Using machine learning to uncover synergies between forest restoration and livelihood support in the Himalayas
Bibliographic record
Abstract
In recent years, governments and international organizations have initiated numerous large-scale tree planting projects with the dual goals of restoring landscapes and supporting rural livelihoods. However, there remains a need for greater knowledge of drivers and conditions that enable positive social and environmental outcomes over the long term. In this study, we used interpretable machine learning (IML) to explore win–win and win–lose outcomes between livelihood benefits and forest cover using four decades of tree plantation data from northern India. Our results indicated that, in areas with a larger population of socioeconomically marginalized groups, moderate levels of education, and existing histories of community collective action, there is a higher probability of achieving joint positive outcomes. We also found that joint positive outcomes are more common within a consolidated local institutional space, suggesting that decentralized governance structures with cross-sectoral duties and functions may be better equipped to mediate conflicts between intersecting forest and land use challenges. Finally, our findings showed that non-forestry and anti-poverty interventions such as universal labor generation programs and universal education are associated with improved forest cover alongside livelihood benefits from plantations. Whereas contemporary policy discussions have given substantial attention to tree plantation schemes, our work suggests that effective restoration requires much more than planting alone. A broad mixture of socioeconomic, institutional, and policy interventions is needed to create favorable conditions for long-term success. In particular, anti-poverty programs may serve as important indirect policy pathways for ensuring restoration gains.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".