Bibliographic record
Abstract
Abstract The Intergovernmental Panel on Climate Change in its Sixth Assessment Report posits that temperatures will continue to rise across Africa while precipitation will decline in northern and southern Africa; west, central, and east Africa will witness increased, high-intensity, short-duration, and poorly distributed precipitation that is mostly unavailable for crop growth. Despite its limited contribution to the global and regional greenhouse gas budget, Africa as a continent and most of its regions continue to be more vulnerable to the impacts of climate change. There is evidence across Africa that its relatively higher vulnerability to climate change stressors is anchored in its extremely relatively low adaptive capacities evidenced in high poverty and low literacy rates. The impacts of climate change are seen mostly in the context of recurrent, low resilience and high vulnerability to stressors or climate shocks, such as droughts, floods, and sandstorms. Because African economies are wired on agriculture, these stressors or shocks are ravaging agricultural systems, thus creating daunting food security and economic challenges. This chapter reviews some of the climate change adaptation actions carried out by governments, nongovernmental organizations, and communities to adapt to climate change across the Sahel in the agricultural sector, as this region of Africa is particularly vulnerable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".