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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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".