Forest and landscape restoration monitoring frameworks: how principled are they?
Bibliographic record
Abstract
Forest and landscape restoration (FLR) aims to simultaneously restore ecological functionality to deforested or degraded landscapes and ensure the provision of ecosystem services essential for human well‐being. Interest in FLR has followed the ambitious commitments made to restore degraded forest by 2030 under the Bonn Challenge and the New York Declaration on Forests. To clarify and define FLR, the Global Partnership on Forest and Landscape Restoration articulated six principles that underlie this approach, but other sets of principles have also been developed. Our paper examines if and to what extent these principles and their interdependencies are captured in frameworks currently used to monitor FLR. We conducted a literature review to identify FLR monitoring frameworks that linked criteria to principles, but found only five appropriate publications. These frameworks were strictly hierarchical and thus unlikely to capture the interactions and interdependencies among different elements of FLR. Two of the five addressed all six principles. Second, we conducted a series of group exercises with experts to characterize the topology of FLR monitoring frameworks by linking criteria to principles and examining interconnections. We cataloged 18 criteria and 76 indicators, in a non‐exhaustive exercise. Cognitive mapping of the interconnections between FLR principles and criteria showed that criteria are typically linked to more than one principle indicating the need to consider networked frameworks. However, no FLR monitoring frameworks currently exist for understanding and operationalizing all six principles, and integrating the interconnected processes underpinning FLR planning, monitoring, and assessment.
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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 | 0.118 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.025 | 0.047 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".