POTENSI ATMOSFER DALAM PEMBENTUKAN AWAN KONVEKTIF PADA PELAKSANAAN TEKNOLOGI MODIFIKASI CUACA DI DAS KOTOPANJANG DAN DAS SINGKARAK 2010
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
Kajian potensi atmosfer terhadap proses pembentukan dan pertumbuhan awan konvektifpada saat pelaksanaan Teknologi Modifikasi Cuaca (TMC) telah dilakukan dengan datapengamatan sounding dari stasiun Tabing Sumatera Barat. Sebanyak 330 buah datapengamatan harian jam 00Z dan 12Z dari Juni sampai dengan Nopember 2010 telahdianalisis. Dengan aplikasi RAOB analisis dilakukan untuk menentukan parameterdan indeks radiosonde sebagai penduga potensi atmosfer di wilayah tersebut. Hasilanalisis kandungan uap air yang diwakili oleh nilai Mixing Ratio dan PW menunjukkanbahwa selama bulan-bulan tersebut kandungan uap air cukup besar, presipitasi yangdihasilkan dipengaruhi oleh labilitas atmosfer yang diindikasikan oleh beberapa indeksradiosonde. Apabila labilitas pada hari itu cukup baik, maka peluang presipitasinyaakan semakin besar.Study the potential of the atmosphere on the formation and growth of convective cloudsduring the implementation of Weather Modification Technology (TMC) has been donewith observational data came from Padang, West Sumatra station. A further 330 piecesof observation data at 00Z and 12Z each day from June to November 2010 has beenanalyzed. By the RAOB application analysis conducted to determine the parameters and indices as sounding estimators of potential atmospheric in the region. Moisture content analysis results that represented by the value of Mixing Ratio (MR) and Precipitable Water (PW) showed that during the months of water vapor content is quite large, the rainfall was influenced by atmospheric unstability could indicated by several indexes. If unstability on the day was good enough, then the precipitation will be even greater.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it