Priority areas identification and management strategies for landscape forest restoration in Mozambique
Bibliographic record
Abstract
In the last decades, Forest Landscape Restoration (FLR) emerged as a solution to restore ecological integrity while enhancing human well-being in deforested or degraded forest landscape. One key challenge in implementing FLR includes the identification of suitable intervention areas according to the restoration strategy (active or passive restoration) and the local socio-bioophysical constraints. The aim of this study was to develop a new approach to locate where forest landscape restoration would enhance multiple ecosystem functions and identified management strategies (passive or active restoration) in two districts in central Mozambique. The methodology involved (i) the ecosystem functions mapping to identify multifunctional hotspot and (ii) the assessment of the land-use history to differentiate areas with low or high regeneration potential. We derived three spatially-explicit ecosystem functions (biomass, soil carbon sequestration potential and forest connectivity) and one characteristic (woody species diversity potential) based on field inventory. We mapped and analyzed land-use history, defined by the current fallow age, the time since the first forest clearcutting and the number of crop-fallow cycles. The results showed that 118,629 ha were identified as priority areas (10.9% of the study area) for forest landscape restoration, with 42,255 ha (36%) with natural regeneration potential and 76,373 ha (64%) with low regeneration potential and would require human activities to recover ecosystem functionality and ability to provide ecosystem services. This study provides new insights for integrating ecosystem functions at landscape scale to support decision making for forest restoration and support the Mozambican government commitments to restore degraded landscapes at national scale.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".