Relationships between Sacred Natural Sites, Ecological Corridors, andPriority Resilience Climate Change Areas in the Upper Paraguay RiverBasin
Bibliographic record
Abstract
This dissertation highlights the critical role of Sacred Natural Sites (SNS), defined by Wild & McLeod (2008) as "areas of land or water having special spiritual significance for people and communities" as complementary tools for biodiversity conservation. With the global goal of conserving 30% of terrestrial and aquatic areas by 2030, established by the Kunming-Montreal Global Biodiversity Framework, SNS are proposed as effective measures to be included in Target 3 as Other Effective Area-Based Conservation Measures (OECMs). However, achieving this requires a reassessment, at the national level, of the concept of biodiversity,9’0’ used in decision-making processes for the creation of protected areas. This reassessment must incorporate cultural and spiritual perspectives, as well as the traditional knowledge of Indigenous peoples and local communities, whose practices are deeply intertwined with the conservation of these sacred sites. We further investigate the spatial relationships of SNS in the Upper Paraguay River Basin, Pantanal - lowland and Cerrado - plateau, focusing on key parameters for biodiversity conservation, such as Ecological Corridors, which is the result of the research carried out for Da Rosa Oliveira (2024) and Priority Resilience Climate Change areas, that were delimited by the research of The Nature Conservancy (2024). The results reveal a significant overlap between Sacred Natural Sites (SNS) and Ecological Corridors. This highlights their importance in maintaining ecological connectivity and supporting species movement, which directly contributes to increased biodiversity. It also demonstrates the effectiveness of traditional communities in safeguarding these vital areas, which are also crucial for their well-being, there so, provision of cultural ecosystem services. However, the overlap between SNS and Priority Resilience Climate Change areas was not statistically significant, likely due to differing selection criteria and the unique characteristics of wetland ecosystems. Despite this, our findings emphasize the essential role of SNS in preserving biodiversity and cultural heritage, particularly in regions of high biological and cultural diversity like the Pantanal. By integrating SNS into conservation strategies, this study reinforces their value as a complementary approach to achieving global biodiversity targets and ensuring the resilience of both ecosystems and traditional communities.
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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".