Dynamics of woody plant cover in the Sahelian agroecosystems of the northern region of Burkina Faso since the 1970s–1980s droughts
Bibliographic record
Abstract
This study was carried out in the northern region of Burkina Faso under Sahelian climatic conditions. The area was particularly affected by the 1970s–1980s droughts that led to the degradation of land and vegetation. Since the early 1990s, a gradual return of rainfall has been observed throughout the Sahel region. In this new environmental context, understanding the development of woody plants is important for effective conservation and management. We analyzed the dynamics of woody plant cover over the 30 years following the end of the 1970s–1980s droughts by using Landsat images from 1986, 1999, and 2015 with 30 m spatial resolution and taking into account changes in rainfall and land use. The change in the enhanced vegetation index 1 (EVI1) at the beginning of the dry season was used as a proxy for the change in photosynthetic activity of woody plants. Results showed an improvement in EVI1 on 98% of the study area, with a mean increase of 0.20 from 1986 to 2015. This improvement was accompanied by an increase in agroforestry and was weakly correlated with rainfall. The improvement in EVI1 was unstable, however, with a decline from 1999 to 2015 in the areas undergoing regreening.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".