pesawat udara, salah satunya adalah pesawat Boeing 777-300ER yang memiliki Aircraft Clasification Number (ACN) sebesar 86. Nilai ACN yang lebih besar dari PCN ditengarai sebagai salah satu penyebab terjadinya kerusakan dan menurunnya daya dukung runway. Kajian kondisi perkerasan eksisting dan perencanaan untuk meningkatkan nilai PCN runway diperlukan untuk dapat melayani operasional sejenis pesawat Boeing 777-300ER sampai 10 tahun ke depan. Data-data yang dikumpulkan sebagai parameter dan variabel dalam menentukan kondisi dan merencanakan perkerasan runway yaitu persentase jenis dan persentase kerusakan, tebal lapis perkerasan runway, Nilai California Bearing Ratio (CBR), karakteristik pesawat udara dan annual departure. Hasil analisa menggunakan software FAARFIELD dan COMFAA mendapatkan Pavement Classificasion Index (PCI) runway Bandara Internasional I Gusti Ngurah Rai-Bali adalah 65,83 termasuk kondisi cukup (fair). Kondisi ini mengindikasikan bahwa runway Bandara Internasional I Gusti Ngurah Rai-Bali dalam keadaan kelelahan (fatique). Hasil analisis juga mendapatkan, untuk meningkatkan daya dukung runway sehingga mampu melayani pesawat sejenis Boeing 777-300ER sampai 10 tahun ke depan (sampai Tahun 2030), diperlukan tebal lapis tambahan (overlay) 10,41 cm. Overlay senilai tersebut menjadikan Bandara Internasional I Gusti Ngurah Rai-Bali memiliki runway dengan PCN 121 F/C/X/T.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.032 | 0.013 |
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".