African rainfall variability: science and society
Bibliographic record
Abstract
The Sahel-Soudano zone that spans North Africa, from Senegal to Ethiopia, has experienced pronounced climatic variability (and conflicts) for millennia. This home to 250 million people—one quarter of Africa’s population—is a fragile transition zone in environmental and human terms. From south-to-north, rainfall decreases from around 30 inches per year on average to essentially nothing. Back-to-back contrasting rain years (deficits in 2011, floods in 2012) left over 18 million people in the West African Sahel threatened by food shortages between 2012 and 2013, highlighting yet again the strong the dependence between livelihoods on rainfall in the region. Ironically (tragically, even), the stakeholders within the Sahel have less access to, and therefore use less, instrumental rainfall information for planning and management than almost anywhere else in the world. Furthermore, short-term weather and seasonal climate forecasting have limited skill for West Africa. Whilst many of the National Hydrological and Meteorological agencies are making impressive efforts to produce tailored climate forecasts for their stakeholders, most appear to be country specific. Recognizing these constraints, this Special Issue presents the latest understanding of African rainfall variability and on-going efforts to translate this into useable information through knowledge co-production and dissemination, to assure content relevance and accuracy for intended purposes. Stakeholders must establish practical innovations to anticipate impending crises and work collaboratively across the region to share information and strengthen supporting infrastructure. Within this framework, timely access to user-relevant climate information, access to relevant and reliable forecasts, and the ability of stakeholders to act on that information through effective strategic partnerships will prove the difference between coping proactively with emerging climate challenges and perpetuating the cycle of climate triggers and crisis.
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".