Current and projected changes in climate extremes and agro-climatic zones over East Africa
Bibliographic record
Abstract
Given the sensitivity of the agricultural sector to climate variability and change, a comprehensive understanding of environmental factors to this sector and the extent to which climate change may alter such factors is critical for planning and adaptation strategies. This study aims to assess the extent of extreme climate conditions and livestock-relevant maps of agro-climatic zones across East Africa in current and future climates. Ensembles of seven global climate models selected from the sixth Coupled Model Intercomparison Project (CMIP6) are considered under SSP245 and SSP585 socio-economic pathways. The water extreme/stress indicators considered include indices to indicate drought and flood situations. The heat stress indicators are composed of the frequency of hot spells and the duration and intensity of heat waves. As expected, all heat stress indicators are projected to increase in future climates. The frequency and intensity of heavy precipitation events are also projected to increase with increased GHG emissions. The drought stress indicators follow the precipitation pattern and are projected to decline over most of the domain. A heterogeneous response of the agro-climate regime to climate changes is projected for eastern Africa, with some areas (e.g., southern Tanzania) experiencing alterations towards drier zones while others (e.g., northern Somalia, South Sudan) are experiencing a shift towards wetter zones. The increase in short-duration heavy precipitation events together with the enhanced pace of heat stress over the region, will have critical implications for agriculture in general and local livestock production in particular.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".