Achieving Quality Forest and Landscape Restoration in the Tropics
Bibliographic record
Abstract
Forest and landscape restoration (FLR) is being carried out across the world to meet ambitious global goals. However, the scale of these efforts combined with the timeframe in which they are supposed to take place may compromise the quality of restoration, and thus limit the persistence of restoration on the landscape. This paper presents a synthesis of ten case studies identified as FLR to critically analyse implemented initiatives, their outcomes, and main challenges, with an eye to improving future efforts. The identified FLR projects are diverse in terms of their spatial coverage, objectives; types of interventions; and initial socioeconomic, institutional, and environmental conditions. The six principles of FLR—which have been widely adopted in theory by large global organisations—are inadequately addressed across the initiatives presented here. The identified FLR project or interventions, although expected to offer diverse benefits, face many challenges including the lack of long-term sustainability of project interventions, limited uptake by regional and national agencies, limited monitoring, reporting and learning, poor governance structures, and technical barriers, which are mainly owing to institutional weaknesses. On the basis of these cases, we propose that the best pathway to achieving FLR is via an incremental process in which a smaller number of more achievable objectives are set and implemented over time, rather than setting highly ambitious targets that implementers struggle to achieve.
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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".