The Impact of Agricultural Expansion on Forest Cover in Ratanakiri Province, Cambodia
Bibliographic record
Abstract
In the northeastern Cambodian province of Ratanakiri, agricultural expansion has been a significant factor in the decline of forest coverage. As forests are essential for rural populations’ livelihoods and a healthy environment, this study presents the dynamics of this transformed forest landscape resulting from changes in farming, land accessibility and policy changes. A multitemporal dataset consisting of two ALOS/AVNIR-2 images in 2007 and 2011 were used to compare changes in land cover, and the panchromatic image of 2012 Worldview-1 acquired at 100 km2 was used to access specific land-use patterns. Qualitative research methods ranging from an ethnographic method to qualitative data analysis were performed for gathering in-situ information to understand human-induced changes in land use. The results illustrate three triggers found at the local level, actively changing the forest landscape: (1) indigenous people transforming the swidden farming system to the mono-cropping system without external support and agricultural market information, (2) chaotic property market resulting from migrants purchasing existing farms or forest lands from indigenous people via land brokers, and (3) the introduction of land concessions by government via the 2001 Land Law, which allows agricultural cooperation to develop plantations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".