Drought indices and their application to East Africa
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This study analysed and modified (where necessary) the properties of three drought indices: the Palmer drought severity index (PDSI), the Bhalme–Mooley index (BMI) and the standardized precipitation index (SPI). We modified the original PDSI's recursive formula, potential runoff, and Z index, which produced more realistic results than the original PDSI (designed for the USA) for East Africa. We improved the SPI by first using a plotting position formula designed for the Pearson type III (P3) distribution to transform the ‘smoothed’ precipitation data into non‐exceedance probabilities, which we then transformed into standard P3 variates by the regional flood index method. The modified SPI depicted East Africa's drought conditions more accurately than the original SPI. Using the three indices and East Africa as a case example, we identified eight assessment criteria to determine the most appropriate index for detecting drought events on a regional basis. BMI produced results that are highly correlated to those of the modified PDSI, which suggested that precipitation alone could explain most of the variability of East African droughts. Furthermore, among the three indices, SPI is more appropriate for monitoring East African droughts because it is more easily adapted to the local climate, has modest data requirements, can be computed at almost any time scale, provides relatively consistent power spectra spatially, has no theoretical upper or lower bounds, and is easy to interpret. Copyright © 2003 Royal Meteorological Society
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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 | 0.000 | 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 | 0.000 | 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 it