Recent Patterns of Exposure, Sensitivity, and Adaptive Capacity of Selected Crops in Cameroon
Bibliographic record
Abstract
In most parts of sub-Saharan Africa, precipitation is impacted by climate change. In some countries like Cameroon, it is still not clear how maize, millet and rice will respond to changes in growing season precipitation. This work examines the exposure, sensitivity, and adaptive capacity of the above crops to droughts at both the national and sub-national scale. Crop yield data were culled from FAOSTAT while growing season precipitation data were culled from the database of UNDP/Oxford University and the climate portal of the World Bank. Adaptive capacity proxies (literacy, and poverty rate) were collected from KNOEMA and the African Development Bank. The analysis was performed using the vulnerability index equation. Nationally, millet has the lowest vulnerability and rice has the highest. At the sub-national scale, northern maize has the highest vulnerability followed by western highland rice. It is observed that when scales change, the crops that are vulnerable also change. However, at both levels vulnerability has an inverse relationship with adaptive capacity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".