Agroforestry Species Selection for Forest Rehabilitation in the Asia-Pacific Region: A Meta-Analysis on High-Level Taxonomy
Bibliographic record
Abstract
Agroforestry is important for forest management and rehabilitation in the southeast Asia-Pacific Region (APR), where economic issues, intensive land use, deforestation, and forest degradation are common. Species selection is a key process in establishing agroforestry systems. In this study, we reviewed the agroforestry literature across eight economies within the southeast APR, documented the species used, and compared the existing systems to better understand the challenges and opportunities for the region’s agroforestry expansion. We conducted rule and Maptree analyses using 108 species, belonging to 95 genera and 49 families of plants, to unravel the various agroforestry practices in this region. We identified the most common plant families used in agroforestry combinations within each economy. We then divided the economies into three groups based on the most commonly used genera: (1) Thailand, Vietnam, Papua New Guinea and Fiji (Hevea, Oryza, Eucalyptus, Acacia, and Zea); (2) Nepal and Yunnan China (Zea, Leucaena, Morus, and Hevea); and (3) Indonesia and the Philippines (Oryza, Hevea, Zea, and Brassica). Although this study focused on high-level taxonomic classification (family and genus), we believe that this work will fill the current knowledge gaps, offering guidance to economies in the southeast APR regarding species selection and the adoption of sustainable agroforestry practices.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.015 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".