The spatial distribution of a comprehensive drought risk index in Java, Indonesia
Bibliographic record
Abstract
Drought is a common hydro-meteorological disaster in several remote regions of Java, Indonesia, due to the limitation of supporting facilities in water supply management. However, little attention has been paid to drought preparedness, especially within drought risk assessment. Since Java is the most densely populated area in Indonesia and has been identified as a national food center, an effective drought risk assessment is needed for drought disaster mitigation. Therefore, a spatial drought risk assessment for the sub-district level was conducted for Java using a framework that considers the combined roles of drought hazard and drought vulnerability. Observed rainfall data from 1294 stations across Java for the time period 1991–2020 were used to define a drought hazard index. Furthermore, a drought vulnerability index was obtained by classifying several indicators that accommodated non-climate aspects. Based on these, an overall drought risk index was obtained by integrating the drought hazard index and drought vulnerability index. The results identified several areas of Java in need of priority government attention for drought disaster mitigation. Of these, the main priorities are regions with a high risk of drought, which were spread across five provinces as follows (percentage represents the number of high drought hazard index values in the sub-district to the total number of sub-districts): Yogyakarta (13.92%), West Java (7.26%), Banten (5.77%), Central Java (6.23%), and East Java (4.32%).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".