Akurasi Data Curah Hujan Satelit Terhadap Data Pengukuran di Daerah Tangkapan Air (DTA) Waduk Sutami
Bibliographic record
Abstract
Sutami Reservoir that located in the Brantas River Basin is a multi-purpose reservoir, it’s used to provide of raw water, irrigation, flood control, and power plants, fish farm, and tourism. Rainfall data information is very important in hydrological analysis as the basis for determining operating patterns, water balances, and calculating sediment rates. Rainfall data that is recorded in a row can show us trends or the nature of rain, but in reality it is very difficult to obtain representative rainfall observation data, both in terms of quality and length of observation data, which is quite in accordance with what is required in several locations, it is very difficult due to the absence of rain stations or broken gauges. Therefore, by taking advantage of technological advances, it is necessary to analyze the accuracy of rainfall data via satellite (GPM V6 and TRMM 3B43 V7) as an alternative to using rainfall data to fill data shortages at certain locations. The results of the analysis of the two satellite rainfall data (GPM V6 and TRMM 3B43 V7) are based on the Nash Sutcliffe Efficiency (NSE) parameters, Root Mean Square Errror (RMSE), Real Error (KR), Correlation Coefficient (R) can be used as an alternative to rainfall data, with satellite rainfall data GPM V6 has better accuracy and performance with average value of NSE 0,8, RMSE 66,46, KR 21,63%, R 0,92.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.046 | 0.020 |
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".