Trends in Woody and Herbaceous Vegetation in the Savannas of West Africa
Bibliographic record
Abstract
We assess 32 years of vegetation change in the West African Sudano-Sahelian region following the drought events of the 1970s and 1980s. Change in decadal mean rain use efficiency is used to diagnose trends in woody vegetation that is expected to respond more slowly to post-drought rainfall gains, while change in the slope of the productivity–rainfall relationship is used to infer changing herbaceous conditions between early and late periods of the time series. The linearity/non-linearity of the productivity–rainfall relationship and its impact on the interpretation of overall greening trends, and specific woody and herbaceous vegetation trends, is also examined. Our results show a mostly positive association between productivity and rainfall (69% of pixels), which can be best described as linear (32%) or saturating (37%). Choosing the ‘best’ model at a specific location using Akaike Information Criterion has no discernible effect on the interpretation of overall greening or herbaceous trends, but does influence the detection of trends in woody vegetation. We conclude that widespread recovery in woody vegetation is responsible for the post-drought greening phenomenon reported elsewhere for the Sahel and Sudanian sub-regions. Meanwhile, trends in herbaceous vegetation are less pronounced, with no consistent indication towards either herbaceous degradation or recovery.
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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".