Graph-Based Approaches Modeling to Assess Ecological Connectivity, Create Natural Habitats and Strengthen Ecological Networks in Ouagadougou, Burkina Faso
Bibliographic record
Abstract
Cities in developing countries are particularly vulnerable to natural resource degradation, as urban development is often unplanned, and even when it is, it does not take biodiversity into account.Yet biodiversity is crucial to the construction and transformation of sustainable cities in Africa.Integrating ecological connectivity into urban planning is an essential tool for conserving and enhancing biodiversity and improving the quality of ecosystem services for city dwellers.This study aimed to assess ecological connectivity using the landscape graph modeling approach to identify priority fragments and dispersal corridors for conservation in the city of Ouagadougou, based on their overall contribution to forest habitat quality and connectivity.The ecological network modeling methodology used was based on the landscape graph approach, using Graphab 3 and QGIS 3.38.2software.Urban terrestrial mammals, a group of species sensitive to deforestation, were used as data for modelling.The main results reveal significant fragmentation of forest habitats, characterized by isolated habitat patches and a limited number of functional corridors.Local connectivity metrics calculations identified strategic habitats and essential corridors to be preserved.This spatial modeling research highlights the need to integrate conservation strategies into urban planning, particularly through the creation of corridor networks, with an emphasis on nature-based solutions for urban biodiversity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".