Evaluation of Conservation Efficiency: Metrics for the Management of Permanent Preservation Areas and Legal Reserves in Brazil
Bibliographic record
Abstract
The Brazilian Forest Code regulates Permanent Preservation Areas (PPA) and Legal Reserves (LR) across all federative states. These areas support the maintenance of ecological functions and are essential for biodiversity conservation and environmental balance. However, implementing these initiatives faces significant challenges, particularly in supporting the expansion of agribusiness. Effective management is essential for economic development while also preserving natural habitats. Our study relies on data from the Rural Environmental Registry (RER), managed by the Brazilian Federal Government, to assess PPA and LR in São Paulo. We apply the geometric metrics of the Circularity Index, Edge Factor, Fractal Dimension, and Compactness Index to evaluate these protected areas’ shape and physical characteristics, individually and as groups. The results underscore the relationship between the morphology of these areas and their ecological functions, including their susceptibility to edge effects and habitat degradation. Moreover, the large-scale analysis correlating several areas revealed the complexity of these landscapes, characterized by differing degrees of connectivity, vulnerability, and ecological efficiency, while assessing 645 districts. In conclusion, the results provide a framework for implementing protected areas that support ecosystem management and biodiversity conservation, particularly for enhancing agricultural productivity.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".