FACTOR FOR CORRECTING THE RAINFALL OF CHIRPS SATELLITE DATA AGAINST OBSERVATION DATA ON THE CILIWUNG WATERSHED(CASE STUDY OF KEMAYORAN METEOROLOGI STATION)
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Bibliographic record
Abstract
The hydrological and environmental cycles in a river area strongly affect rainfall intensity and seasonal patterns. To accurately assess water resource capacity, precise rainfall data from each observation station is crucial. However, unevenly distributed rain gauges often challenge researchers, as insufficient data can hinder their analysis. In these situations, satellite images can provide valuable additional information.Aims: The objective of this study was to analyze the accuracy of CHIRPS satellite rainfall data from observation stations in the Ciliwung watershed, especially in the DKI Jakarta Province area, over the last 30 years (1993–2022).Methodology and results: Statistical analysis such as multiple linear regression with the stepwise method is used to analyze CHIRPS rainfall against observed rainfall data according to the location of the rain station. The validation results in this study show that the average results of the two observation stations have a value of R2 = 0.91 and NSE = 0.9068.Conclusion, significance and impact study: CHIRPS data can be categorized as very good if used as an alternative to limited observational rainfall data, which can then be used in analyzing water availability in the Ciliwung watershed (Jakarta).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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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