Integrating Landscapes that have Experienced Rural Depopulation and Ecological Homogenization into Tropical Conservation Planning
Bibliographic record
Abstract
If current trends of declining fertility rates and increasing abandonment of rural land as a result of urbanization continue, this will signal a globally significant transformation with important consequences for policy makers interested in conservation planning. This transformation is presently evident in a number of countries and projections suggest it may occur in the future in many developing countries. We use rates of population growth and urbanization to project population trends in rural areas for 25 example countries. Our projections indicate a general decline in population density that has either occurred already (e.g., Mexico) or may occur in the future if current trends continue (e.g., Uganda). Using both temperate and tropical examples we present evidence that this process will lead to ecological homogenization as a dominant habitat (e.g., forest replaces a mosaic of human-maintained landscapes), resulting in declines in biodiversity at the local scale. Building on this information, we consider research programs that need to be conducted so that policy makers are prepared to effectively manage depopulated rural areas.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".