POTENSI ATMOSFER DALAM PEMBENTUKAN AWAN KONVEKTIF PADA PELAKSANAAN TEKNOLOGI MODIFIKASI CUACA DI DAS KOTOPANJANG DAN DAS SINGKARAK 2010
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.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".