USULAN PEMANFAATAN TEKNOLOGI MODIFIKASI CUACA DENGAN GROUND-BASED GENERATOR UNTUK MENAMBAH DEBIT ALIRAN SUNGAI MAMASA, SULAWESI
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
IntisariTelah didesain sebuah usulan pemanfaatan teknologi modifikasi cuaca (TMC) dengan ground-based generator (GBG) untuk menambah debit aliran sungai Mamasa di Sulawesi guna meningkatkan produksi listrik dari Pembangkit Listrik Tenaga Air (PLTA) Bakaru. GBG adalah metode alternatif operasi penyemaian awan dari darat menggunakan menara. Penelitian tentang GBG telah selesai dilakukan di kawasan Puncak Bogor yang merupakan bagian dari sistem orografik Gunung Gede-Pangrango. Daerah Aliran Sungai (DAS) Mamasa memiliki kemiringan lereng antara 25%-40%. Topografi dengan kelerengan curam berada di bagian tengah, sebagian kecil di bagian hulu serta di bagian hilir DAS. Faktor orografi sangat dominan dalam pembentukan awan di DAS Mamasa. Uap air yang masuk ke DAS dipaksa naik oleh pebukitan di batas DAS sehingga terjadi pembentukan awan. Bagian tengah DAS sisi sebelah barat (Sikuku dan Sumarorong) memiliki curah hujan paling banyak sedangkan bagian tengah sisi sebelah timur (Rippung, Tabone, Tatoa dan Salembongan) memiliki curah hujan paling rendah. Hasil kajian topografi merekomendasikan wilayah Sikuku, Makuang dan Sumarorong sebagai lokasi menara GBG. Sementara itu, Polewali direkomendasikan untuk lokasi radar. Karena DAS Mamasa adalah daerah yang rawan longsor maka pembangunan menara GBG disarankan dilakukan pada bulan bulan tidak banyak hujan yaitu pada bulan Juni sampai dengan Agustus.AbstractA proposed use of weather modification technology (TMC) with ground-based generator (GBG) to increase Mamasa river flow in Sulawesi to increase electricity production from Bakaru hydropower was designed. GBG is an alternative method of cloud seeding operations from the ground using towers. Research on GBG has been completed in the area of Puncak, Bogor, which is part of the orographic system Gunung Gede-Pangrango. Mamasa Watershed has a slope of between 25% -40%. Topography with steep slopes are in the middle, a small portion in the upstream and in the downstream of watershed. Orography is very dominant factor in the formation of clouds in the Mamasa watershed. Water vapor that enters the watershed is forced up by the hills in the watershed resulting in the formation of clouds. The middle part of west side (Sikuku and Sumarorong) have the most rainfall, while the central part of the eastern side (Rippung, Tabone, Tatoa and Salembongan) has the lowest rainfall. Results of the assessment of topography recommend the area of Sikuku, Makuang and Sumarorong as GBG tower locations. Meanwhile, Polewali recommended for radar location. Because Mamasa watershed is an area that is prone to landslides, the construction of the GBG tower suggested carried out during June to August.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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 it