Evaluasi Kapasitas Spillway Bendungan Darma sebagai Salah Satu Dasar dari Aspek Keamanan Bendungan (Hal. 31-38)
Bibliographic record
Abstract
ABSTRAK Kapasitas spillway Bendungan Darma perlu tinjauan kembali mengenai kemampuannya dalam menjaga agar tidak terjadi limpasan karena pada umumnya bendungan yang dibangun dibawah tahun 1980 tidak didesain untuk debit banjir ( ) Probable Maximum Flood (PMF). Dari analisis hidrologi dengan metode Gama 1 untuk penelusuran banjir reservoir didapatkan pada Q1000 yaitu Qinflow = 879,02 m3/s, Qoutflow = 574,44 m3/s dan adalah Qinflow = 1546,63 m3/s, Qoutflow = 620,74 m3/s. Berdasarkan hasil perhitungannya didapatkan selisih muka air melimpah dan mercu bendungan pada Q1000 adalah 1,83 m dan pada QPMF adalah 0,54 m. Saat terjadi banjir PMF, bendungan mengalami overtopping karena menurut SNI batas maksimal banjir yang boleh terjadi adalah 0,75 di bawah puncak mercu bendungan.Kata kunci: debit banjir, bendungan, limpasan, Probable Maximum Flood (PMF). ABSTRACTThe capacity of the spillway Dam Darma requires review regarding his ability to keep the spillway did not overflow because in general the dam built under the year 1980 is not in design for flood discharge ( ) the Probable Maximum Flood (PMF). Hydrological analysis method of Gama 1 for search flood reservoir obtained on the Q1000 are Qinflow = 879.02 m3/s, Qoutflow = 574.44 m3/s and are Qinflow = 1546.63 m3/s, Qoutflow = 620.74 m3/s. Based on the results of the calculations are obtained by difference advance water overflow and mercu dam on the 1000 is 1.83 m and on QPMF is 0.54 m. At the time of the flood, the dam suffered a PMF overtopping due to flood, according to the maximum limit of the SNI standartmay happen is 0.75 mercu beneath the dam.Keywords: flood discharge, dam, spillway, Probable Maximum Flood (PMF).
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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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 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".