Three decades of woodland cover change in Hwedza, Zimbabwe reveals similar trajectories of woodland loss in communal and resettlement areas
Bibliographic record
Abstract
Zimbabwe has pledged to halt and reverse forest loss by 2030, which if accomplished may enhance the delivery of ecosystem services. Uncertainty over the extent of woodland cover change and the impact of land redistribution could impede progress. Through comparative analysis of communal and resettlement areas we investigated the patterns, causes and implications of land-cover change in Hwedza, Zimbabwe between 1990 and 2020. Land-cover classification of remotely sensed data reveals that Hwedza has transitioned from a trajectory of net woodland loss to net woodland gain. There is no evidence that resettlement increased deforestation compared to communal areas. Changes in off-farm income, smallholder tobacco farming, and reduced profitability of staple crops were perceived by interviewees to be important factors affecting woodland change. Due to the importance of woodland services such as fuelwood, our findings highlight the need to address the societal implications of policies aiming to reduce deforestation .
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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 | 0.001 | 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 | 0.001 |
| 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".