A long-distance trail as a strategy to maintain habitat connectivity in the central Brazilian savanna
Bibliographic record
Abstract
The Brazilian Cerrado is being rapidly converted, threatening its biodiversity and essential ecosystem services. As a conservation strategy, the Brazilian Government established the ‘National Network of Long-Distance Trails and Connectivity’, aiming to integrate nature-based tourism with landscape connectivity and species conservation. This work analyzes the potential role of the ‘Caminho dos Veadeiros’ trail on landscape connectivity and biodiversity conservation in the Brazilian Cerrado, focusing on medium to large-sized mammals. Landscape structure and legal protection of the territory and along the trail corridor were analyzed. Species occurrence data were compiled, and wildlife corridors were modeled and compared with the trail route. At least 35 species of medium to large mammals were identified within the trail region, where a significant amount of native vegetation is maintained with remnants relatively undisturbed, showing low levels of fragmentation. Protected Areas cover 19% of this landscape, with most protected areas located in the northern and southern sections, resulting in a ‘conservation gap’ in central areas. It is suggested that the ‘Caminho dos Veadeiros’ trail could support landscape connectivity between key conservation areas, such as Chapada dos Veadeiros National Park and Formosa Military Training Area, through strategic route adjustments and conservation efforts along the trail corridor. The methodological approach applied – incorporating spatial analysis, participatory planning, and species monitoring – offers a replicable, science-based framework to guide conservation-oriented long-distance trails in biodiversity-rich regions. The need for corridor width considerations and targeted habitat protection is highlighted to maximize conservation impacts while supporting sustainable tourism and community involvement.
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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 | 0.002 |
| 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 | 0.001 |
| 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".