Development and Assessment of Localized Seasonal Rainfall Prediction Models: Mapping and Characterizing Rift Valley Fever Hotspot Areas in the Southern and Southeastern Ethiopia
Bibliographic record
Abstract
Rift Valley Fever disease has been recognized as being among permanent threats for the sustainability of livestock production in Ethiopia, owing to shared boarders with RVF endemic countries in East Africa. Above-normal and widespread rainfall have outweighed as immediate risk factor that facilitated historical outbreaks of the disease in the East Africa. The objective of the present study, thus, was to develop prospective localized seasonal rainfall anomaly prediction models, and assess their skills as early indicators to map high risk localized rift valley fever disease outbreak areas (hotspots) over the southern and southeastern part of Ethiopia. 21 years of daily rainfall data; for five meteorological stations, was employed in diagnosing existences of any anomalous patterns of rainfall, along with a cumulative rainfall analysis to determine if there were ideal conditions for potential flooding. The results indicated that rainfall in the region is highly variable; with non-significant trends, and attributed to be the results of the effects of large-scale climatic-teleconnection. The moderate to strong positive correlations found between the regional average rainfall and large scale teleconnection variables (r ≥ 0.48), indicated some potentials for early prediction of seasonal patterns of rainfall. Accordingly, models developed, based on the regional average rainfall and emerging developments of El Niño/Southern Oscillation and other regional climate forcings, showed maximum skills (ROC scores ≥ 0.7) and moderate reliability. Deterministically, most of the positive rainfall anomaly patterns, corresponding to El Niño years, were portrayed with some skills. The study demonstrated that localized climate prediction models are invaluable as early indicators to skillfully map climatically potential RVF hotspot areas.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".