PERBANDINGAN ANALISIS DEBIT BANJIR MENGGUNAKAN HIDROGRAF SATUAN SINTETIS (HSS) SNYDER DAN NAKAYASU PADA SUNGAI KRUENG TRIPA
Bibliographic record
Abstract
Sungai Krueng Tripa merupakan salah satu sungai yang melewati 2 lintasan Kab yakni Kab Gayo Lues di hulu sungai dan Kab Nagan Raya di hilir sungai. Luas DAS Krueng Tripa dengan bagian hilir di Desa Ujong Krueng sebesar 2.953,458 km2. Banjir sering terjadi di Desa Ujong Krueng akibat luapan dari Sungai Krueng Tripa dengan ketinggian mencapai 30-150 cm dengan periodik 4-6 kali dalam setahun. Tujuan studi ini yakni guna menganalisis besarnya debit banjir pada Sungai Krueng Tripa yang dilakukan dengan menghimpun data curah hujan serta peta topografi. Berlandaskan analisis hujan rencana periode ulang 2, 5, 10, 25, 50,dan 100 tahun menggunakan HSS Snyder yakni 3265,437 m3/dtk; 4438,160 m3/dtk; 5239,825 m3/dtk; 6280,393 m3/dtk; 7074,094 m3/dtk; 7887,613 m3/dtk. Sedangkan analisa debit banjir rencana memakai HSS Nakayasu periode ulang 2, 5, 10, 25, 50, dan 100 tahun adalah 3543,434 m3/dtk; 4870,081 m3/dtk; 5618,920 m3/dtk; 6558,960 m3/dtk; 7714,292 m3/dtk; 8458,272 m3/dtk. Pada penelitian ini HSS Nakayasu memperoleh debit banjir lebih besar dibandingkan dengan HSS Snyder.
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.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".