Land use change and forest patch analysis as a decision‐making tool to sustainably develop plantation agriculture and optimize biodiversity conservation
Bibliographic record
Abstract
Abstract Established for biodiversity conservation, protected areas (PAs) have been downsized, downgraded, and/or degazetted for socioeconomic development including plantation agriculture. Although studies have highlighted causes, implications for biodiversity conservation, and the need for policies governing Protected Area Downsizing, Downgrading, and Degazettement (PADDD), no study has proposed a methodology to inform PADDD events to help decision making that balance economic growth pursuit with ecological and environmental commitments. A methodology based on land use change and forest patch analysis has been applied to Buvuma Island to guide the choice of PAs that can undergo PADDD as well as identification of new areas that can be declared as PAs. Our results revealed that, over the last decade, natural vegetation of Buvuma Island has been highly degraded with forest depletion from 45.0% in 2007 to 15.8% in 2016. About 65% of the initial forest cover were lost. The average yearly forest loss rate was 3.2% or 712.8 ha. A total number of 19 PAs covering 3103 ha including 2816 ha of existing PAs and 287 ha of identified forest patches were selected for biodiversity conservation. This flexible methodology can be applied at various spatial and temporal scale to ensure sustainable plantation agriculture development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".