Identifying Forest Degradation and Restoration Opportunities in the Lancang-Mekong Region: A Tool to Determine Criteria and Indicators
Bibliographic record
Abstract
Forest restoration is increasingly becoming a priority at international and national levels. Identifying forest degradation, however, is challenging because its drivers are underlying and site-specific. Existing frameworks and principles for identifying forest degradation are useful at larger scales, however, a framework that includes iterative input from local knowledge-holders would be useful at smaller scales. Here, we present a new mechanism; a framework for developing criteria and indicators that enables an approach for the identification of forest degradation and opportunities for restoration in landscapes that is free from failures that are often inherent to project cycles. The Degradation and Restoration Assessment Mechanism (DReAM) uses an iterative process that is based on local expertise and established regional knowledge to inform what is forest degradation and how to monitor restoration. We tested the mechanism’s utility at several sites in the Lancang-Mekong Region (Cambodia, Laos, Myanmar, Thailand, and Vietnam). The application of this mechanism rendered a suite of appropriate criteria and indicators for use in identifying degraded forests which can help inform detailed guidelines to develop rehabilitation approaches. The mechanism is designed to be utilized by any individual or group that is interested in degradation identification and/or rehabilitation 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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".