Identifikasi Material Piroklastik Banjir Lahar dingin Hasil Erupsi Gunung Merapi yang Merusakkan Jaringan Pipa Air Bersih dengan Metode USCS di Kali Boyong
Bibliographic record
Abstract
Kali Boyong berada di hulu Gunung Merapi yang menampung hasil erupsi yaitu material piroklastik. Bahaya sekunder dari erupsi adalah aliran lahar dingin. Aliran mengangkut batu, pasir, dan kerikil terendapkan di lereng bercampur air hujan, menjadi banjir apabila intensitas curah hujan yang terjadi cukup tinggi 40 mm/jam. Akibat peningkatan aktivitas sejak 5 November 2020 terjadi kenaikan jumlah material mengakibatkan kerusakan jaringan pipa air bersih. Tujuan penelitian adalah mengetahui karakteristik banjir lahar dingin yang merusakkan jaringan pipa air berdasarkan tipe material piroklastik yang terbawa aliran banjir lahar dingin. Metode yang digunakan kuantitatif, metode USCS dan metode kualitatif. Sampel diambil pada 3 tabung. Lokasi pengambilan disekitar titik kerusakan. Parameter uji analisis ukuran butir tanah menggunakan sampel sebanyak 100 gram. Hasil pengujian tabung 1, SM (Sand Silt) berbutir kasar, gradasi buruk, kategori pasir berlanau. Tabung 2, SW SM (Sand Well-Sand Silt) berbutir kasar, bergradasi buruk, dan campuran pasir berlanau. Tabung 3, GW GM (Gravel Well-Gravel Silt) berbutir kasar, bergradasi baik, kelompok kerikil sangat berpasir. Material pengujian didominasi ukuran butir pasir halus hingga sedang, menghanyutkan kerikil, kerakal, dan batu besar kerusakan jaringan pipa dimungkinkan material berukuran besar terbawa aliran ke sisi dalam dan menabrak alur sisi luar sungai saat melaluinya, lokasi pengambilan tidak sesuai.Kata Kunci: Banjir Lahar dingin; Material Piroklastik; USCS
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".